Dr Christian Fischer
email: christian.fischer@ilr.uni-bonn.de

Luis A Gil-Alana

-Time-Series Models < C2 -Econometric Methods: Single Equation Models < C -Mathematical and Quantitative Methods, Q13 -Agricultural Markets and Marketing|Cooperatives|Agribusiness <

Keywords: international trade, tourism, long memory, Spanish wine international trade, tourism, long memory, Spanish wine JEL classification: F14, C22, Q13, L83

This article deals with the relationship between international trade and tourism. We focus on the effect that German tourism to Spain has on German imports of Spanish wine. Due to the different stochastic properties of the series under analysis, which display different orders of integration, a methodology is used based on long memory regression models, where tourism is supposed to be exogenous. The period covered is January 1998 to November 2004. The results show that tourism has an effect on wine imports that lasts between two and nine months. Disaggregating the imports across the different types of wine it is observed that only for red wines from Navarra, Penedús and Valdepeñas, and to a certain extent for sparkling wine, tourism produces an effect on future import demand. From a policy-making perspective our results imply that the impact of tourism on the host economy is not only direct and short-term but also oblique and delayed, thus reinforcing the case for tourism as a means for economic development.

Introduction

International tourism has grown strongly during the last decades. Worldwide tourist trips reached almost 700 million in 2000, as compared to about 25 million in 1950.

Measured in relative terms, at 120 trips per thousand of world population in 2000, tourism activity has increased more than tenfold during this period (World Tourism Organization, WTO, various issues). At the same time, international merchandise trade has also grown significantly. According to figures of the WTO, world exports per head of world population have increased from 44 current USD in 1961 to almost 1,200 in 2003. Thus, because there seems to be correlation between the international flows of people and goods a growing literature has emerged, which, using different methodologies, have investigated the possibility of a causal relationship between these two phenomena.

From a policy perspective, the topic of possible significant and positive interdependencies between tourism inflows and exports of manufactures is important in at least two ways. First, industrial development officers and trade association officials may find it useful to better understand the dynamics and determinants of industrial export success. While, in practice, it may be difficult to actively influence tourism arrivals, the knowledge about confirmed tourism-trade interdependencies may enhance the ability to predict exports by taking into consideration tourism data.

Second, tourism development agencies could demonstrate that the positive impacts of international travel on a national economy may be multiple and lasting. If tourists can be shown of not only generating income and jobs while they are in the country, but also of creating significant economic impulses by means of resulting exports to their source countries, the attention given to tourism development may perhaps be raised. theoretically the link between trade and tourism, there is a host of empirical work which shows the connection between the two. [START_REF] Easton | Is Tourism Just Another Commodity? Links between Commodity Trade and Tourism[END_REF] analyzed whether Canadian total exports are complementary or substitutive to tourist arrivals, using pooled data regressions. The study finds "some evidence of substitution of Canadian exports for tourist excursions to Canada" (p. 542) by showing that when the relative price of exports goes up, the number of tourists visiting Canada increases. [START_REF] Kulendran | Is there a relationship between international trade and international travel?[END_REF] analyzed the direction of causality between different travel and (aggregate) trade categories for Australia and its four main trading partners. Their results show that travel Granger causes international trade in some cases and vice versa in others. [START_REF] Shan | Causality Between Trade and Tourism: Empirical Evidence from China[END_REF] replicate this latter approach and also find two-way Granger causality using aggregate data for China. [START_REF] Aradhyula | Does Tourism Promote Cross-Border Trade?[END_REF] used a simultaneous bivariate qualitative choice model to show that crossborder business trips have a significant and positive effect on US agribusinesses' propensity to trade. [START_REF] Fischer | The influence of immigration and international tourism on the demand for imported food products[END_REF] explored the connection between aggregate imports and imports of individual products and bilateral tourist flows, using an errorcorrection model. His results show that trade-tourism elasticities are consistently higher for individual products.

One of the aspects which has not yet been explored according to our knowledge is the temporal nature of the relationship between tourism and trade. As described by [START_REF] Kulendran | Is there a relationship between international trade and international travel?[END_REF] when recommending further research directions: "… the lag structures between the travel and trade flows … may require further attention" (p. 1007). Spain and the number of German travellers to that country. It aims to empirically analyze whether tourism has an effect on future imports and, if yes, determine the length of this effect. Thus we expand current scientific knowledge about the temporal structure of the tourism-trade link. For several reasons we concentrate on the case of wine. First, wine has become a truly globalised industry with about 40% of production (in value terms) being exported worldwide in 2001 [START_REF] Anderson | Introduction[END_REF]. Second, in industrialized nations, wine is a commonly available commodity offered in a large variety mostly differentiated by production origin. Given that objective wine quality is hard to assess for non-expert consumers, the origin of a wine is often used as a shortcut quality indicator in cases where the country of origin is associated with a preferred holiday destination [START_REF] Felzenstein | Is the Country of Origin the Fifth Element in the Marketing Mix of Wine? A Critical Review of the Literature[END_REF]. Last, wine imports have been shown to display a significant connection with tourism activities among a range of previously investigated products [START_REF] Fischer | The influence of immigration and international tourism on the demand for imported food products[END_REF].

Investigating Spain and Germany seems particularly interesting given that

Spain is both a significant exporter of wine and an important tourist destination, while Germany is an important importer of wine and a main tourism source country. In 2001, Spain was the world's third biggest wine exporter (in value terms) while Germany was its third most important wine import market, behind the UK and the US [START_REF] Anderson | Introduction[END_REF]. In 2000, Spanish wine accounted for 14.8% of total German import value and imports were up 12% on the year earlier [START_REF] Storchmann | Germany[END_REF]. Between 1997 and2004, this The organization of the article is as follows: in section 2, we describe the methodology employed in the paper and present the model used in the empirical work. In section 3 we describe the series used in the analysis and examine their statistical properties. Section 4 contains the empirical work relating German wine imports from Spain with the number of German travellers to Spain. In section 5 we disaggregate the wine series according to the different products. Section 6 concludes.

The econometric model

Most of the time series work examining the relationship between international trade and tourism is based on cointegration. However, that methodology imposes a priori the assumption that the individual series must share the same degree of integration, usually 1. In other words, each series must be I(1), and they will be cointegrated if there exists a linear combination of them that is I(0) stationary. 1

In the context of the series analyzed in the present article (which are aggregate wine imports and total tourism), we face various problems. First the two series do not posses the same order of integration. In fact, as explained later in section 3, the wine imports data is I(0), while tourism is clearly nonstationary I(1). Moreover, the later series presents a clear seasonal pattern, while the former does not. If the two series 1 For the purpose of the present article we define an I(0) process {u t , t = 0, ±1, …} as a covariance stationary process with a spectral density function that is positive and finite at the zero frequency. An I(1) process is then defined as a process that requires first differences to get I(0) stationarity. (HEGY, 1990). (See, e.g., [START_REF] Kulendran | Is there a relationship between international trade and international travel?[END_REF] However, a simple inspection of figure 1 shows that the aggregate wine import series is not seasonally integrated. For many years, seasonality was considered as a component that obscured the time series properties of the data, and seasonal adjustment procedures were implemented to sort this problem out. However, these methods have been strongly criticized in recent years on the basis that seasonal data contains some statistical relevant information by themselves. In this article we deal with the seasonal problem in tourism by using two approaches. First, we deseasonalise the series by using seasonal dummy variables. As a second approach, we take first seasonal differences (on the logged series), such that the series then represents monthly growth rates.

Looking at the orders of integration of the two deseasonalised series, we still face the problem that both series are now I(1), while wine import is I(0), invalidating an analysis based on cointegration. 2

What we propose in this article is to look at the relationship between the two variables (aggregate wine imports and tourism) by using a methodology based on fractional integration.

2 As an alternative approach to model the seasonal structure, and in line with the methodology used in the paper, we could have removed the seasonal component using (seasonal) fractional integration techniques (Gil-Alana and [START_REF] Gil-Alana | Seasonal fractional integration in the UK and Japanese consumption and income[END_REF][START_REF] Gil-Alana | Seasonal long memory in the aggregate output[END_REF]. However, since the wine import series does not present evidence of seasonality, that approach would be invalid in a regression model relating the two variables. (See Section 4). , where u t is I(0) and L is the lag operator (Lx t = x t-1 ). The polynomial above can be expressed in terms of its binomial expansion, such that for all real d,

F

= d ) L 1 ( =0 j j j L ) 1 ( j d = ... L 2 ) 1 d ( d L d 1 2 +
. The macroeconomic literature has usually stressed the cases of d = 0 and 1. However, d can be any real number. Clearly, if d = 0 in (1), x t = u t , and a "weakly autocorrelated" (e.g., ARMA)

x t is allowed for. However, if d > 0, x t is said to be a long memory process, also called "strongly autocorrelated" because of the strong association between observations that are widely separated in time. As d increases beyond 0.5, x t can be viewed as becoming "more nonstationary", for example, in the sense that the variance of partial sums increases in magnitude. 3 The fractional differencing parameter d plays a crucial role in describing the persistence in the series: the higher the d, the higher the level of association between the observations. 4

We now consider the following model,

(2) ... , 2 , 1 , ' = + = t x z y t t t
, where y t is a raw time series; is a (kx1) vector of unknown parameters; z t is a (kx1) vector of deterministic (or weakly exogenous) variables, and x t is given by (1). [START_REF] Robinson | Efficient Tests of Nonstationary Hypotheses[END_REF] proposed a Lagrange Multiplier test of the null hypothesis:

3 See [START_REF] Baillie | Long memory processes and fractional integration in econometrics[END_REF] for an interesting review of I(d) models. 4 At the other end, if d < 0, x t is said to be "anti-persistent", because the spectral density function is dominated by high frequency components. See [START_REF] Mandelbrot | Fractals. Form, chance and dimension[END_REF]. where the probability that a standard normal variate exceeds z is .

F

The time series data

The trade series (German imports of Spanish wine in current euro) were obtained from two different Eurostat databases. First, aggregate imports were taken from "DS- from this data (referred to as products A to H in our analysis) were chosen (see table 1). Most of these wines are quality wines produced in specific areas because it could be expected that returned tourists look exactly for the products of the region they were holidaying in. These eight products taken together represent on average about 62% of total German wine imports over the period of investigation. The period from 1998m1 to 2004m11 was selected due to data availability. Except for the sparkling wine (A)

and Sherry category (H), all products are quality wines produced in distinct Spanish areas sold under a certified geographical label.

(Insert table 1 about here) Since we are mainly interested in the relationship between the two variables, the first thing we do is to analyze the statistical properties of each of the variables individually. For this purpose, we first implemented some classic methods to investigate if the series are stationary I(0) or nonstationary I(1). In particular, we use the tests proposed by Dickey and Fuller (ADF, 1979), Phillips and Perron (PP, 1988) and Kwiatkowski, Phillips, Schmidt and Shin (KPSS, 1992).8

The results of the above procedures for the Aggregate Wine Imports are displayed in table 2(i). We observe that using no regressors, the tests cannot reject the hypothesis of a unit root. However, including an intercept and/or a linear trend, this hypothesis is rejected in all cases in favour of stationarity.

Anyway, the use of these procedures for testing the order of integration of the series is too restrictive in the sense that they only consider integer values for the degree of differentiation. Moreover, it is a well-known stylised fact that the above 7 As an alternative definition of German tourism in Spain we also employed the number of nights spent in Spanish hotels by German travellers. However, the series presents a similar pattern as the one used in the present article. methods have very low power if the alternatives are of a fractional form [START_REF] Diebold | On the power of Dickey-Fuller tests against fractional alternatives[END_REF]Hassler and Wolters, 1994, etc.). Across table 2(ii) and (iii) we display the results for the AWI series based on two approaches for estimating and testing the order of integration of the series from a fractional point of view.

The results in table 2(ii) refers to the parametric approach of [START_REF] Robinson | Efficient Tests of Nonstationary Hypotheses[END_REF] described in section 2, assuming that z t in ( 2) is a deterministic component that might include a constant (i.e., z t = 1) or a linear time trend (i.e., z t = (1, t)'). In other words, we test the null hypothesis (3): d = d o , for any real value d o in the model given by: (4) ;

t t x t y + + = , ) 1 ( t t d u x L =
assuming that u t is white noise and also autocorrelated. In the latter case, we use the [START_REF] Bloomfield | An exponential model in the spectrum of a scalar time series[END_REF] exponential spectral model. 9 We display the 95% confidence intervals of the values of d o where H o (3) cannot be rejected for the three cases of no regressors, an intercept, and an intercept and a linear time trend. These confidence intervals were built up according to the following strategy. where m is a bandwidth parameter number, d o is the true value of d and with the only additional requirement that m slower than T.

Table 2(iii) displays the estimates of d for values of m from 1 to T/2. We also include in the figure the 95%-confidence interval corresponding to the I(0) case. It is observed that practically all values of d are within the I(0) interval, which is consistent with the results based on the parametric approach above.

As a conclusion, the results presented across table 2 suggest that the aggregate wine imports series is stationary I(0).

10 Note that the LM procedure of [START_REF] Robinson | Efficient Tests of Nonstationary Hypotheses[END_REF] is based on the Whittle function, which is an approximation to the likelihood function. 14 Next, we concentrate on tourism, and the first thing we do is to remove the seasonal component. Here we take two approaches. First, we assume that seasonality is deterministic and use seasonal dummies to remove that component. The plots of the deseasonalised series (DST), though not reported, showed that the series may be nonstationary. As a second approach we assume that the seasonality in tourism has a stochastic nature, and use seasonal first differences on the logged series, creating thus a new series, which is the monthly growth rate (MGRT). Similarly to the previous case, nonstationarity was found in this series.

(Insert tables 3 and 4 about here)

Across tables 3 and 4 we display the same type of analysis as the one performed before for the wine import series, referring now to the deseasonalised series (table 3) and the monthly growth rates (table 4). The results are very similar in both series: using classic methods (tables 3(i) and 4(i)) evidence of a unit root is found in all cases when using the test statistic with most realistic assumptions. Using the fractional framework, ((ii) and (iii)) the unit root is almost never rejected though fractional orders of integration, with values of d slightly below 1 are also plausible in most of the cases.

To conclude, we can summarize the results presented across this section by saying that the aggregate wine imports seem to be stationary I(0), while tourism, once the seasonal component has been removed, is nonstationary I(1). Thus, given the different nature of the processes under analysis and their possible fractional underlying structures, we consider in the following section a dynamic regression model relating two variables through a fractional integration approach. with k in (5) equal to 1, 2, …, and 12. As an alternative approach, we could have employed a dynamic lag-structure for DT in (5) in line with the literature on dynamic regressions in standard models. However, that approach would impose the same degree of integration across the lags, while here we permit different values of d for each lag. First, we employ the deseasonalised tourism series based on the seasonal dummies. Table 5a reports the results for white noise disturbances, while table 5b refers to the Bloomfield model. In both cases, we report, for each k, the estimates for 11 p refers to the number of parameters required to describe the short run dynamics. Other values of p were also employed and the results were very similar to those reported in the article with p = 1.

F

12 We conducted some tests for exogeneity of tourism in the wine imports equation. To establish evidence for non-causality, an unrestricted VAR was used. Weak exogeneity appeared to be satisfied in the dynamic equation because when entering the current value of DT in the equation it proved to be insignificantly different from zero. This finding supports the view that DT is weakly exogenous for the model. (Insert tables 5 -7 about here)

Starting with the case of white noise u t , we see that ] appears significant for k = 1, 2, 3 and 4, implying that tourism has an effect on wine imports that lasts at least the following four months. We also see that the interval of non-rejection values of d is relatively wide in all cases, ranging from -0.41 (k = 8) to 0.05 (k = 6). The case of d = 0 is included in all intervals but lowest statistics are obtained for negative d. Note that the estimates of ^and ] are based on the value of d producing the lowest statistic, which seems to be more appropriate from a statistical viewpoint. Imposing a weak dependence structure on the disturbances throughout the model of [START_REF] Bloomfield | An exponential model in the spectrum of a scalar time series[END_REF], table 5b, the intervals are now wider, the values of d with the lowest statistics being still negative, and the slope coefficient is now significant for the first seven periods, implying a longer dynamic effect of tourism than in the previous case.

Table 6 is similar to table 5 above but using the monthly growth rates as the deseasonalised series. If u t is white noise, only the first two lags appear statistically significant, however, using the model of [START_REF] Bloomfield | An exponential model in the spectrum of a scalar time series[END_REF], the significant coefficients reach the lag 9.

We can therefore conclude this section by saying that there is some kind of dynamic behaviour in the effect that German tourism has on German imports of Spanish wines. This significant effect lasts less than a year though varies substantially depending on the model considered and the type of series used for measuring tourism.

Disaggregation by products

In this section we examine separately the different wine products and perform the same type of analysis as the one employed in section 4. That is, we consider the same model as in ( 5) and ( 6), using specific types of wine rather than the aggregate flow. 13 In table 7 we use the DT t series (with seasonal dummies), for the two cases of white noise (in table 7a) and Bloomfield disturbances (7b). We observe that the results are very similar in both cases, implying that the short run dependence (i.e., the type of modelling approach) is not very important when describing the behaviour of these two series. In general, we observe that only for two wine types (reds from Navarra and those from Valdepeñas) the coefficients are significant across the whole period of analysis. For sparkling wine and reds from Penedús, the significant coefficients start five periods after, and the effect of tourism lasts three periods for the former and 8 months for the latter wine type. 14 Very similar results were obtained when using the monthly growth rate of tourism as a regressor.

13 Alternatively we could have employed a multivariate system for all wine categories. However, in the context of fractional processes, multivariate models are not yet fully theoretically justified.

14 One possible reason for the 5-months delayed reaction of imports to tourism flows in the case of sparkling wine and Penedús red could be that they may be goods in particular demand for New Year's Eve and Christmas respectively. Contrary to the other analyzed wine types, these two import series peak at around November while the tourism series' main peak is in May, thus pointing to a possible link for some wine types between the choice of festivity drinks and a recent holiday destination.

Conclusions

In this article we have examined the relationship between German imports of Spanish wines and German tourism to Spain. For this purpose, we first analyzed each of the series separately, and it was found that wine imports were I(0) stationary, while tourism (once the seasonal component was removed) was nonstationary I(1). Due to the different orders of integration observed in the two series, we examine the relationship between the two variables by means of a long memory regression model, using tourism retarded k periods (k = 1, …, 12) as a weakly exogenous variable. The results about the order of integration in the regression model indicate that this value is negative. However, the fact that the null hypothesis about the order of integration cannot be rejected when d = 0 suggests that the conclusions presented below should not substantially change if standard regression models were applied. Nevertheless, the estimated parameters in the regressions are supposed to be more precisely estimated once the appropriate order of integration is taken into account.

(Insert table 8 about here)

The obtained results are summarized in table 8. The first row lists the average duration of effects while the second row gives the total effect as the sum of all monthly effects in euros. 15 The average lengths of the import-promoting effects is 15 The numbers are the average (mean) of the estimates given in tables 5 and 7. The interpretation of the estimates for the monthly growth rates series is not directly comparable to the ones obtained from the deseasonalised traveller series, therefore they have not been included in the summary calculation of table 8. 16 Most accurately one would have used the series of German tourists travelling to specific Spanish areas rather than the overall German-tourists-travelling-to-Spain series for the estimation of the ]parameters. However, such regional-travellers series were not available and it can be assumed that about 5.5 months for total wine imports, three months for sparkling wine and 9-10 months for the just mentioned quality reds. This result clearly shows that, at least in the analyzed cases, tourism has a positive impact on the travel destination economy which lasts for many months after the tourists have already left the country. On average, the total import enhancing effect of tourism on total yearly wine imports of Spanish wine into Germany amounted to about 950,000 euros over the analyzed period. This represents about 0.4% of total imports. Our estimates also show that much (869,000 euros) of this sum can be attributed to sparkling wine, assuming that as a wine type produced in several areas and available in restaurants and retail outlets all over the country, more or less all German tourists may come in contact with it during their stay. For three quality reds (from Navarra, Penedús and Valdepeñas) we find the highest parameter estimates and longest-lasting effects but we are not able to calculate the accumulated total sum of tourism effects. The reason for this is that these wines are regional products and one would need to have the number of German tourists travelling to these specific areas and not the overall German tourists travelling to Spain for an accurate calculation of the accumulated effect. 16 The connection between tourism and trade seems only to hold for red wines and sparkling wine but not for white wine. Moreover, there seems to be a possible connection between wine quality (as expressed by price) and the magnitude and length of the tourism effect. Although the average import unit value at EUR 2.3 per litre is higher than the one for quality reds from Navarra (EUR 1.6), no significant relationship with the tourism series has been found. A possible explanation for this exception may be the fact that Rioja reds (accounting for on average 19% of imports during the period of investigation) comprise both some of the best, most expensive and internationallyappreciated Spanish quality wines and lots of lowly-priced bulk wine (mainly produced in the 'Baja' region) [START_REF] Albisu | Spain and Portugal, in The World's Wine Markets -Globalization at Work[END_REF]. Given their long tradition, Rioja wines may thus be internationally received as the 'typical' Spanish wine, similar to Bordeaux in France or Chianti reds in Italy. Hence, Rioja wine exports may reflect both demand by quality-oriented international wine collectors and price-conscious mass retailers, both types of demand probably being little affected by international tourism flows.

Overall, this analysis has shown that the export-promoting effects of international tourism, in some cases at least, are statistically significant, positive, relatively long-lasting, but not overly high in magnitude. Nevertheless, tourism promotion as a means for export development may still work and could be costeffective in some cases. But while a "watering can"-policy approach, spending money indiscriminately on each tourism group will not be useful, policy makers and industryas well as tourism-development officials are rather advised to make appropriate planning and budget allocation decisions only after important identification work has been done as to which manufactured goods and tourist groups display significant connections. Based on this information, effective foreign-marketing programs and strategies for exploiting existing tourism-trade interaction effects could be implemented. Further academic research should focus on providing overall guidance for the identification of those goods and tourism groups (i.e., source countries) which in general can be expected to display the strongest links. In addition, more knowledge is needed on the causes of and determinants for these relationships.

From a methodology point of view, the approach employed in this paper, based on fractional integration, solves the dichotomy produced by the I(0)/I(1) specifications, and, given the different stochastic nature of the two series considered here, the use of I(d) regression models where one of the variables is weakly exogenous allows us to examine the dynamic behaviour of the series in a very flexible way. The frequency domain formulation of the test statistic used here seems very unpopular with many econometricians and, though there exist time domain versions of the tests [START_REF] Robinson | Testing for serial correlation and dynamic conditional heteroskedasticity in multiple regression[END_REF][START_REF] Tanaka | The nonstationary fractional unit root test[END_REF], the preference here for the frequency domain approach is motivated by the somewhat greater elegance of formulae it affords, especially when the model of Bloomfield is used. A following-up step in this direction would be to examine the relationship between the two variables using fractional cointegration techniques [START_REF] Hualde | Cointegration in fractional systems with unknown integration orders[END_REF] and work in this direction is now under progress.

× × = = = = = 1 1 1 1 1 1 1 1 1 2 ) ( ) ( )' ( ) ( )' ( ) ( ) ( 2 ˆTj T j T j j j T j j j j j j T A ), ( min arg ; 2 ); ; ( log ) ( ; 2 sin 2 log ) ( 2 * T j j j j j T j g = = = =
where T * is a compact subset of the R q Euclidean space. I( j ) is the periodogram of u t evaluated under the null, and g above is a known function coming from the spectral density of u t , f = ( 2 /2 )g.

Appendix B

The estimate of [START_REF] Robinson | Gaussian Semiparametric Estimation of Long Range Dependence[END_REF] is implicitly defined by:

, log 1 2 ) ( log min arg ˆ1 = = m j j d m d d C d . 0 , 2 , ) ( 1 ) ( ; ) 2 / 1 , 2 / 1 ( 1 2 = = = T m T j I m d C d for j m j d j j
where m is a bandwidth parameter number. Notes: *The average sum of effects has been calculated as the average of the monthly effects in table 7a and 7b multiplied by the average amount of monthly German tourists traveling to Spain and by the average length of effects (first row in the above table ). For the AWI and sparkling wine category it seems save to assume that imports directly related to total German tourists. However, for the regional wine categories (D), (E) and (G), only the fraction of German tourists traveling to these specific areas can be used for calculating the overall effect in euros. Unfortunately, these figures have not been available to us.

2(iii) Estimates of d Based on the Gaussian Semiparametric Estimate

Unit values have been calculated from Eurostat data. The 2003 import unit values for the other analyzed products are: white wine from Penedús (B): 2.78 euros per liter; white wine from Rioja (C):

1.89; red wine from Rioja (F): 2.31; Sherry (H): 2.32.

  model has not yet been developed to establish

  with the relationship between the German wine imports from

  seasonally integrated, cointegration could still be the methodology used, along the lines of the procedure suggested by Hylleberg, Engle, Granger and Yoo

  016894 -EU trade since 1995 by HS2-HS4". The source of the disaggregated data is the "DS-016890 -EU trade since 1995 by CN8" database. The latter database dozen different wine categories. The eight most important ones

Figure 1 (

 1 Figure1displays plots of the aggregate wine imports and its first differences, along with their corresponding correlograms and periodograms. Starting with the original series, we observe some peaks but no strong seasonal component. The correlogram and the periodogram of the original data seem to indicate that the series is I(0). In fact, if we take first differences both the correlogram and the periodogram show that the series is then overdifferenced with respect to the zero frequency.6

  and their corresponding t-ratios), the value of d o producing the lowest statistic, its confidence interval (at the 95% level) and the value of the test statistic.

  However, quality reds from Rioja seem to be an exception.

Figure 1 .

 1 Abbreviation

Figure 2 .

 2 Figure 2. German Travellers to Spain, 1998m1-2004m11Note: The large sample standard error under the null hypothesis of no autocorrelation is 1/ T or roughly 0.038. The periodograms were computed based on the discrete Fourier frequencies a j = 2bj/T, j = 1, 2, …, T/2.

  i): In parenthesis the critical values at the 5% level. 2(ii): The values in parenthesis within the brackets refer to the value of d producing the lowest statistic. 2(iii): The horizontal axis refers to the bandwidth parameter number, while the vertical one corresponds to the estimated values of d. The dotted line refers to the 95% confidence interval for the I(0) hypothesis.

  i): In parenthesis the critical values at the 5% level. 3(ii): The values in parenthesis within the brackets refer to the value of d producing the lowest statistic. 3(iii): The horizontal axis refers to the bandwidth parameter number, while the vertical one corresponds to the estimated values of d. The dotted line refers to the 95% confidence interval for the I(0) hypothesis.

  i): In parenthesis the critical values at the 5% level. 4(ii): The values in parenthesis within the brackets refer to the value of d producing the lowest statistic. 4(iii): The horizontal axis refers to the bandwidth parameter number, while the vertical one corresponds to the estimated values of d. The dotted line refers to the 95% confidence interval for the I(0) hypothesis.

  share rose more or less steadily from 11.9% to 15.5%. In Germany (10.4m). In relative terms, the share of Germans travelling to Spain in all outbound German tourists rose from 14.0% in 1997 to 18.3% in 2001 but fell continuously back to 14.4% in 2004.

	(12.2m) outnumber
	F o r
	P
	e
	e r
	R e v
	i e
	w
	Spain, the largest incoming tourist group from 1997 (7.8 million stays) to 2002
	(11.3m) were Germans, according to Eurostat data. Only in 2003, did British tourists
	6

empirical application based on a long memory regression model

  Denoting Aggregate Wine Imports by AWI t and Deseasonalised Tourism as DT t ,

	4. An (either as DST t or MGRT t ) we employ through the model given by (1) and (2), testing
	H o (3) for given values d o = -2, -1.99, …, 0, …, 1.99, 2, assuming that u t is white
	noise and Bloomfield (with p = 1). 11 However, in order to examine the dynamic
	structure underlying the two series, we use as a regressor lagged values of the tourism
	series. 12 In other words, we test the null model,
	(5) (6)	AWI o + = t r ) 1 ( t o d x L =	DT t , t u	k	+	x	t	,
		P
		e
			e r
					R e v
								i e
								w

Table 8

 8

	also lists unit values (import value/import

their inter-temporal variance should be highly correlated to the one of the national-travellers series, thus resulting in similar parameter estimates.

Table 2 . Statistics for the Aggregate Wine Imports Series 2(i) Unit Root Tests

 2

	F o r
	P
	e
	e r
	R e v
	i e
	w

95% Confidence Intervals of the Non-Rejection Values of d

		No Regressors	With an Intercept	With a Linear Trend
	White noise	[-0.15 (-0.11) 0.06]	[-0.26 (-0.14) 0.02]	[-0.34 (-0.21) 0.02]
	Bloomfield (p = 1)	[-0.16 (-0.09) 0.32]	[-0.33 (-0.08) 0.31]	[-0.35 (-0.26) 0.28]
	Bloomfield (p = 2)	[-0.17 (-0.13) 0.39]	[-0.37 (-0.18) 0.36]	[-0.37 (-0.19) 0.34]

Table 3 . Statistics for the Deseasonalised Travellers (DST) series 3(i) Unit Root Tests

 3

		No Regressors	With an Intercept	With a Linear Trend
	ADF	-2.13 (-1.94)	-2.19 (-2.90)	-2.38 (-3.47)
	PP	-2.68 (-1.94)	-2.65 (-2.90)	-2.59 (-3.47)
	KPSS	---	0.98 (0.46)	0.44 (0.146)
	3(ii)

Confidence Intervals of the Non-Rejection Values of d

		No Regressors	With an Intercept	With a Linear Trend
	White noise	[0.62 (0.75) 0.95]	[0.62 (0.73) 0.89]	[0.65 (0.74) 0.89]
	Bloomfield (p = 1)	[0.40 (0.59) 0.91]	[0.45 (0.76) 1.02]	[0.61 (0.80) 1.03]
	Bloomfield (p = 2)	[0.30 (0.61) 1.14]	[0.32 (0.98) 1.31]	[0.58 (0.99) 1.39]

Table 4 Statistics for Monthly Growth Rate of Travellers (MGRT) Series 4(i) Unit Root Tests

 4

		No Regressors	With an Intercept	With a Linear Trend
	ADF	-2.50 (-1.94)	-2.43 (-2.90)	-1.64 (-3.47)
	PP	-3.02 (-1.94)	-2.91 (-2.90)	-2.44 (-3.47)
	KPSS	---	0.99 (0.46)	0.23 (0.146)
	4(ii)

95% Confidence Intervals of the Non-Rejection Values of d

		No Regressors	With an Intercept	With a Linear Trend
	White noise	[0.62 (0.73) 0.90]	[0.56 (0.79) 1.11]	[0.51 (0.77) 1.19
	Bloomfield (p = 1)	[0.56 (0.66) 0.80]	[0.57 (0.83) 1.11]	[0.52 (1.06) 1.39]
	Bloomfield (p = 2)	[0.60 (0.69) 0.82]	[0.66 (0.87) 1.10]	[0.63 (1.07) 1.41]

Table 5 . Estimates of Parameters in AWI t and TRAV t-k Relationship, Using the DST Series:

 5

	AWI	t	=	+	t DST	k	+	x	t	;	1 (	L	)	d	x	t	=	u	t
	5a)

With White Noise Disturbances (in Parenthesis t-Ratios)

	k	Alpha	Beta	d-95% Confidence Interval	d	Stat.
	1	16.738 (1152.21)	0.507 (2.951)	[-0.37 0.01]	-0.22	-0.0245
	2	16.751 (1126.75)	0.467 (2.713)	[-0.39 0.03]	-0.22	0.0445
	3	16.751 (1056.77)	0.358 (1.996)	[-0.39 0.04]	-0.21	-0.0445
	4	16.733 (1003.17)	0.352 (1.911)	[-0.37 0.04]	-0.20	0.0505
	5	16.736 (992.33)	0.293 (1.592)	[-0.39 0.04]	-0.20	-0.035
	6 7 8 9	16.755 (971.13) 16.761 (910.11) 16.770 (1190.02) 16.781 (1178.78) F o r	0.268 (1.440) 0.217 (1.147) 0.059 (0.345) -0.112 (-0.035)	[-0.36 0.05] [-0.36 0.04] [-0.41 0.04] [-0.38 -0.03]	-0.20 -0.19 -0.25 -0.24	0.0255 0.0053 -0.0157 -0.0065
	10 11 12	16.779 (1198.88) 16.784 (1137.70) P -0.070 (-0.445) -0.159 (-0.963) 16.773 (1153.71) -0.128 (-0.798) e	[-0.40 -0.03] [-0.39 0.02] [-0.41 0.01]	-0.23 -0.24 -0.22	-0.0367 0.0243 0.0451
	K	Alpha	Beta e r	d-95% Confidence Interval	d	Stat
	1 2 3 4 5 6 7	16.734 (2645.81) 16.713 (3555.17) 16.712 (3837.67) 16.752 (3640.92) 16.754 (4838.55) 16.753 (4411.51) 16.754 (2930.76)	0.486 (4.363) 0.433 (4.650) 0.396 (4.445) 0.368 (4.016) 0.302 (3.911) R e v [-0.62 0.03] [-0.75 0.02] [-0.77 0.02] [-0.85 0.01] [-0.95 0.05] 0.260 (3.219) [-0.97 0.05] 0.219 (2.136) [-0.94 0.05] i e w	-0.37 -0.46 -0.44 -0.43 -0.41 -0.49 -0.49	0.0065 -0.0004 0.0566 -0.0987 -0.0425 0.0044 0.0140

5b) With Bloomfield (p = 1) Disturbances (in Parenthesis, t-Ratios)

Table 6 . Estimates of Parameters in AWI t and TRAV t-k Relationship Using the MGRT Series:

 6

	AWI	t	=	+	t MGRT	k	+	x	t	;	1 (	L	)	d	x	t	=	u	t
	6a)

With White Noise Disturbances (in Parenthesis, t-Ratios)

	k	Alpha	Beta	d-95% Confidence Interval	d	Stat
	1	16.771 (1227.33)	0.234 (2.031)	[-0.45 0.04]	-0.25	-0.0242
	2	16.769 (1150.38)	0.223 (1.838)	[-0.44 0.09]	-0.25	-0.0354
	3	16.761 (1058.18)	0.045 (0.342)	[-0.43 0.12]	-0.22	0.0254
	4	16.764 (939.35)	0.135 (1.382)	[-0.41 0.14]	-0.17	-0.0235
	5	16.765 (959.99)	0.131 (1.081)	[-0.44 0.13]	-0.19	-0.0235
	6 7 8 9	16.766 (967.77) 16.769 (957.17) 16.766 (904.16) 16.769 (881.81) F o r	0.070 (0.577) 0.067 (0.617) 0.059 (0.863) 0.035 (0.256)	[-0.43 0.12] [-0.42 0.11] [-0.42 0.15] [-0.44 0.18]	-0.20 -0.19 -0.19 -0.19	-0.0153 0.0611 0.0783 0.0145
	10 11 12	16.763 (940.87) 16.763 (928.85) P 0.013 (0.045) -0.051 (-0.467) 16.763 (878.28) -0.043 (-0.376) e	[-0.44 0.15] [-0.45 0.11] [-0.44 0.15]	-0.21 -0.22 -0.21	0.0246 -0.0265 0.0556
	k 1	Alpha 16.777 (6113.28)	Beta 0.212 (6.134) e r	d-95% Confidence Interval [-1.33 0.17]	d -0.53	Stat 0.0129
	2 3 4 5	16.780 (8934.05) 16.766 (8500.14) 16.773 (10816.4) 16.773 (11433.3)	0.255 (11.041) 0.055 (2.400) 0.106 (6.152) 0.077 (4.688) R e v [-1.31 0.28] [-1.46 0.22] [-1.47 0.15] [-1.66 0.17]	-0.55 -0.53 -0.58 -0.56	-0.0545 -0.0365 -0.0654 0.0655
	6 7	16.772 (11261.3) 16.770 (9670.66)	0.073 (4.344) 0.057 (3.151)	[-1.62 0.21] [-1.62 0.21] i e	-0.67 -0.66	-0.0276 0.0065
	8 9	16.773 (10341.6) 16.771 (14991.8)	0.087 (5.137) 0.090 (8.744)	[-1.55 0.24] [-1.64 0.23] w	-0.66 -0.71	0.0067 -0.0869
	10	16.773 (10229.8)	0.021 (1.911)	[-1.63 0.24]	-0.69	-0.0317
	11	16.771 (12723.2)	-0.021 (-1.156)	[-1.71 0.19]	-0.67	0.0055
	12	16.765 (12960.2)	0.005 (1.056)	[-1.72 0.15]	-0.63	0.0156
	Note: In bold, significant values at the 5% significance level.
			34

6b) With Bloomfield (p = 1) Disturbances (in Parenthesis, t-Ratios)

Table 7 . Slope Coefficients in the Regression Using the DST Series 7a) With White Noise u t

 7 Sparkling wine; B: White from Penedús; C: White from Rioja; D: Wines from Navarra; E: Wines from Penedús: F: Wines from Rioja; G: Wines from Valdepeñas; H: Sherry. In bold, significant coefficients at the 5% significance level.

	k	A	B	C	D	E	F	G	H
	1	0.275	-1.682	-1.264	1.497	0.001	-0.327	1.432	-0.354
	2	0.307	-0.821	-0.835	1.458	0.204	0.100	1.174	-0.736
	3	0.287	-0.403	-0.311	1.229	0.529	-0.404	1.279	-1.049
	4	0.519	-0.887	-1.048	1.073	0.651	-1.050	0.861	-1.520
	5	0.608	-0.022	0.024	1.000	1.226	-0.548	1.567	-1.101
	6	0.612	0.087	-0.694	1.101	1.614	-0.900	1.227	-1.120
	7	0.617	1.234	-1.059	1.248	1.521	-0.829	1.532	-1.445
	8	0.279	-0.673	-0.525	0.895	1.249	-0.560	1.332	-0.900
	9	0.019	-1.980	-0.897	0.612	1.447	-1.074	1.100	-0.864
	10	0.098	-1.560	-0.336	0.799	1.483	-1.335	1.008	-0.875
	11	-0.045	0.293	-1.177	1.257	2.177	-1.198	1.256	-0.399
	12	0.149	0.890	-0.782	0.482	2.287	-1.206	1.437	-0.273
				7b) With Bloomfield (p = 1) u t
	k	A	B	C	D	E	F	G	H
	1	0.274	-1.697	-1.112	1.674	-0.291	-0.033	1.226	-0.135
	2	0.304	-1.583	-0.379	1.552	0.018	0.867	0.894	-0.701
	3	0.308	-1.574	1.111	1.429	0.387	-0.008	1.250	-1.015
	4	0.518	-1.470	-1.221	1.361	0.368	-1.074	0.602	-1.522
	5	0.604	-1.225	1.953	1.028	1.157	0.344	1.534	-1.062
	6	0.612	-1.188	-0.403	1.144	1.605	-0.739	1.119	-1.103
	7	0.615	-1.073	-1.212	1.310	1.537	-0.480	1.539	-1.474
	8	0.279	-1.326	0.284	0.924	1.455	0.622	1.265	-0.905
	9	0.039	-1.398	-0.876	0.544	1.873	-0.547	1.034	-0.869
	10	0.121	-1.194	0.690	0.805	2.012	1.206	0.947	-0.885
	11	-0.011	-0.803	-1.485	1.263	2.309	-1.547	1.275	-0.404
	12	0.196	-0.622	-0.698	0.468	2.353	-1.437	1.464	-0.310
	Note: A:

Table 8 . Summary Results from Estimated Regressions: Relationship Between German Tourists to Spain and German Imports of Spanish Wine

 8

				Wine type
		Aggregate (total) (AWI)	Sparkling (A)	Quality red from Navarra (D)	Quality red from Penedús (E)	Quality red from Valdepeñas (G)
	Average lengths of effect (months)	5.5	3	10	8	11
	Average sum of effects (euros per year)*	950,000	869,000	---	---	---
	Contribution to total
	yearly category	0.4	0.8	---	---	---
	imports (in %)
	Average share in AWI
	value (%), Jan. 1997 to	100	38.8	2.6	1.2	2.9
	Nov. 1994
	Import unit value (euros per liter), 2003	1.34	2.72	1.64	3.11	2.70

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

This means that the test is the most efficient one when directed against local alternatives. In other words, if we direct the tests against the alternative: H a : d = d o + YT -1/2 , the limit distribution is normal, with variance 1 and mean that cannot be exceeded in absolute value by any rival regular statistic.

The periodogram is an asymptotic unbiased estimate of the spectral density function f( ). If a series is I(0), 0 < f(0) < , and if it is overdifferenced, f(0) = 0. Thus, the periodogram should mimic that behaviour.

The first two methods (ADF, PP) test the null hypothesis of a unit root (I(1)) against the alternative of stationarity, while KPSS tests the null of stationarity against the alternative of a unit root.

This is a non-parametric approach of modelling the I(0) disturbances that produces autocorrelations decaying exponentially as in the AR(MA) case. An empirical application of this procedure can be found in Gil-Alana and[START_REF] Gil-Alana | Testing of unit roots and other nonstationary hypotheses in macroeconomic series[END_REF].

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

Appendix A

The test statistic proposed by [START_REF] Robinson | Efficient Tests of Nonstationary Hypotheses[END_REF] is based on the Lagrange Multiplier (LM) principle, and is given by:

where T is the sample size and


Keywords: interest rates, monetary policy, currency substitution, inflation, exchange rates interest rates, monetary policy, currency substitution, inflation, exchange rates

This paper conducts a thorough intertemporal analysis of nominal interest rate based monetary policy. Its main contribution is to show how such a policy can have different effects depending on the assumptions made about the saving and borrowing behaviour of firms. We consider two cases (i) consumers are savers and firms are borrowers, and (ii) both consumers and firms are borrowers (the nation as a whole is borrowing from abroad). In one case we confirm conventional wisdom, but in the other case we find there may be unexpected and surprising results. Moreover, our analysis has important implications for both inflation and nominal exchange rate targeting policies.

Introduction

The central innovation of this paper is that it conducts a thorough intertemporal analysis of the effects of using the nominal interest rate as the instrument of monetary policy. It pays careful attention to the impact of different assumptions about the saving and borrowing behaviour of consumers and firms in generating different policy effects, and the role of inflation in accounting for the divergence between the real interest rate and the real return on capital.

The debate concerning choice of monetary policy instrument can be traced right back to [START_REF] Poole | Optimal Choice of Monetary Policy Instruments in a Simple Stochastic Macro Model[END_REF] who analysed the choice between the nominal interest rate and the nominal money stock in the presence of stochastic shocks. In the absence of risk, the choice of the nominal interest merely implies a willingness to allow the money stock to adjust endogenously to accommodate the nominal interest rate target. [START_REF] Sargent | Rational Expectations, the Optimal Monetary Instrument, and the Optimal Money Supply[END_REF] then showed that, under rational expectations, nominal interest rate pegging led to an indeterminate price level. It was later shown that minor respecifications of the model could restore price determinacy, such as specifying the aggregate demand function in terms of the real money stock, as in [START_REF] Mccallum | Price Level Determinacy with an Interest Rate Policy Rule and Rational Expectations[END_REF]. It is implicitly this specification that is embodied in our model, hence ensuring price determinacy. [START_REF] Artis | Monetary Targets and the Exchange Rate -A Case For Conditional Targets[END_REF] then extended this conventional ISLM based analysis to explicitly consider the effects of exchange rate targeting. In the late 1980s the emphasis of research shifted to the analysis of target zones, reflecting in part the problems experienced by national monetary authorities operating under the European Exchange Rate Mechanism, and in part the development of the application of techniques of stochastic calculus. This literature used stochastic extensions of [START_REF] Dornbusch | Expectations and Exchange Rate Dynamics[END_REF] models to focus on the analysis of regime switches and the effects of speculative attacks. A good representative collection of this literature is to be found in Krugman and Miller (1992). [START_REF] Mishkin | International experiences with different monetary policy regimes[END_REF] and [START_REF] Taylor | The robustness and efficiency of monetary policy rules as guidelines for interest rate setting by the European Central Bank[END_REF] consider different monetary regimes.

Since the nominal interest rate has increasingly become the central instrument of monetary policy in recent years, it is necessary for economists once again to turn their attention to renewing the analysis of this monetary policy but under a more modern intertemporal framework. [START_REF] Turnovsky | Monetary and Fiscal Policy under Perfect Foresight: An asymmetric two-country analysis[END_REF] and [START_REF] Turnovsky | Optimal government finance policy in a stochastically growing open economy[END_REF] show how optimal monetary policy should be directed towards interest rate targeting modelling the intertemporal utility of representative agents. In their paper they show that reasonable monetary growth eliminates the need for continuous exchange rate intervention. [START_REF] Alexandre | Inflation Targeting and Exchange Rate Targeting[END_REF], [START_REF] Sutherland | Inflation targeting in a Small Open Economy' mimeo Svensson, L[END_REF] and Svensson (1999) concentrate on inflation targeting and have similar conclusions to those of interest rate targeting. Our findings show that the determination of the exchange rate depends on the intertemporal decisions of consumers and firms under different borrowing scenarios. We analyse two cases, the first where firms are borrowers and consumers are savers, and second where both are borrowers from abroad. This change in international indebtedness brings about exchange rate movements, a central innovation of this our paper. This is the task we attempt in this paper. However, our analysis is sufficiently robust to capture the use of the nominal interest rate to target the inflation rate or the nominal exchange rate.

The paper is organised into a series of sections and subsections as detailed below. In section 2 we construct the model, focusing first on production and then on consumption. We then solve the basic model, and construct the diagram that will be used to illustrate the analysis. In section 3 we analyse

The Model

In this section we construct the basic model used in our analysis, which employs a [START_REF] Ramsey | A Mathematical Theory of Saving[END_REF] type framework to analyse the behaviour of representative consumers and firms. Section 2.1 looks at the behaviour of firms and section 2.2 the behaviour of consumers. Section 2.3 then details the solution to the model, and finally section 2.4 constructs the diagram that is later used to illustrate the policy analysis, which takes place in section 3.

Production

We consider a continuum of identical monopolistically competitive firms1 , each producing a single differentiated product, indexed

( ) j i j i A P p A =
. Thus

1 1 = j i j i A PA p
and hence marginal revenue is ( )

( ) ( ) ( ) 1 1 1 = i j i j j i p A PA A . Equating with marginal costs (right hand side of equation ( 2)) gives ( ) ( ) [ ] ( ) [ ] 1 + = i i i i i y k R y a W p
and using the definition of price in equation ( 3) yields a solution for output as

( ) ( ) { } ( ) ( ) { } [ ]( ) [ ] 1 + + = i i i i i i i i i y k R y a W y Rk y Wa y
. This follows [START_REF] Helpman | Market Structure and Foreign Trade[END_REF]. 5 We assume that the government runs a balanced budget. Since the government deficit is always zero it therefore does not need to appear in the national income identity. The Ricardian Equivalence Proposition, which holds in this model, implies that we do not need to analyse issues surrounding the effects of tax versus bond finance, as in Obstfeld andRogoff (1995, 1996). ) ( )

d d d d d M d d d d A R L M A U L M A U + = 1 , , , , ( 5)
This states that the marginal rate of substitution of consumption to money holdings (i.e. future consumption) is equal to the intertemporal price 6 .

Another way of stating this is the more familiar condition that the marginal rate of intertemporal substitution (MRIS) is equal to the real interest rate.

Graphically this yields the tangency condition between the budget line (BCI)

and the community indifference curve at point A0 on Figure 1. This may be rewritten as

( ) ( ) ( ) ( ) ( ) ( ) d d d d d d A d d d d d A d d d d M I L M A U R L M A U L M A U + + = + = 1 1 , , 1 , , , , ( 6)
using the definition of the real interest rate ( ) ( ) ( )

d d d I R + + = + 1 1 1
, where d I is the domestic nominal interest rate and d the domestic inflation rate 7 ,

6 Note that usually the marginal rate of intertemporal substitution refers to the substitution between goods over time, whereas here it is between goods consumed today and money held today (that is saving today held as money balance for future consumption).

7 Domestic inflation is defined as

d d d P P & where d P is the price index 7 ( ) ( ) ( ) + = 1 1 0 1 1 1 n n f d d di i p C E di i p P
where E is the domestic nominal exchange rate (domestic price of foreign currency. Since we have assumed that the domestic country is large, this implies n is closer to one than zero. Similarly, foreign inflation is defined as )

H K Zq Y , = we obtain ( ) ( ) ( ) + = 1 1 0 1 1 1 n n f d f di i p di i p E C P Notice that this implies that = + + f d p p P P ( ) d d d d R W p p , = , ( ) f f f f R W p p , =
(prices equal to marginal products) from equation ( 4), noting that

d d M W & & = , f f M W & & =
(growth of real wages must equal the growth of real money), hence

= + M p p d d & & & and = + L p p f f & & &
a la Quantity Theory, but not in one for one proportion.

8 This is similar to the Keynesian transactions demand for money. 9 Due to an intertemporal effect, rather than a Keynesian speculative effect. 10 A rise in inflation causes agents to substitute present consumption in place of future consumption, increasing the real demand for money (life-cycle theory of consumption). 11 That is, the production function has the normal properties (importantly, diminishing marginal productivity).

8 d K K d R Zq Zq K + = = = = ! 1 " " # " #" " " ( 7) d H H d W Zq Zq H + = = = = ! 1 " " # " #" " " #" & & & ( 8)
Equation ( 7) states that the marginal rate of intertemporal transformation (MRIT) is equal to the real return on capital. Graphically this yields the tangency condition between the budget line (BC) and the intertemporal production possibility frontier (IPPF) at point P0 on Figure 1. As the real interest rate declines, given the property of diminishing marginal productivity, the marginal product should decline. This encourages and investment and increases capital. Similarly, equation ( 8) states the usual condition that the marginal product of labour is equal to the real wage rate.

Figure 1 Optimal investment takes place where the cost of investment, given by the marginal product of capital, K Zq in equation ( 7), is equal to the return on investment, given by the real return on capital ( )

d R + 1
in equation ( 7). gives us the production point with no investment and P1 gives us the production point with optimal investment.

General equilibrium condition

The model is closed by equating ( 5) with ( 7), which yields the general equilibrium condition that the marginal rates of intertemporal transformation and substitution are equal 7) and ( 8), however, means that firms bring forward less production (comparing pre policy point P1 with post policy point P2). That is, current production falls and the real return on capital is reduced (the slope of BCG is less than the slope of BCI). Note that redistribution has occurred from consumers to producers.

( ) ( ) ( ) d d M A R K q Z U U + = = 1 (
Consumer welfare has fallen, but producers' stocks of capital have been less depleted (there remain higher stocks of future capital). Thus, the effect of the government's cut in the nominal interest rate has led to a fall in current production, reduced consumption and welfare, fall in real wages and deflation.

The effect on the trade balance is ambiguous (we do not know whether T2 is greater or less than T1) since it is impossible to say whether the fall in production is greater or less than the fall in consumption. Consumers are consuming less in the current period and domestic producers are also producing less.

If consumption is cut less than production, the trade balance worsens (since imports must rise to fill the gap between domestic production and domestic consumption). The real exchange rate will consequently rise (there will be a real depreciation). It is impossible to say whether the nominal exchange rate will appreciate or depreciate since, from equation ( 10), the inflation rate has also fallen 12 . Thus if policy makers are using the nominal interest rate as an instrument to achieve a nominal exchange rate target, their target may not be realised.

12 From the definition of the real exchange rate,

( ) ( ) [ ] [ ] + + = + 1 1 1 E E C C & & , then ( ) ( ) [ ][ ] + + = + 1 1 1 C C E E & & . Since ( ) C C & + 1 and [ ] + 1
are moving in opposite directions, the effect on ( ) It is not hard to replicate this analysis for other plausible scenarios 13 . For example, suppose that firms are again borrowers, but this time consumers are borrowers, too. The fall in the nominal interest rate will reduce consumer' cost of borrowing, stimulating consumption in both periods (again due to consumption smoothing behaviour). This leads to demand push inflation.

E E & + 1 is
13 To avoid undue repetition of analysis, we don't consider the case where both consumers and firms are savers (in this scenario, there is a similar ambiguity concerning the effects on the trade balance and hence the exchange rate that are detailed above in Case I). Likewise we don't repeat the standard textbook [START_REF] Obstfeld | Foundations of International Macroeconomics[END_REF] case where firms are savers and consumers are borrowers (here the trade balance unambiguously improves, and hence is identically opposite to the analysis provided in Case II below). Thus in this case, we have the same redistribution from consumers to firms, reduced production (and, from equation ( 8), reduced real wages), but this time the added ogre of increased inflation. It is also clear that the trade deficit has increased, from T1 to T2, and hence net international debt will have risen. Balance of payments adjustment will, of course, mean that there will follow a real depreciation (weakening) of domestic currency. Since there has been a real depreciation and a rise in inflation, this implies by definition that there will be a nominal depreciation. In summary, a cut in the nominal interest rate when both firms and consumers are borrowers leads to a fall in real wages, a fall in the real return on capital, a rise in net international debt, a weakening of the domestic currency and a rise in domestic inflation. However, despite these, the welfare of the consumer and the benefits to firms have improved. Essentially, the firms and consumers are borrowing from themselves in the future through the mechanism of increasing international indebtedness today (borrowing from the rest of the world). This is to be contrasted with the first case where the welfare effects are ambiguous. In that case it is not known whether benefits to firms outweigh the loss of welfare to consumers. Production falls but consumption falls too. So the effect on the trade balance cannot be determined. Hence the effect on the exchange rate is also ambiguous. Since control of the money supply has proved to be an elusive target, governments have increasingly switched to using the nominal interest rate as their instrument of monetary policy. Policy makers have, however, been surprised by results that seem to be at variance with the effects suggested by orthodox economic theory. This paper makes a significant contribution to the policy making debate by explicitly considering the effects of different assumptions about consumers' and producers' saving and borrowing behaviour on the policy outcomes.

A cut in the nominal interest rate is normally expected to increase investment and consumption expenditure and hence output. However, we show in this paper that in some circumstances neither of these may happen and hence the results of the policy are very different from those which the policy makers expected. When firms are borrowers and consumers are savers, a cut in the nominal interest rate reduces current production and current consumption. The fall in the nominal interest rate reduces consumers' income from savings, forcing them to decrease their consumption, not only in the current period, but also in the future period due to consumption smoothing behaviour. The fall in current consumption leads to an endogenous fall in the money stock and hence demand deficient deflation. Since consumption smoothing implies that the fall in the inflation rate is less than the fall in the nominal interest rate, there will be a fall in the real interest rate. The fall in the real interest rate means that firms will bring forward less production from the future (that is, deplete the future capital stock less). Thus, both current consumption and production have fallen, giving an ambiguous effect on the trade balance. The fall in the real interest rate implies in the long run a fall in the real wage (there is long run factor price equalisation). Overall, The ambiguous effect on the trade balance implies an ambiguous effect on the real exchange rate. In one case the cut in the inflation rate and decrease in the real exchange rate work in the same direction to cause a nominal exchange rate fall (nominal appreciation). In the other case, the fall in inflation and rise in the real exchange rate generate an ambiguous effect on the nominal exchange rate. The conclusion in this respect therefore is that if policy makers explicitly or implicitly target the nominal exchange rate they may get unexpected results.

When both consumers and producers are borrowers, a fall in the nominal interest rate stimulates consumption in the current period (due to the lower cost of borrowing) and in the future period (due to consumption smoothing behaviour). The increase in current consumption causes an endogenous rise in the money stock and hence demand push inflation. The combination of the fall in the nominal interest rate and the rise in the inflation rate work in the same direction to reduce the real interest rate. Thus production again falls.

Since current consumption has risen and production has fallen, the trade deficit widens, implying that the domestic country becomes more indebted to the rest of the world. One could therefore reinterpret the rise in inflation as imported inflation -the terms of trade have worsened. There has not only been a redistribution from consumers to firms, but also from the domestic country as a whole to the foreign country (even though domestic consumer welfare improves).

Thus, the saving and borrowing behaviour of firms and consumers matters crucially when determining the likely effects of nominal interest rate policy.

  monetary policy in terms of interest rate changes, and the implications on inflation, first when consumers are savers and firms are borrowers, and secondly where both consumers and firms are borrowers. Conclusions are presented in section 4.

  varies positively with inflation)10 . Note that changes in the nominal interest rate, which is the policy variable in this model, induces endogenous changes in the money supply. In other words, a change in the nominal interest rate affects the money supply directly, and affects the inflation rate through changes in the transaction demand for money. Inflation therefore is not entirely a monetary phenomenon.2.2.4.2 Production Euler EquationAssuming a standard 11 production function for the representative monopolistically competitive firm of the form

(

  is represented by the tangency between the production function (PF) and the budget line (BCI) at point P1 in Figure1. Thus P0

  subdivided into two sections. The first details the case where firms are borrowers and consumers are savers. The second details the case where both firms and consumers are borrowers. 3.1 Case I (Firms are borrowers, consumers are savers) In this section we consider the effect of a nominal interest rate cut by the domestic government under the assumption that firms are borrowers and consumers are savers. The cut in the nominal rate of interest affects both the domestic inflation rate and the real interest rate. Firstly the interest rate cut reduces consumers' income from savings and hence depresses domestic consumption. The real money supply adjusts in the real interest rate assuming that the fall in the inflation rate is less than the fall in the interest rate, a plausible assumption due to consumption smoothing behaviour. Graphically, the fall in the real interest rate is represented by an anticlockwise rotation of the budget line to the position BCG shown on Figure 2. The aim of the government's reduction in nominal interest rates is to reduce the costs of firms' borrowing and hence stimulate investment and production. In terms of this model, firms are borrowing by bringing forward production (extracting capital resources from the future to use for today's production). Thus the production function (PF) drawn in Figure 2 faces in the opposite direction from that drawn earlier in Figure 1. The fall in the real interest rate established above, from equations (

  Figure 2Interest rate cut when firms are borrowers and consumers are savers

  10) we can see that the real interest rate again falls, but this time by a higher magnitude, since now the fall in the nominal interest rate and rise in inflation are working in the same direction to reduce the real interest rate. The analysis conducted above for producers carries through to this case, too. That is production falls. The corresponding diagrammatic representation is shown in Figure3below.

Figure 3

 3 Figure 3Interest rate cut when both firms and consumers are borrowers

  increase the nominal interest rate, they will succeed in cutting inflation, but at the cost of a nominal appreciation (strengthening of the domestic currency). This is precisely what happened in the UK during the nominal interest rate rises leading up to 1998.

  fallen and there has been a redistribution from consumers to producers.

  ambiguous.

	If consumption is cut more than production, the trade balance improves
	(since fewer imports are needed to meet the shortfall of domestic production
	in meeting domestic consumption). The real exchange rate will consequently
	fall (there will be a real appreciation. A real appreciation combined with a
	fall in inflation implies a nominal appreciation. In this case, if policy makers
	had an exchange rate target, there is here the opportunity to meet it. Of
	course, in practice, policy makers do not necessarily have all the information
	at hand ex ante to know which of these cases prevails. Here, a cut in the
	nominal interest rate has led to a fall in inflation and possibly a nominal F appreciation (a strengthening of the domestic currency). This is somewhat at o r variance with conventional wisdom (Bank of England Monetary Policy
	P
	e e r
	R e v i e w
	13

Committee, 1999) that a fall in the nominal interest rate leads to a rise in the nominal exchange rate (a nominal depreciation). This may explain why Pound Sterling remained strong in the UK despite the successive 25 basis points nominal interest rate cuts from 1998:Q4 through 1999:Q1 (essentially as consumers postponed their consumption).

3.2 Case II (Both firms and consumers are borrowers)

In order to build a two country model we require there to be product differentiation, some varieties of which are produced domestically and some in the foreign country, hence the assumption of monopolistic competition rather than perfect competition is appropriate and standard in this type of literature, see for exampleObstfeld and Rogoff (1995, 1996).

A full symbols list is provided in Appendix A.

& & & &, and since

& & &

E52 -Monetary Policy (Targets, Instruments, and Effects) < E5 -Monetary Policy, Central Banking,

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript


Xosé-Antón A Rodríguez

Julio Pallas

Determinants of foreign direct investment in Spain

Keywords: foreign direct investment, panel data, Spanish sectors and regions foreign direct investment (FDI), panel data, Spanish sectors and regions JEL classification: C23, F21

INTRODUCTION

Foreign Direct Investment (FDI) has been instrumental in shaping the dynamic of the globalization process within different economies and the Spanish economy has been no exception. FDI, besides having important implications in terms of a country's balance of payments, also affects the productive structure of the country receiving the investment, its business organization, potential technological change and innovation and the geographical distribution of production and employment. It would seem important from an empirical perspective therefore, to be able to establish and analyze those factors that have played a key role in determining FDI in Spain.

A review of the literature that focuses on the determinants of Spanish FDI, reveals that most empirical research have focused on the study of macroeconomic variables and have used aggregate data for the whole of the Spanish economy (Varela and Rodríguez de Pablo, 1974;[START_REF] Donges | La industrialización en España. Políticas, logros, perspectivas[END_REF][START_REF] Felipe | Un modelo de la balanza de capitales española, paper presented in the V Meeting Asepelt-España[END_REF]Bajo, 1991a;[START_REF] Bajo | Modelling foreign direct investment in Spain, 1961-1988: An application of cointegration analysis[END_REF]Bajo and Sosvilla,1992;[START_REF] Muñoz | La inversión directa extranjera en España: Factores determinantes[END_REF].

From the early 1990's onwards however, this type of study has also been carried out on a regional level (Egea and López, 1991b;[START_REF] Díaz | Inversión extranjera directa en España[END_REF]. This paper investigates the determinants of FDI in Spain by simultaneously examining macroeconomic, sectoral and regional factors. The present study also incorporates a wider range of factors than those that have previously attempted to analyze FDI in Spain. The paper is organized as follows. Section 2 both reviews and explains some of the methodological issues related to the modelling of FDI determinants. Section 3 contains the empirical analysis. The final section provides some conclusions and policy recommendations. The methodology most frequently adopted when analyzing the factors that determine foreign investment is conditioned by the fact that the agents of this investment are firms. The works of [START_REF] Barrell | An econometric analysis of US foreign direct investment[END_REF], Stevens and Lipsey (1992) or Bajo andSosvilla (1992, 1994) provide sturdy foundations upon which to base a theoretical model. The above studies suggests the following relationship between FDI (at any given moment in time) and its determinants: [START_REF]) or (R) depending on whether the analysis refers to the sector or the region 2. t-ratio appear in parenthesis 3[END_REF] where D represents the size of the domestic market (the aggregate demand), C represents the relative unit costs (of the host country compared to the country of origin), B represents the potential trade barriers, K t-i represents the volume of foreign capital at the start of the period analyzed and E1 represents a group of factors that are external to the firm but which are capable of significantly influencing its levels of production The empirical models that are concerned with the determinants of foreign investment usually consist of a generalized version of the theoretical model [START_REF]) or (R) depending on whether the analysis refers to the sector or the region 2. t-ratio appear in parenthesis 3[END_REF] 2 .

METHODOLOGY

IED t = h(D t , C t , B t, K t-i , E t )
The specification of the empirical model normally involves using a multiple regression model, one which is usually log-linear [START_REF] Bajo | Un análisis empírico de los determinantes macroeconómicos de la inversión extranjera directa en España, 1961-1989[END_REF]Muñoz,1999). Therefore, equation [START_REF]) or (R) depending on whether the analysis refers to the sector or the region 2. t-ratio appear in parenthesis 3[END_REF] can be written as:

t t LnE i t LnK t LnB t LnC t LnD t LnIED + + + + + + = 5 4 3 2 1 0 [2]
where Ln represent natural logarithms.

There is a distinct lack of consensus, in the vast majority of empirical studies, as to the relative importance and the direction of the impact of the potential determinants of FDI. This suggests that the exact relationship between FDI and its determinants is essentially empirical. The aggregate demand or the size of the market of the country that receives the direct investment should have a positive influence on FDI [START_REF] Billington | The Location of Foreign Direct Investment: An Empirical Analysis[END_REF][START_REF] Chandprapalert | The Determinants of U.S. Direct Investment in Thailand: A Survey on Managerial Perspectives[END_REF][START_REF] Love | Analysis the determinants of US direct investment in Mexico[END_REF][START_REF] Chakraborty | Foreign direct investment and growth in India: a cointegration approach[END_REF][START_REF] Janicki | Determinants of foreign direct investment: empirical evidence EU accession candidates[END_REF]. Although there is no complete consensus with respect to the empirical results obtained for relative unit costs, most of these results indicate that they have a negative influence on FDI [START_REF] Chakrabarti | The Determinants of Foreign Direct Investment: Sensitivity Analysis of Cross-Country Regressions[END_REF]. The effects of either openness or trade barriers on FDI have also been widely debated. The ease of entry to economic markets may be a factor that plays a role in the attraction of FDI (Root and Ahned, 1978); hence, it is to be expected that, in general, barriers to trade act as a negative stimulus to FDI (Mundell, 1957), although there are empirical studies that would seem to contradict this point of view): while [START_REF] Culem | The Locational Determinants of Direct Investments Among Industrialized Countries[END_REF] reported a significantly negative correlation between trade barriers and FDI, [START_REF] Lunn | Determinants of U. S. Direct Investment in the E. E. C.: Further Evidence[END_REF] observed a positive relationship, and Blonigen and Feenstra (1996) found that trade barriers play an insignificant role in attracting FDI. Past decisions with respect to direct investment may also affect future flows of FDI, in this case positively, since they establish patterns of behaviour with respect to investment preferences. Finally, it is not known, a priori, what effects the group of factors that are external to the firms themselves will have; this will depend on the each individual factor. Factors such as infrastructure, technological and human capital should have a significantly positive effect on FDI.

EMPIRICAL ANALISIS

Data and variables

The study of the determinants of FDI in Spain may adopt at least two distinct approaches: the first of these involves considering the total aggregate investment for the whole of Spain and the second uses different levels of investment in order to study a regional or sectoral breakdown of FDI. The second approach also allows the study to take advantage of the positive econometric effects that are derived from the use of panel data techniques3 . In order to maximize these advantages, the determinants of foreign investment in Spain are analyzed by looking at a breakdown of the 28 sectors of the Spanish economy (set out according to the National Classification of Economic Activities, 1993). The 13 main manufacturing sectors are then analyzed and a third application is also carried out for the 17 Spanish Regions. The temporal period covered in the study spans the years 1993-

2002.

The dependent variable used was gross effective investment which, in addition to accurately reflecting the flows of FDI, facilitates the sectoral and regional analysis of said investment. Hence, this empirical work uses the homogeneous series of Gross Effective Foreign Investment provided by Spanish Ministry of Economy (SME) since this is believed to be the best indicator of FDI.

A priori, there would seem to be a wide and varied array of potential factors, both intrinsic and extrinsic to the firms that might in some way affect the foreign firms' decisions to invest in any given country (see [START_REF] Muñoz | La inversión directa extranjera en España: Factores determinantes[END_REF]. On looking at the FDI literature cited above and by taking equation [2], it may be deduced that the empirical model should include at least three types of FDI determinants: factors linked to the demand or the size of the market (D), factors that ultimately affect the firms' costs or profits -such as the group of factors C, B, E-and, to some extent, the previous decisions taken with respect to FDI (K t-i ). Table 1 therefore, provides a regional and a sectoral breakdown of the determinants of Spanish FDI.

INSERT

Specification of the models and results

The empirical analysis of the determinants of FDI, which utilizes a breakdown of the flows of investment received, in terms of productive sectors and autonomous regions, is estimated using panel data techniques. Therefore, if there is a panel of N sectors or regions, for T periods of time, the model [2], which does not consider specific temporal effects, may be expressed as follows:

LnFDI i t = it + i 'LnX it + it ; i=1,...N ; t=1,...T [3]
where Xit and i are k-vectors of independent variables (determinants) and parameters respectively; it is the component which includes the specific characteristics (individual effects), and it is the disturbance. The appropriate estimation method and the estimators' properties will depend on the characteristics of it and it (and on the relation between them), as well as on the relation between the independent variables and the disturbance.

This work uses a panel of 252 observations for the temporal period of 1993-2002 4 when all of the 28 selected productive sectors are available. 117 observations are available when the 13 manufacturing sectors are analyzed and 153 for the 17 regions. The estimations are carried out using the Econometric Views program 5.0.

The first part of the study evaluates three alternative forms of modelling (3). The proposals for modelling using fixed or random effects

+ + + + + + = 4 3 1 2 ) ( 1 0 [4]
where SECFDI represents the FDI for each sector. Given the existence6 of heteroskedasticity and the characteristics of the sample in which T<N, the most generic of the methods, SUR, cannot be applied. Hence the model [4] is estimated using the GLS (Cross Section Weights) method. The results of the estimation of this model are given in table 2.

The findings suggest that the model is robust. The adjusted-R 2 indicated good explanatory power. The reported F-statistic is substantial enough to conclude that there is joint significance of the chosen independent variables. The results indicate that the level of foreign direct investment according to sectors (SECFDI) depends on; the particularities of each sector (DU), the difference between the productivity of each employee and the cost of each employee (SECPLP-SECCOST)7 , on past decisions (SECFDI(-1)) -those sectors that proved to have the most attractive power in the past also tend to maintain this power in the present -, the fiscal pressure in Spain (FISC) and the capacity that the sectors have in order to keep their domestic markets supplied (DM). As one might expect, all of these variables, with the exception of FISC, take a positive sign. The first four variables are significant at the conventional level of 5%. The DM variable is significant at 19%. The 28 sectors analyzed are very heterogeneous and take in activities that vary substantially, both in terms of quantity and quality. Therefore, a specific analysis is carried out which looks at the 13 manufacturing sectors, i.e. those sectors that have most in common and those that focus on those processes of industrial transformation that are generally intensive. For the manufacturing sectors the following model provides the best results:

it it LnSECEXP t LnSPCPI it LnDM t FISC it LnSECFDI it SECCOST it SECPLP Ln it DU it LnSECFDI + + + + + + + + = 6 5 4 3 1 2 ) ( 1 0
[5]

The model [5] is also estimated using the GLS (Cross Section Weights) method and the results are given in table 2. On utilizing the data relevant to the industrial branches alone, the significance of the variables included in model [4] is, once again, thrown into sharp relief. The one exception is the FISC variable which stops being statistically significant in explaining the behaviour of SECFDI8 . Within the same context however, the analysis reveals that the SPCPI variable (prices in Spain compared to the EU average) is highly significant (and logically takes a negative sign), as is the SECEXP variable, which reflects the level of exports generated by the industrial sub-sectors and, to a certain extent, measures how competitive they are.

The capacity of a country to attract FDI may be measured in a disaggregate way by measuring the investment in its economic sectors or by considering the geographical location of the country within which this investment is taking place.

In the case of the latter, the methodology involves trying to determine those factors that exist in a given zone that convince foreign firms to set up there.

+ + + + + + + + + = 7 6 5 4 3 1 2 ) ( 1 0 [6]
where RFDI represents regional FDI. Model [6] is also estimated using the GLS (Cross Section Weights) method and the results are given in table 2. The results of the model [6] vindicate those obtained in former studies in the sense that the level of foreign direct investment according to region (RFDI) depends on the particularities of each region (DU), the difference between the value of the productivity per employee and the cost of each employee (RPLP-RCOST), on past decisions (the regions with the greatest power to attract FDI in the past continue to exert this power in the present) and demand factors, represented by the RGDP of each region. Of the two national Spanish variables, FISC and SPCPI, it is the SPCPI variable that reveals itself as being the most statistically important in explaining investment. The model [6] provides information which is not contained in [4] and [5] since the results indicate that regional human capital has a positive effect on attracting investment, an effect that is statistically significant at the 12% level as opposed to 5%. This is probably due to the fact that a part of its effect has already been captured by the partial labour productivity variable. On a regional level, Madrid must be modelled slightly differently, since it receives a particularly high relative proportion of investment. This is achieved by introducing a dummy variable for the region (DUM). The variable is significant at the 5% level and confirms the importance of Madrid as a national service centre. If the endogeneity of the lagged dependent variable (FDI(-1)) is taken into account, then the GLS (Cross Section Weights) method becomes inappropriate and an instrumental variables method must be used. Potential methods include; the Two-Stage Least Squares (2SLS), the Weighted Two-Stage Least Squares (W2SLS) and the Three-Stage Least Squares (3SLS) methods. Given that this study uses panel data, the W2SLS is used. The W2SLS is an appropriate technique when some of the right-hand side variables are correlated with the disturbance term and the model is heteroskedastic. The estimation of the models [4], [5] and [6] by means of the W2SLS method is given in table 2, in which the variable FDI (-2) is used as an instrument of FDI (-1).

INSERT TABLE 2 ABOUT HERE

INSERT TABLE 3 ABOUT HERE

The results presented in Table 3 vary slightly with respect to the earlier estimates presented in Table 2. One of the only really substantial differences is the improvement in the statistical significance of the human capital (HK) and relative fiscal pressure (FISC) variables, when the estimation is carried out by means of the W2SLS method in model [6].

CONCLUSIONS AND POLICY IMPLICATIONS

The vast majority of empirical studies analyzing the determinants of foreign investment in Spain have tended to focus upon macroeconomic factors and to a lesser extent on regional factors. This study looks jointly at macroeconomic, regional and sectoral factors.

The demand factors (DM and RGDP) have, in general, been statistically significant. This result is in line with those obtained by Egea and López (1991b), [START_REF] Bajo | Un análisis empírico de los determinantes macroeconómicos de la inversión extranjera directa en España, 1961-1989[END_REF], [START_REF] Muñoz | La inversión directa extranjera en España: Factores determinantes[END_REF] and [START_REF] Díaz | Inversión extranjera directa en España[END_REF].

It should be underlined that, in the three models estimated in this study, the variable that takes in the difference between the productivity of labour and its cost plays a key role in explaining the behaviour of FDI. This result both clarifies and, reaffirms the results obtained in similar studies, in the sense that it quite clearly shows that firms are motivated, not solely by the evolution of labour costs, but by the relative difference between labour productivity and labour costs.

Human capital, which is a fundamental element of increased per-worker labour productivity, when considered individually, is also a significant determinant of foreign investment in the different regions.

In addition to productivity, a further indicator of competitiveness is the export potential of the manufacturing sectors, which clearly reveals itself to positively influence the entry of flows of foreign investment.

Of all of the macroeconomic factors utilized that measure the situation of Spain with respect to the EU average (SPGDP, FISC, SPLTIR, SPCPI and SPIPI), the two which, in general, have been statistically significant and take a negative sign in the definitive models estimated by W2SLS were, fiscal pressure (FISC) and the inflation differential (SPCPI).

Fairly predictably, the sectoral or regional determinants have performed better than those that work on the national Spanish level (the specific factors have been shown to be relatively more important than macroeconomic factors). This result is in line with those obtained by [START_REF] Giulietti | Foreign direct investment in UK: evidence from a disaggregated panel of UK food sector[END_REF].

In view of the results, it would seem evident that economic policy in Spain orientated towards attracting foreign direct investment, besides taking into account the traditional factors of demand and costs should focus on boosting all of those variables that favour the growth of labour productivity as is the case of investment in education, research, innovation and technology.

  . J. and P. Perron (1985) Testing the random walk hypothesis: power versus frequency of observation, Economics Letters, Vol. 18, 381-386. Spanish Ministry of Economy (Several years) Foreign investment series. Spanish Ministry of Economy (Several years) Foreign trade series. Spanish Ministry of Science and Technology (SMCT) (Several years) Prices series Stevens, G. y R. Lipsey (1992) Interactions between domestic and foreign investment, Journal of International Money and Finance, 11. Varela, F. y J. Rodríguez de Pablo (1974) Las inversiones extranjeras en España: 1959-1974. Una vía al desarrollo, Información Comercial Española,

TABLE 1

 1

	ABOUT HERE

  of different economic sectors or regions. The inclusion of a dummy variable (DU it = it ) in order to capture the heterogeneity across economic units and over time has, in general, functioned satisfactorily. In the case of the sectoral analysis, breaking the sample down into 28 sectors of activity for the Spanish economy, the model that has achieved the best results is as follows:

	LnSECFDI	it	DU	it	Ln	it SECPLP	it SECCOST
			LnSECFDI	it	FISC	t	LnDM	it	it
			F o r
					P
					e
							e r
							R e v
								i e
								w
								5 are not capable of
	adequately capturing heterogeneous or temporal performance or the performance

  line, therefore, that a model is offered which is similar to that provided in equations [4] and [5], but one which has been adapted for the Spanish scenario in which there are 17 quasi-autonomous regions:

	LnRFDI	it	DU	it	Ln	it RPLP	it RCOST	LnRFDI	it	FISC	t
			it LnRGDP		LnSPCPI	t	LnRHK	it	DUM	t	it
			F o r
				P
						e
						e r
									R e v
									i e
										w
												It is within this

Table 1 .

 1 The possible determinants of FDI

	TABLES
	Variable	Description	Unit/Source
		Factors of demand (Market size)
	SPGDP	Real Gross Domestic Product (GDP) in Spain Thousand of euros /Spanish
			Statistical Institute (SSI)
	SPEGDP GDP in Spain compared to the EU average	Percentage/ EUROSTAT
	RGDP	Real GDP of the Spanish regions	Thousands euros /SSI
	DM	The proportion of sectoral production that is	Percentage/SSI
		destined for the domestic market
		Factors of cost (profit)
	SPCOST	Mean cost per employee on average	Euros/SSI
		throughout Spain
	RCOST	Mean cost per employee on average in each	Euros/SSI
		region
	SECCOST Mean cost per employee on average	Euros/SSI
		according to sector
	SPPLP	Partial Labour Productivity on average in	Euros/SSI
		Spain
	RPLP	Partial Labour Productivity, on average,	Euros/SSI
		according to region
	SECPLP	Partial Labour Productivity according to	Euros/SSI
		sector
	SUBS	Production subventions, made available by	Millions euros /SSI
		the Spanish Public Administration
	FISC	Fiscal pressure (Direct and indirect	Percentage/ OECD
		taxes/GDP) in Spain compared to the EU
		average
	SPLTIR	Long term interest rate in Spain compared to	Percentages/ EUROSTAT
		the EU average
	SPCPI	Consumer price index in Spain compared to	Percentages/Spanish Ministry
		the EU average	of Science and Technology
			(SMCT)
	SPIPI	Index of industrial prices in Spain compared	Percentages/ SMCT
		to the EU average
	RHK	Human capital according to region
		(percentage of employees with upper-	Percentage/SSI
		secondary education level or over)
	SECHK	Human capital according to sector (percentage
		of employees with upper-secondary education	Percentage/SSI
		level or over)
	SPPK	Public capital in Spain (as a proxy of the level	Millions euros/SSI
		of infrastructures)
	TEC	Ratio of income, as measured on the Spanish	Percentage/SSI
		technological balance of payments, to GDP
		(as a proxy of technological capital)
		Previous FDI decisions

Table 2 .

 2 Estimates of the models 6, 7 and 8. Method: GLS (Cross Section Weights) Tabla 3. Estimates of the models 6, 7 and 8. Method: W2SLS

	Dependent Variable: Ln(?FDI)
	Independent	Model[ 6]	Model [7]	Model [8]
	Variable	(Total sectors)	(Manufacturing sectors)	(Regions)
	DU		1.094439 (10.59514)*	1.111707 (10.56652)*	1.122849 (7.330527)*
	Ln(?PLP-?COST)	0.105245 (2.107667)*	1.311781 (9.171453)*	0.543657 (2.216901)*
	Ln(?FDI(-1))	0.844293 (32.46423)*	0.327381 (5.030839)*	0.654079 (10.17201)*
	Ln(FISC)		-2.043291 (-2.053786)*	-1.096478 (-0.372633)	-3.481905 (-0.892265)
	Ln(DM)		0.941128 (1.339398)	4.723175 (2.960093)*
	Ln(SPCPI)			-1.559847 (-7.248110)*	-3.992940 (-3.845437)*
	Ln(SECEXP)		0.823760 (6.4821671)*
	Ln(RGDP)				0.564193 (5.290689)*
	Ln(RHK)				1.168325 (1.575153)
	DUM				0.467840 (1.987841)*
	Nº. of obser.	252	117	153
	Adjusted R	2	0.97	0.98	0.98
	D-W		2.262	1.945	2.072
	F-statistic		1900.7	914.1	869.1
	Prob.(F-stat.)	0.00	0.00	0.00
	Notes: 1. (?) Is substituted for (SEC) or (R) depending on whether the analysis refers to the
	sector or the region
	2. t-ratio appear in parenthesis
	3. (*) Indicate significance at the 5% level

[START_REF] Porter | Competition in global industries: A conceptual framework[END_REF] and[START_REF] Esteban | Crecimiento y convergencia regional en España y Europa[END_REF] underline the fact that neither production nor productivity depend solely upon the level of physical capital and work but also upon other variables, factors that are external to the firms, such as the level of infrastructure, human capital, and technology.

This generalization involves carrying out an extrapolation from the micro (or firm-level) to the macro (or countrylevel) and includes a number of determining factors which constitute a series of economic variables that might influence firms' costs and incomes and, in consequence, the decision to invest in the foreign country.

These techniques provide certain statistical and econometric advantages, advantages that are highlighted in the works of[START_REF] Hsiao | Analysis of Panel Data[END_REF],[START_REF] Dielman | Pooled cross-sectional and Time Series Data Analysis[END_REF],[START_REF] Baltagi | Econometric Analysis of Panel Data[END_REF],[START_REF] Matyas | The Econometrics of Panel Data: Handbook of Theory and Applications[END_REF] or[START_REF] Greene | Análisis Econométrico[END_REF] among others.

In this paper, the properties of the time series that were used were not studied because the number of annual observations was relatively small. AsShiller-Perron (1985) andDavison-Mackinnon (1993) point out, the power of the unit root test basically depends upon the chronological span of the data. In order to guarantee that the resultant regressions are not spurious, the relationships established by existing economic theory and other empirical analyses within the same field are taken into consideration.

The informatics system Econometric Views 5.0 has also been used in an attempt to include additional specific temporal effects. This option does not improve the results, rather, on combining the two types of effects (group and temporal), given the characteristics of the sample, tends to provoke situations in which there is perfect multicolinearity.

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

The Bartlett, Levene and Brown-Forsyhe tests indicated the presence of heteroskedasticity.

The result of the individual inclusion of SECPLP and SECCOST in the model was not significant.

To a certain extent, this indicates that, when foreign investment by companies holding foreign stocks is not included, as is the case of the manufacturing sectors, this variable ceases to be significant. This is because the main factor underlying the rise of this type of investment, which has an important relative weight as a proportion of total foreign investment, has been the fiscal incentives that Spain has offered with respect to the incomes obtained through foreign business investments (Tax Law of Companies, 43/1995).