PANEL DATA ANALYSIS

Introduction

Panel data sets are also known as longitudinal data or cross sectional time-series data. They have spatial (N) and temporal (T) dimensions. They constitute of a number of observations overtime on a number of cross sectional units such as individuals, firms, and countries allowing researchers to analyse the dynamics of change in short time series data. According to [START_REF] Baltes | History and rationale of longitudinal research[END_REF], longitudinal data and techniques involve "a variety of methods connected by the idea that the entity under investigation is observed repeatedly as it exists and evolves over time". These methods have been applied in different research disciplines. [START_REF] Frees | Longitudinal and Panel Data: Analysis and Applications in the Social Sciences[END_REF] posits that they have developed because important databases have become available to empirical researchers.

The term panel data was introduced by Lazarsfeld and Fiske (1938[START_REF] Frees | Longitudinal and Panel Data: Analysis and Applications in the Social Sciences[END_REF]) in their study of the effect of the relationship between radio advertising and product sales, where they proposed to interview a 'panel' of consumer overtime. Toon (2000[START_REF] Frees | Longitudinal and Panel Data: Analysis and Applications in the Social Sciences[END_REF]), acknowledges Engel's 1857 budget surveys as one earliest application of longitudinal data. In this survey Engel collected data on the expenditure pattern from the same set of subjects over a period time. The aim was to study expenditure on food as a function of income. Panel data modelling and estimation techniques developed in the second half of the twentieth century.

Early application of this technique are those [START_REF] Kuh | The Validity of Cross-Sectionally Estimated Behavior Equations in Time Series Applications[END_REF], [START_REF] Johnson | Econometric Methods[END_REF], Mundlak (1961) and [START_REF] Hoch | Estimation of Production Function Parameters Combining Time Series and Cross Section Data[END_REF] who used the fixed effect models (explained later in the text) and [START_REF] Balestra | Pooling cross section and time series data in the estimation of a dynamic model: The demand of natural gas[END_REF] and [START_REF] Wallace | The use of error components models in combining timeseries with cross-section data[END_REF] who used the random effect models (explained later in the text).

This chapter focuses on the application of panel data techniques in the tourism literature. The chapter is organised as follows: The next section explains panel data modelling technique emphasising on the difference between fixed and random effects. Dynamism in modelling is introduced and the implication for model estimation is discussed. The chapter then devotes a section to unit root and cointegration tests which is followed by an illustration of the application of panel data analysis in tourism research namely, in tourism demand modeling and in explorations of the relationship between tourism and economic growth. The conclusion spells out the limitations of this technique and directions for future research.

Panel Data Modelling Techniques and Benefits

Panel data analysis offers several advantages. The most obvious is that inferences are performed using a larger sample and the lack of degrees of freedom is fairly unlikely to occur.

According to [START_REF] Baltagi | Econometric Analysis of Panel Data[END_REF], more complex relationships can be modelled, for example temporal changes in cross-section can be analysed. One of the most important advantages however, is that panel data modelling allows for the control of heterogeneity in the sample.

A standard approach to model the relationship between Y (dependent variable) and X, a set of explanatory variables, is given below where εit is the stochastic error term which takes into account the variation in the expected value of Y which cannot be explained by the X's. yit = x'itβi + εit (1) For example in a tourism demand model, Y can stand for the number of arrivals to a particular destination while the X's include factors affecting demand, such as income in the home country, relative prices, marketing expenditure, transportation cost and so on. The X's or explanatory variables can be included in the model so long as they are observable and measureable. There are however, factors such as culture and other unique characteristics of the individuals or groups under study which are not observable or measurable but which influence the outcome of expected Y. These factors are referred to as the heterogeneity and are not directly part of Equation 1. The effect is incorporated in the one of the β's, should they be correlated with the respective X or otherwise included in εit. As a result, in the first case, the estimated β will not reveal the true effect of the variation in X on expected Y. By modeling the relationship between X and Y, using the panel data technique, the researcher is able to separate the effect of the heterogeneity from that of β.

Suppose K number of subjects are observed over time. The response subject of Y1t will tend to be similar to responses in previous years but different to the rest Y2t,Y3t…..Yk-1,t. That is, there is uniqueness in the behaviour of this subject and this is assumed to be constant over time. The uniqueness of Y1 can be attributed to factors such as culture, and past experience (for a person) business practices, (for a firm), history, or system of government (for a country). For example, the cultural and historical links that Australia and UK share may be expected to promote international tourism flows between the two countries. In this respect, tourism flows to Australia from UK are expected to be influenced by factors which are unique to travellers from this source and not pertinent for travellers from other home countries. In a panel data model, the uniqueness of each market in the sample is captured by μi which is the unobserved heterogeneity in the sample as given in Equation ( 2) below.

yit = μi + x'itβi + εit (2)
The additional benefit of including μi in the model is that it offers a potential solution to the problem of omitted variables and measurement errors in data. Lack of data is the most common problem faced by researchers. In regression analysis, it often results in the omission of relevant variables from the model. This may gives rise to potential model specification errors for example, due to difficulties in obtaining data on marketing expenditure, this variable is often omitted from tourism demand model, although a priori, it is expected to be relevant in explaining demand.

Consider Equation 3, where Yit is determined by three variables.

yit = β1x1it + β2x2it + β3x3it + εit (3)
omitting x3 from the model will reduce Equation 3 to Equation 4, where the effect of X3 is soaked by the εit. The actual model estimated is

yit = β1x1it + β2x2it + β3x3it + uit (4)
where Uit = x3t + εit

In this case the residuals include the effect of x3t and will display patterns leading to the conclusion that the model may be suffering from serial correlation [START_REF] Green | Econometric Analysis[END_REF]. The implications for the   's will depend on whether the Xit's are correlated with X3t. If so, the estimated coefficients will be biased. If on the other hand, the covariance between X3t and the Xit's is equal to zero,   's will be unbiased and consistent. According to [START_REF] Wooldridge | Econometric Analysis of Cross Section and Panel Data[END_REF], panel data modelling technique offers an effective solution to this problem. The inclusion of μi in Equation 4 will solve the problem as it will absorb the effect of X3t. This solution is also applicable when measurement errors are present in the data.

Taking the example of tourism model, [START_REF] Seetaram | Computing Airfare Elasticities or Opening Pandora's Box[END_REF] explains the complexities that arise when faced with the computation of airfare elasticities. Airfare data are often plagued with measurement errors which arise mainly because of the wide array of airfares and travel class categories which are prevalent in the market. This makes the task of the researcher complex as often no choice is left but to use an average airfare to represent the transportation cost to the destination. Average airfare is not always a good representation of actual airfare. Suppose that airfare, x3t is measured with errors such that the actual variable which is included in the model is

t t 3 * t 3 v x x  
.The model estimated is given by:

yt = β1x1 + β2x2 + β3x3t * + ηit (5)
where ηit = εit + vt.

In Equation 5, the stochastic error term is given by ηt. The cov (ηi,x3t * ) =β 2 v  where 2 v  is the variance of the measurement error [START_REF] Green | Econometric Analysis[END_REF]. This violates the crucial assumption of non-correlation between any explanatory variable and the residuals of the equation [START_REF] Green | Econometric Analysis[END_REF]. As a consequence, all the Ordinary Least Squares (OLS) estimators,   's will be biased and inconsistent and   3 will be attenuated. If instead the relationship between Y and the explanatory variables are modelled using the format of Equation 2, the measurement errors, vt, will be absorbed by the unobserved heterogeneity μi leaving εit free from its effect.

Fixed Effect and Random Effects

There are two ways of modeling μi, namely the fixed and random effect. The choice between these two depends on whether μi is correlated to any of the other explanatory variables of the model [START_REF] Wooldridge | Econometric Analysis of Cross Section and Panel Data[END_REF]. Equation 2 is formulated using the fixed effect (FE) technique.

This method assumes that the heterogeneity in the model is μi time invariant and specific to the individual group. In Equation 2, the slopes are fixed but the intercepts vary for each cross section. This is equivalent to adding a dummy specific for each cross section which is why it is also referred to as the Least Square Dummy Variable (LSDV) method. The slopes are treated as constant across group and across time. It is however, possible to allow the slope to vary across groups, across time or both (Hsaio, 2003). The rationale behind this modelling approach is that since μi accounts for time invariant characteristics of the group, it removes the pernicious effect of omitted variables [START_REF]Fixed Effects Regression Methods for Longitudinal Data Using SAS[END_REF]. Fixed effect is often chosen as a precaution against omitted variable bias. The drawback is that μi cannot be used to assess the effect of characteristics which changes overtime.

The FE technique explores the relationship between explanatory and dependant variables within one individual group. For each group, the variations of the all variables from their mean values are considered and the estimated coefficients are also known as the within estimates. This can be a limitation of the FE method as in-between variations are ignored.

Furthermore, only the effect of variable with sufficient variability can be analyses. For example, in a longitudinal study of individual's tourist's perception of the quality of destination attributes, the effect of gender and ethnicity cannot be analyzed. The modeler is expected to make a trade-off between sample variability and omitted variable bias [START_REF]Fixed Effects Regression Methods for Longitudinal Data Using SAS[END_REF]. However, as explained before the effects of these time-invariant factors are controlled in the FE model. In circumstances when the in between variation are not relevant, FE model makes use of maximum information, yielding errors terms with smaller variations [START_REF]Fixed Effects Regression Methods for Longitudinal Data Using SAS[END_REF]. FE models may additionally include an error component which changes over time but not for each unit, τt. τt is treated as a constant in the model.

yit = x'itβ + μi +τt + εit (6)
Taking the example of the tourist demand model, consider a sample which includes arrivals from 10 sources over a period of 10 years. The aim is the find the income and price elasticities of demand. Each market is a group in the sample. μuk will take into account all characteristics of UK other than price and income that will influence arrivals to Australia. UK as a market has certain characteristics which may or may not influence income and relative prices in the country. For example, μi can stand for system of government, democracy, which may or may not be related to income level in UK. However, in the instant that there exists such a relationship, then the inclusion of μi controls for the effect of democracy and allows the estimation of the net effect of income on number of arrivals from UK. The FE technique also assumes that μ is unique to UK and is not correlated to the characteristics of other countries in the sample. Any correlations between μuk and μusa are ignored.

If, however, μi's are correlated to one another and to the error terms of other groups, the resulting variance will be high making statistical inference dubious. A better approach in this case will be to use the random effect (RE) technique. In the RE approach, variation across entities is assumed to be random and uncorrelated with independent variables in the model.

The RE model is given as

yit = x'itβ + μit + τt + εit (7)
μit is referred to as the between-group error. The advantage of RE is that since variation across the sample are considered it permits the study of time invariant factors such as gender, ethnicity and race in the model. The RE method use variations both within and between individuals, random effects methods typically have less sampling variability than fixed effects methods [START_REF]Fixed Effects Regression Methods for Longitudinal Data Using SAS[END_REF]. The problem however, is that all relevant measureable variables need to be included in the model and since data on a few may not be available therefore leading to omitted variable bias in the model.

The choice between FE and FE depends on whether μi is correlated to any of the other explanatory variables of the model [START_REF] Wooldridge | Econometric Analysis of Cross Section and Panel Data[END_REF]. When such a correlation exists, the fixed effect technique is superior. Otherwise, the random effect is more parsimonious and gives more efficient estimates [START_REF] Wooldridge | Econometric Analysis of Cross Section and Panel Data[END_REF]. A formal test for assessing the correlation between the unobserved heterogeneity and other explanatory variable is the [START_REF] Hausman | Specification Tests in Econometrics[END_REF] specification test. In the tourism literature, FE method has been more frequently applied since the groups under observations are often markets, or destinations which have characteristics which influence the other explanatory variables of the model. The rest of the chapter will focus on the FE modelling method.

Dynamic Panel Data Models

By nature, all panel data models are dynamic since they are taking into account the time series dimension of the sample. However, functions which specifically model the effect of lagged dependant variables are referred to as dynamic panel data models. A general dynamic panel data model with FE effect is given as Equation 6 below.

yit = γiyi,t-1 + β0 + x'iktβk + μi + εit (8)

It is assumed that:

(a) μi ~ (N, σμ 2 ) and εit ~ (N, σε 2 ) where σμ 2 ≥ 0 and σε 2 > 0 (b)

The explanatory variables are strictly exogenous, that is they are not correlated with the error terms. i.e. E(εit εjs) = 0 for i ≠ j or t ≠ s.

(c)

The unobserved heterogeneity, if it is present, is random. i.e. E(μi μj) = 0 for i ≠ j

(d)
The unobserved heterogeneity is uncorrelated within the countries and with the error i.e. E(μi εjs) = 0 for  i, j, t, s (e)

The explanatory variables are strictly exogenous, that is they are not correlated with the error terms. i.e. E(xit εjs) = 0 for  i, j, t (f)

The unobserved heterogeneity are correlated with the predetermined variables i.e.

E(xit μj) = 0 for  i, j, t (g) γi0 is uncorrelated with the error term i.e. E(γi0 εjt) = 0 for  i, j, t (h) γi0 can be correlated with the unobserved heterogeneity. i.e. E (γi0 μj ) = unknown for

 i, j
Dynamic panel data analysis is becoming increasingly popular in the tourism literature modeling. In tourism demand model, this γi accounts for destination loyalty and repeat visitations. It takes into account to extent to which current visits are dependent on the number of past visits. It takes into account the effect of habit persistence in demand and the extent to which consumer react to ex-post information available. γi is an indication of the efficiency of information diffusion through word-of-mouth.

Generally, the functional form utilized for Equation 8 is that of double logarithm implying that the   's are the short term elasticities. The long term elasticities the long term elasticities *   may be obtained by

      1 * (9)
The most widely used estimation technique for dynamic panel data sets in the tourism literature has been has been the Arrelano [START_REF] Arellano | Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations[END_REF]. Examples of studie swhihc have applied this technique are Garín-Muños (2006), Khadaroo, andSeetanah (2007) and Eugenio-Martin et al. (2004).

Estimation Technique

Estimating   using standard LSDV method yields biased and inconsistent estimators.

LSDV 

estimator, also referred to as the covariance estimator, is given by: x and it y are the mean of xit and yit respectively. LSDV  will be biased and inconsistent unless T →  [START_REF] Nickell | Biases in Dynamic Models with Fixed Effects[END_REF][START_REF] Anderson | Estimation of Dynamic Models with Error Components[END_REF][START_REF] Arellano | Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations[END_REF][START_REF] Kiviet | On Bias, Inconsistency and Efficiency of Various Estimators in Dynamic Panel Data Models[END_REF][START_REF] Owen | Estimating Dynamic Panel Models: a Guide for Macroeconomist[END_REF]. This occurs because in Equation 8, yit-1 will be correlated with the mean of the stochastic error term models it  by construction and will be correlated to εit-1 which is contained in it  (Hsaio, 2003).

      1 N T N T ' ' LSDV it it it it it it it it i 0 t 1 i 0 t 1 ˆx x x x x x y y                          (
Anderson and Hsiao (AH) (1981) and Arellano and Bond (AB) (1991) show that the bias may be reduced by first differencing Equation 8 and using the lagged level values of the yit as instruments. Consider Equation 11 below which is similar to Equation 8 but for simplicity, the vector of exogenous variables x is left out.

y y it i 1 it it        (11) i = 1, 2....N, t= 1, 2....T
μi is the fixed effect which is the cause of the bias in the estimation by LSDV. To eliminate μi, Anderson and Hsiao (AH) (1981) suggest that first difference transformation be applied to Equation (11). First differencing Equation (11) gives the following: 12) is a first difference autoregressive process of order one with no exogenous regressors.

) ( ) y y ( ) y y ( 1 it it 2 it 1 it 1 it it             (12) i = 1, 2....N, t= 1,2....T Equation (
) y y ( y 2 it 1 it 1 it      
, is correlated with the error

) ( 1 it it    
. The second lag 2 it y  , and the first difference of this second lag,

) y y ( y 3 it 2 it 2 it      
, are possible instruments, since they are both correlated with

) y y ( 2 it 1 it   
but are uncorrelated with

) ( 1 it it    
, as long as the εit themselves are not serially correlated (Anderson and Hsiao,1981). Using the second lag and the first difference of this second lag as instrumental variables, two estimators

IV  and * IV

 can be developed. These are given in ( 13) and ( 14).

                     T 3 t 3 it 2 it 2 it 1 it N 1 i T 3 t 3 it 2 it 1 it it N 1 i IV ) y y )( y y ( ) y y )( y y ( ˆ (13)
N T it it 1 it 2 * i 1 t 2 IV N T it 1 it 2 it 2 i 1 t 2 (y y )y ˆ(y y )y                  (14)
These estimators are consistent when N   or T   or both. AB argue that more efficient estimators can be obtained by taking in additional instruments whose validity is based on orthogonality between lagged values of the dependent variable it y and the errors εit. These results are confirmed by [START_REF] Kiviet | On Bias, Inconsistency and Efficiency of Various Estimators in Dynamic Panel Data Models[END_REF] and [START_REF] Owen | Estimating Dynamic Panel Models: a Guide for Macroeconomist[END_REF]. However the bias persists in samples with small T [START_REF] Kiviet | On Bias, Inconsistency and Efficiency of Various Estimators in Dynamic Panel Data Models[END_REF][START_REF] Owen | Estimating Dynamic Panel Models: a Guide for Macroeconomist[END_REF]. In fact it increases with the value γ and decreases with T [START_REF] Kiviet | On Bias, Inconsistency and Efficiency of Various Estimators in Dynamic Panel Data Models[END_REF]. An estimator that relies on lags as instruments under the assumption of white noise errors will lose its consistency if the errors are serially correlated [START_REF] Kiviet | On Bias, Inconsistency and Efficiency of Various Estimators in Dynamic Panel Data Models[END_REF].

Using simulations to generate data, Judson and Owen (1999) performed an exercise with a panel of varying size. The number of cross sections, N, takes the values of 20 or 100 and the number of time periods, T, is given the values of 5, 10, 20 and 30. Judson and [START_REF] Owen | Estimating Dynamic Panel Models: a Guide for Macroeconomist[END_REF] show that although the value of the bias falls as T increases, it is nevertheless still considerable at T = 30 and can be as high as 20 percent of the true value of the parameter.

They prove that estimates resulting from the AB technique have higher variances than the LSDV estimators supporting earlier results of [START_REF] Kiviet | On Bias, Inconsistency and Efficiency of Various Estimators in Dynamic Panel Data Models[END_REF].

LSDV estimates are more efficient than any other class of estimates developed for autoregressive panel data models [START_REF] Kiviet | On Bias, Inconsistency and Efficiency of Various Estimators in Dynamic Panel Data Models[END_REF][START_REF] Owen | Estimating Dynamic Panel Models: a Guide for Macroeconomist[END_REF]. The removal of the bias in LSDV estimates opens the possibility of obtaining more robust estimates [START_REF] Kiviet | On Bias, Inconsistency and Efficiency of Various Estimators in Dynamic Panel Data Models[END_REF]. [START_REF] Kiviet | On Bias, Inconsistency and Efficiency of Various Estimators in Dynamic Panel Data Models[END_REF] evaluated the bias in the true parameters based on a Monte Carlo study and developed a method to correct for potential bias in the estimated parameters when the true parameters are known. On a practical level however, true parameters are seldom known, in which case [START_REF] Kiviet | On Bias, Inconsistency and Efficiency of Various Estimators in Dynamic Panel Data Models[END_REF] suggests that estimates be obtained, using the techniques proposed by AH (1981) and AB (1991). These estimates can then be corrected for the bias by applying the [START_REF] Kiviet | On Bias, Inconsistency and Efficiency of Various Estimators in Dynamic Panel Data Models[END_REF] Corrected LSDV (CLSDV) method. This method is only applicable to balanced samples-samples which contain the same number of observations for each cross section. In the tourism literature, authors such as Cortés-Jiménez ( 2008) in her study of regional tourism in Spain and Italy and Soukiazis and Proença (2008) examining regional tourism in Portugal have employed this technique.

Unit Root and Cointegration Tests

Classical statistical inferences rely on data being mean reverting. However, economic variables which tend to evolve over time are not always stationary and failure to account for this will result in spurious regression results. To circumvent such problems, unit roots are carried out to ascertain that regression results are valid. However, while testing for unit root is standard in the time series literature, it is quite recent in panel data [START_REF] Baltagi | Econometric Analysis of Panel Data[END_REF]. In the tourism literature although not very common, the availability of samples with fairly large time dimensions has resulted in more testing for unit root in the panel data set up, for example, [START_REF] Lee | Tourism Development and Economic Growth: A Closer Look at Panels[END_REF] in a study of tourism development and economic growth and Seetaram (2010a[START_REF] Seetaram | Estimating Demand Elasticities for Australia's International Outbound Tourism[END_REF] in the context of Australian outbound tourism.

In the panel data setup, panel unit roots tests have higher power than unit root tests based on individual time series for each of the cross section, since the later performs poorly when data periods are short [START_REF] Baltagi | Econometric Analysis of Panel Data[END_REF][START_REF] Banerjee | Panel Data Unit Roots and Cointegration: An Overview[END_REF][START_REF] Banerjee | Some Cautions on the Use of Panel Methods for Integrated Series of Macroeconomic Data[END_REF][START_REF] Levin | Unit Root Tests in Panel Data: Asymptotic and Finite-Sample Properties[END_REF]Im, Persaran and[START_REF] Im | Testing for Unit Roots in Heterogeneous Panels[END_REF][START_REF] Pedroni | Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors[END_REF]. Several tests for unit roots in panel data have been developed. A few examples are [START_REF] Maddala | A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test[END_REF], [START_REF] Breitung | The Local Power of Some Unit Root Tests for Panel Data[END_REF] and [START_REF] Choi | Unit Root Tests for Panel Data[END_REF]. According to [START_REF] Baltagi | Econometric Analysis of Panel Data[END_REF] however, the two most efficient tests for stationarity in a panel data setting are Levin, Lin and Chu (hereafter LLC, 2002) and Im, Pesaran and Shin (hereafter IPS, 2003). The fundamental difference between these two tests rests on the assumption made regarding the autoregressive process [START_REF] Baltagi | Econometric Analysis of Panel Data[END_REF]:

1. LLC assumes that the autoregressive process is common for all cross sections, that is ρ = ρi in Equation 15give below.

2. IPS assumes that the persistence parameter, ρi is allowed to vary across the cross sections.

Both tests are based on estimating the following equation:

i z i y y y it it it 1 ij it j j1             (15)
yit is the dependent variable being tested for unit root. Δ denotes the first difference in the dependent variable 𝐲 𝐢𝐭 . zi is the number of lags to be included in the testing. εit are the error terms. ρi and φij are parameters.

LLC assumes that the error term εit is independent across the units of the sample and have a fixed variance. LLC tests the hypothesis that each of the series in the panel contains a unit root against the hypothesis that all individual series are stationary. This can be written as: H0 : ρ =ρi = 0 for all i ~ All of the individuals have a unit root.

H1 : ρ = ρi < 0 for all i ~ All the individual series are stationary.

The IPS test assumes that the panel is balanced. It hypothesises that each of the series contains a unit root against the alternative hypothesis, that at least one of the series is stationary. These can be represented as follows:

H0: ρi = 0 for all i. ~ All the series have unit roots.

H1:

i 0 for i = 1,2,…N1

0 for i = N 1, N 2,...N        
~ Some of the series have unit roots are the cross sections in the panel data set. This method involves regressing the individual series for each of the cross sections. The critical value for the hypotheses is obtained by taking the average of the student t-statistics for the ρi from the individual regressions. This is given by:

N iT i it NT t ( ) t N           (16)
Series containing unit roots are non-stationary processes which have timevarying mean and variance that increases as sample size grows [START_REF] Baltagi | Econometric Analysis of Panel Data[END_REF].

When variables are individually integrated of the same order such as the ones in this study), a linear combination of these variables can still be stationary [START_REF] Baltagi | Econometric Analysis of Panel Data[END_REF][START_REF] Banerjee | Panel Data Unit Roots and Cointegration: An Overview[END_REF][START_REF] Banerjee | Some Cautions on the Use of Panel Methods for Integrated Series of Macroeconomic Data[END_REF][START_REF] Pedroni | Panel Cointegration; Asymptotic and Finite Sample Properties of Pooled Time Series Tests with an Application to the PPP Hypothesis[END_REF]. If the series are found to be cointegrated then is at least one cointegrating vector which renders the combination of variables stationary. Furthermore, it implies that there is long run relationship among the variables.

The conventional cointegration tests suffer from low power when applied to samples with small time dimension, such as the one used in this chapter [START_REF] Baltagi | Econometric Analysis of Panel Data[END_REF][START_REF] Banerjee | Panel Data Unit Roots and Cointegration: An Overview[END_REF][START_REF] Banerjee | Some Cautions on the Use of Panel Methods for Integrated Series of Macroeconomic Data[END_REF][START_REF] Pedroni | Panel Cointegration; Asymptotic and Finite Sample Properties of Pooled Time Series Tests with an Application to the PPP Hypothesis[END_REF]. Panel cointegrating techniques have been developed to allow researchers to pool information regarding common long run relationships from across the panel. In addition, such techniques allow the associated short run dynamic and fixed effects to be heterogeneous across the different members of the panel [START_REF] Baltagi | Econometric Analysis of Panel Data[END_REF][START_REF] Banerjee | Panel Data Unit Roots and Cointegration: An Overview[END_REF][START_REF] Banerjee | Some Cautions on the Use of Panel Methods for Integrated Series of Macroeconomic Data[END_REF][START_REF] Pedroni | Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors[END_REF]2004). This chapter focuses on the [START_REF] Pedroni | Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors[END_REF] test as it is one of the most widely used test for cointegration in panel data.

The first step in this test is to estimate a cointegrating relationship with fixed effects and heterogeneous time trends for each of the cross section of the study individually. Then the cointegration tests are performed based on the residuals obtained [START_REF] Banerjee | Panel Data Unit Roots and Cointegration: An Overview[END_REF][START_REF] Banerjee | Some Cautions on the Use of Panel Methods for Integrated Series of Macroeconomic Data[END_REF][START_REF] Pedroni | Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors[END_REF]2004). [START_REF] Pedroni | Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors[END_REF] proposes seven tests for cointegration in the panel data framework. Four of these tests are referred to as the 'panel cointegrating statistics' or the within-dimension based statistics (Pedroni, 1999, pp. 658). In these tests, he assumes that there is a common cointegrating relationship among the variables. For these four tests, the residuals are pooled across the time dimension of the panel. An autoregressive function is regressed and the autoregressive coefficient is given by υi. This method assumes that υi = υ, that is, it is common for all countries. The alternate hypothesis is that there is a cointegrating relationship for all the cross sections given as [START_REF] Pedroni | Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors[END_REF]: [START_REF] Pedroni | Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors[END_REF]. In these tests, estimators average the individually estimated autoregressive coefficient for each cross section [START_REF] Pedroni | Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors[END_REF]. The hypotheses here are given by: The group mean statistics can be considered as more accurate, as they allow for more heterogeneity among the countries, and produce consistent estimates (Pedroni, 2001). The higher value of the group mean statistics can be considered to be a more accurate representation of the average long run relationship (Pedroni, 2001).

It can be noted however, that in general the unit root cointegration tests increase the probability of determining if data are stationary or not and whether they are cointegrated [START_REF] Banerjee | Panel Data Unit Roots and Cointegration: An Overview[END_REF][START_REF] Banerjee | Some Cautions on the Use of Panel Methods for Integrated Series of Macroeconomic Data[END_REF]). However, one limitation of these tests is that they assume no cross sectional correlation in the sample [START_REF] Banerjee | Panel Data Unit Roots and Cointegration: An Overview[END_REF][START_REF] Banerjee | Some Cautions on the Use of Panel Methods for Integrated Series of Macroeconomic Data[END_REF]. [START_REF] Banerjee | Some Cautions on the Use of Panel Methods for Integrated Series of Macroeconomic Data[END_REF] showed that the results of cointegration tests were susceptible to dependence among the cross sections. This means that the power of the tests is reduced in cases where the cross sections are not independent. In spite of this, in panel data sets, the problem of spurious regression results is not expected to be as serious as in pure time series.

As demonstrated by Phillips and Moon (1999), noise in time series regression is lessened by pooling cross section and time series observations, implying that the model may be estimated in level form without risking spurious results.

Applications of the panel data techniques in tourism data analysis

The empirical literature based on the panel data methods and applied to the tourism sector, can be divided into two broad areas. The first concerns the determinants of tourism demand.

This topic of demand modelling is not recent and goes back [START_REF] Rugg | The Choice of Journey Destination: A Theoretical and Empirical Analysis[END_REF]. The study of international tourism arrivals and receipts is a vibrant area of research and it cannot be indicate a real and significant effect from the quality to the price. [START_REF] Eilat | Determinants of International Tourism: A Three Dimensional Panel Data Analysis[END_REF] used multinomial logit estimations, based on bilateral data during the period 1985-1998, to study the leisure tourism determinants. This was the first study based on a three dimensional panel data (year, destination, origin). They showed that political risk is a very important determinant of the tourist arrivals. The most recent paper based on the traditional panel data method is that of [START_REF] Arita | Impact of Approved Destination Status on Chinese travel abroad: an econometric analysis[END_REF]. They analyzed the impact of ADS (Approved Destination Statues, beginning of the 1990s) agreements on the international tourism arrivals in China. They integrated, in the estimation, fixed effects, to compare results before and after ADS and also between destinations inside and outside the ADS.

As the relevant data available is often annual, there are many empirical works which use Transport infrastructures, was approximated by the net investment in land, air, and sea infrastructure at the current market price while non-transport infrastructures were measured by net investment at current market price on communication, energy, wastewater, and defence infrastructure. Transport infrastructure was found to be an important determinant of demand for travellers from Asia, Europe, and the United States while the latter two groups are also influenced by the non-transport infrastructure. They however use a poor proxy for prices in their model. They sought to capture the price effect by using the real value of the Mauritian rupees in U.S. dollars. The depreciation or appreciation of the Mauritian rupee against the U.S. dollar cannot be expected to reflect changes in the cost for holidays for visitors from Africa, Asia, and Europe. It is not surprising that they found prices to be insignificant in determining international arrivals to Mauritius.

Naude and [START_REF] Saayman | Determinants of Tourist Arrivals in Africa: A Panel data Regression Analysis[END_REF] analyzed annual data from 1996 to 2000 to estimate tourism arrivals for 43 African countries. Their estimation yielded a negative coefficient for the lagged dependent variable suggesting that tourism in South African is taking a downward trend as currently level of arrivals were negatively related to past levels. Political stability was a key determinant of arrivals. Their model included lagged values of explanatory variables. It is not unlikely that the variables are highly collinear to their lagged values and therefore the model suffers from multicollinearity. This may explain why only a few of their estimated coefficients were statistically significant.

A dynamic data panel model has the fundamental characteristics to establish the long-run relationship between two variables. As the question of difference between short and long-run estimation is important (the results can differ as a short-run estimation can be amplified or cancelled in the long-run), the dynamic model is often used along with another method of estimation which has the objective to estimate the short-run impact. For example, [START_REF] Kuo | Assessing Impacts of SARS and Avian Flu on International Tourism Demand to Asia[END_REF] and [START_REF] Kuo | Estimating the Impact of Avian Flu on International Tourism Demand Using Panel Data[END_REF] tried to estimate impact of infectious diseases (Avion Flu) and severe acute respiratory syndrome (SARS) on international tourist arrivals in selected Asian, African and European destinations, for the sample period of 2001 to 2006 and 2004 to 2006 respectively. The long-run estimation is based on the GMM procedure and the short-run estimation is provided by ARMAX models for each country. They established different results between short and long-run: SARS had a significant impact on tourist arrivals at long and short-run whereas infectious diseases had only an impact at the long-run. Garin-Munos (2006) used annual data from 1992 to 2002 to estimate factors affecting arrivals to Canary Islands from 15 of its markets. Demand was found to be inelastic in the short run but income and price elasticity was greater than one in the long run. Demand was elastic with respect to changes in transport cost in the short run and in the long run.

Another way of comparing short and long-run results is to employ cointegration techniques and dynamic model. Seetaram (2010a) and [START_REF] Seetaram | Estimating Demand Elasticities for Australia's International Outbound Tourism[END_REF] analyzed tourist arrivals and departures in Australia during the period 1991-2008 respectively. After the testing for stationarity and cointegration (based on the suggestion of [START_REF] Pedroni | Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors[END_REF]2004), she employed GMM and CLSDV (provided by [START_REF] Kiviet | On Bias, Inconsistency and Efficiency of Various Estimators in Dynamic Panel Data Models[END_REF], to calculate short-run and long-run elasticities. The justification for using this estimation technique was that the temporal dimension was relatively small. For the tourist arrivals, she found that demand is inelastic in the short-run with respects to its determinants and elastic in the long-run. Concerning the tourism outbound, their results indicated an elasticity of migration on tourism outbound of 0.2% and amplified in the long-run with a value of 0.6%. However, contrary to the inbound model, there were no effects detected from the price index in the outbound model.

Finally, another way is to use directly an estimator for the cointegrated model: Fully

Modified Ordinary Least Squares (FMOLS) or Dynamic Ordinary Least Squares (DOLS).

Seetanah et al. ( 2010) employed a gravity model to test price and income elasticities.

However, this paper suffers from a problem of methodology as they integrate many exogeneous variables into the regression. For a set of n variables, there can be up to n-1 independent cointegrating vectors [START_REF] Harris | Using Cointegration Analysis in Econometric Modelling[END_REF]. If explanatory variables were cointegrated among them, there would more than n-1 cointegrating vectors. So it is not possible to put explanatory variables that are cointegrated between them simultaneously in the regression (FMOLS or DOLS).

Regarding tourism and economic growth, the lack of theoretical foundations in explaining the mechanism through which tourism causes economic growth has prompted several authors in applying cointegration techniques in order to explain the causality between the two variables.

This has resulting in a small but growing number of empirical studies using dynamic panel data techniques. The first studies have analysed the effect or a growing tourism sector on the economic growth of the destination (tourism growth  economic growth). Eugenio-Martin et al. ( 2004) estimated the effect from growth of tourism per capita to income per capita with a sample including 21 Latin American countries from 1985 to 1998. They used a dynamic panel data model with AB estimator and they categorised the countries into three groups based on their respective income per capita: rich, medium and poor. The results established that tourism led to economic growth for medium and poor countries while the reverse was true for rich countries.

Cortés-Jiménez ( 2008) analyzed the impact of international and domestic tourism arrivals on economic growth (by using the GDP per capita) for 17 regions of Spain and 20 regions of Italy during the period 1990-2000. She used two estimators for the dynamic panel model: GMM and CLSDV. She found that tourism impacted positively to economic growth in for costal and Mediterranean regions. Her results are however, surprising for the inland regions in her sample. Here she found that while domestic tourism fostered economic growth, international tourism had the reverse effect. [START_REF] Soukiazis | Tourism as an Alternative Source of Regional Growth in Portugal: A Panel Data Analysis at NUTS II and III Levels[END_REF] studied the effect of tourism activity on the economic convergence of Portuguese regions between 1993 and 2001. Convergence was approximated by the difference of GDP per capita. Three estimators were applied: GMM, CLSDV and GLS (with random effects). The authors used bed capacity as proxy for tourism activity.

According to their results, a tourism capacity expansion of 1% conducts to a supplement of 0.01% on economic growth. Their results indicated also that tourism accelerates economic convergence across the regions. Without tourism, the convergence was estimated to take 11 years whereas when the effect of tourism is taken into account, this reduces to 10 years only.

Sequeira and Maçãs Nunes ( 2008) used a sample of 94 countries (poor countries or small countries, with a population less than 5 millions) for the period 1980-2002. Three indicators of the tourism activity were employed (tourism arrival in population proportion, tourism receipts as a percentage of exports and tourism receipts as a percentage of GDP). They found that tourism had a positive effect on economic growth but only for the poor countries.

In the tourism literature, the panel data cointegration technique has also been employed in order to test for the causality between tourism growth and economic development. [START_REF] Narayan | Tourism and Economic Growth: A Panel Data Analysis for Pacific Island Countries[END_REF] used cointegration tests to estimate long-run elasticities of tourism to Pacific Islands (Tonga, Fiji, Solomon, Papua New-Guinea) for the period extending from 1988 to 2004. They then analyzed the causality from tourism exports to GDP with an ECM model (as its structure permits to check at the short-run and at the long-run). The tourism literature is not clear on the direction of the causality between economic growth and tourism. There are schools of thoughts which suggest that economic growth and development can also impact on tourism demand (economic growth  tourism).

A few studies have empirically tested this link by using cointegration methods for data panel model. For example, [START_REF] Lee | Tourism Development and Economic Growth: A Closer Look at Panels[END_REF]

Limitations of panel data analysis

The first disadvantage of this technique is that it is fairly complex to estimate and data requirements are high. Observing a number of individuals over a period of time usually results in data collection that can be tedious and expensive [START_REF] Baltagi | Econometric Analysis of Panel Data[END_REF]. From a statistical perspective, panel survey designs have some inherent disadvantages as noted in Kitamura (2000, pp 127).

1. Respondents may find it cumbersome to regularly participate in the same survey, which results in increasing non-response.

2. Attrition or dropout rate from the sample can be high.

3. Overtime the accuracy of data collection may decline. This is known as 'panel fatigue'.

4. The response of individuals may be influence by their responses from previous participations.

These disadvantages can however be addressed although solutions do come at a cost.

Solution proposed, include 'refreshing' the survey design and adding fresh participants at later stages. For more in-depth analysis of attrition in panel data see [START_REF] Alderman | Attrition in Longitudinal Household Survey Data: Some Tests from Three Developing Countries[END_REF], [START_REF] Fitzgerald | An Analysis of Sample Attrition in Panel Data: The Michigan Panel Study of Income Dynamics[END_REF] and [START_REF] Uhrig | The Nature and Causes of Attrition in the British Household Panel Study[END_REF].

Conclusion and Future Directions

The use of panel data modelling techniques is becoming more common in the analysis of tourism data. In less than a decade, the literature has progressed from the development of standard static fixed effect and random effect models in the early 2000's to the more sophisticated dynamic panel data cointegration models. So far, the exploitation of this technique has been restricted to tourism demand studies and others which have explored the relationship between the expansion of tourism sectors and economic growth or international trade. It can be extended to other topics of research within the tourism context.

One interesting area is the investigation of the supply side of the tourism industry. For example, a scrutiny of regional differences in the productivity of this industry will benefit from panel data modelling techniques, as models developed may be used to control for the heterogeneity of each region. Other types of studies where this method can prove to be useful are examinations of labour markets and their outcomes across different districts within a destination or in international comparisons of destinations. Examples of problems that can be addressed are analysis of wage differentials and gender bias or human capital formation and return to education within the tourism industry.

While panel data techniques are powerful, and generally yield reliable estimates, they nevertheless suffer from some weaknesses. Estimations can be complex and data requirements are fairly large especially for dynamic panel models. The main limitations however, is that the panel data survey design is inevitably subject to problems related to attrition. The later drawback however, does not seem to have affected tourism research as the existing studies are based on secondary data or 'macro' data where the occurrence is either easier to deal with or less frequent. It is expected however that future research may be based on survey data when researchers seek to find answers to the behaviour of tourists using micro level data.

Regarding unit roots and cointegration tests, it is noted that the tests applied can be restrictive in that they assume that there is no cross sectional correlations in the sample. In the instances that this assumption is overly binding and inappropriate, researchers may circumvent it by using alternative tests such as those of [START_REF] Westerlund | Testing for Error Correction in Panel Data[END_REF] who has designed tests which perform well in smaller samples and have higher power. Additionally, [START_REF] Westerlund | Testing for Panel Cointegration with Multiple Structural Breaks[END_REF] and [START_REF] Westerlund | A Simple Test for Cointegration in Dependent Panels with Structural Breaks[END_REF] have developed cointegration tests for panel data sets with structural breaks. These tests will be useful for researchers who are attempting to examine whether the effect of shocks on the tourism data are permanent or transitory. The exploitation of these tests will be made easier when software such as STATA which is the dominant one for estimating panel data models catch up with theoretical development in this field and incorporate them in the newer version of the programme. Finally it is expected that in the future, the literature will see more use of panel-ECM and that application of panel data techniques for analysing tourism data will extend to non-linear models including binary models.

  and 𝛃 ̂𝐋𝐒𝐃𝐕 is the estimated true coefficient of the exogenous variable xit. it

  individuals are cointegrated By contrast, the remaining three tests are called the 'group mean cointegrating statistics' or the between-dimension. These tests statistics are based on pooling the residuals of the regression along the cross sections of the panel

  confined to gravity models due to factors such as seasonality, cultural links or time constraints which are particular to the consumption of tourism products. The second area consists mainly of analysis of the link between tourism and economic growth. Since, several destinations for example, Spain, Italy and Portugal have simultaneously experienced economic development and expansion of their tourism sector, especially following the adoption of the Millennium goals for the economic development, many authors have attempted to estimate the real impact of international tourism on economic growth.In tourism demand modelling, after a short period of application of standard and classic panel data models (OLS with fixed effects or random effects), the literature had been characterized by a high volume of dynamic panel models. Additionally, from the second half of 2000 onwards, several of the works employ cointegration techniques. The first empirical papers, based on the classic panel data methods, were published in the beginning of the 2000s. These studies focused generally on the prices elasticities or on the impact of political risk and violence on international tourism arrivals. For example, Ledesma-Rodriguez and Navarro-Ibanez (2001) used annual data from 1979 to 1997 to study factors affecting arrivals to Tenerife from 13 markets. They found arrivals to be elastic with respect to income and inelastic with respect to prices and transport cost in the long run.[START_REF] Espinet | Effect on Prices of the Attributes of Holiday Hotels: A Hedonic Prices Approach[END_REF] used panel data with random effects for a hedonic evaluation based on 86,000 prices between 1991 and 1998. The data concerned hotels in the southern Costa Brava region. Their results

  dynamic panel data methods to test long-run relationships. The most commonly used estimation technique in the literature is that of AB although authors such as Maloney and Montes Ronjas (2005) made used of General Method of Moment (GMM) suggested by[START_REF] Blundell | Initial Conditions and Moment Restrictions in Dynamic Panel Data Models[END_REF]. They measured the tourism price elasticity in Caribbean countries, with bilateral data (tourists came from United-States, Canada, United-Kingdom, Germany, Netherlands, Italy and Spain) from 1990 to 2002. They estimated a large price elasticity of 4.9.As mentioned above, the AB technique is the most widely used in the tourism literature. For example,Neumayer (2004) estimated the effect of violence, risks, freedom and human rights violations on annual international tourism arrivals during 1977-2000. This study was based on a sample which contained more than 100 countries. Neumayer employed two methods for this estimation: a traditional data panel model with fixed effects and a dynamic panel data model. He found that in most cases, these explanatory variables had a real and significant effect on tourism. Garin-Munos and Montero-Martin (2007) studied yearly data from 1991 to 2003 to assess factors affecting the number of arrivals to the Balearic Islands. They found a high level of consumer loyalty to tourism in the Balearic Islands and recommended that suppliers of tourism products should raise the quality of their products and should improve their brand image. Khadaroo and Seetanah (2007) used data on arrivals to Mauritius during the period 1978 to 2003 to assess the relative importance of transport infrastructure as a demand determinant.

  used panel cointegration methods to determine the relationship and the causality between economic growth and tourism development in 55 countries (developed and developing countries) covering the time span of 1990 to 2002. After performing the IPS unit root and Pedroni (1999) cointegration tests, they applied FMOLS estimator to estimate the long-run relationship between tourism and economic developments.They then performed causality tests, the results of which indicated a unidirectional causality for developed countries (tourism  economic growth) whereas in developing countries, there were bidirectional causality relationships.

	It is interesting to note that tourism has not only benefited economic growth but also
	international trade. Santana-Gallego et al. (2011) employed FMOLS, DOLS while Keum
	(2011) applied multiple dynamic causal patch analysis to investigate the causality between
	tourism and trade. Cassette et al. (2009) and Petit (2010) developed panel data cointegration
	models to analyze whether international trade of goods and services had any effect on income
	inequalities. Their results indicate that international trade in tourism services results in
	income inequalities disadvantaging low-skilled workers. Whilst tourism activities seem to
	benefit economic growth, the low-skilled workers seem to be losing from globalization of the
	tourism services.


Maryam Thirriard

Harold Nicolson

New Biographer

Nigel Nicolson

Harold Nicolson's name is more often associated with the history of diplomacy than with the history of life-writing, though he happens to be one of the main biographers who participated in the development of the modernistic form we have come to call "New Biography." The term was coined by Virginia Woolf in 1927, when

speaks of a 'new school of biographies,'2 to which Harold Nicolson is said to belong alongside Lytton Strachey, a precursor of New Biography, author of Eminent Victorians, Queen Victoria, Elizabeth and Essex, and also André Maurois, author of the lives of Shelley, Disraeli, Byron, Edward VII, and of the Clark lecture entitled Aspects of Biography. Virginia Woolf herself contributed to this school with Orlando (1928), a fictionalized biography of her lover of the time, who was none other than Vita Sackville-West, Nicolson's wife; with Flush (1933), the life of Elizabeth Barrett Browning's cocker spaniel; as well as with the biography of her Bloomsbury friend, Roger Fry (1940).

However, Nicolson's debut in biography can be traced to 1919 when he was sent to Paris by the Foreign Office as part of the British delegation at the Peace Conferences. It is during this period that he started writing his first biography, Paul Verlaine. 3 The emotional turmoil of that year may very well have provided the impulse which laid the foundation of his literary career. His wife, and mother of both his sons, had eloped with the poetess Violet Keppel, with whom she was living in the South of France. Furthermore, it was in 1919 that Nicolson was introduced to the intellectual and artistic circles in Paris. Through his friend the painter Comte Jean de Gaigneron he became acquainted with André Gide, Paul Adam, Jean Cocteau, and he even met the great Proust himself. Nicolson offers a subtle, ironic portrait of him in his diary, inspired by their encounter at the Ritz Hotel, which Nicolson writes with finesse, revealing his literary potential.

March 2, 1919 Paris Dine at the Ritz-a swell [Marcel] Proust is there. He is white, unshaven, grubby, slip-faced. He puts his fur coat on afterwards and sits hunched there in white kid-gloves. Two cups of black coffee he has, with chunks of sugar. Yet, in his talk, there is no affection. He asks me questions. Will I please tell him how the Committees work? I say, 'Well, we generally meet at 10. There are secretaries behind…' 'Mais non, mais non, vous allez trop vite. Recommencez. Vous prenez la voiture de la délégation. Vous descendez au Quai d'Orsay. Vous montez l'escalier. Vous entrez dans la salle… Et alors? Précisez, mon cher, précisez.' So I tell him everything. The sham cordiality of it all; the hand-shakes; the maps; the rustle of papers; the tea in the next room; the macaroons. He listens enthralled, interrupting from time to time-'Mais précisez, mon cher monsieur, n'allez pas trop vite.' 4 Nicolson excelled in recreating atmosphere; it is because Nicolson is surprised by there being no affectation in Proust's manner of talking that we understand how odd his dress and idiosyncrasies are-he puts on his fur coat after sitting down. This, however, does not diminish his aura in any way-Nicolson's admiration for Proust was boundless at the time. Proust is at first glance the centre of attention: he is the only one depicted; he is the one who is quoted in direct speech and leads the conversation. However, it is distinctly Proust's questions that Nicolson relates. In this way, Proust becomes a lens. First the solemnity of the official picture of the Peace Conferences is conveyed; but, thanks to Proust's insistence, we gain access to the backstage: the tea and macaroons, hand-shakes and chitchatting, 'the sham cordiality of it all' (while whole populations nervously await the fate of their countries and frontiers). By the end of the entry we realize that Proust is not the focus of the picture, he is merely a mirror in which Nicolson's own image appears. And we discover Nicolson's esteem for Proust and love for the French language-not to be translated-his acute observation of those minute details that have so much to communicate, as well his piquant subversive sense of humour. Nicolson is a bon vivant. He is sociable, snobbish and carefree, as he shamelessly relates to Proust, the outsider in all this.

The passage brings to light a central aspect of Nicolson's life-writing which also happens to be a chief feature of New Biography: the way the focus constantly shifts from the subject to the puppeteering biographer. Virginia Woolf was able to pinpoint it precisely in her essay 'The New Biography.' What she saw in Nicolson's writing sums up the essence of New Biography:

So it would seem as if one of the great advantages of the new school to which Mr Nicolson belongs is the lack of pose, humbug, solemnity. […] [T]hey maintain that the man himself, the pith, and essence of his character, shows itself to the observant eye in the tone of a voice, the turn of a head, some little phrase or anecdote picked up in passing. Thus in two subtle phrases, in one passage of brilliant description, whole chapters of the Victorian volume are synthesized and summed up. Some People is full of examples of this new phase of the biographer's art. Mr. Nicolson wants to describe a governess and he tells us she has a drop at the end of her nose and made him salute the quarterdeck. He wants to describe Lord Curzon [to whom Nicolson had been secretary] and he makes him lose his trousers and recite 'Tears, Idle Tears.' He does not cumber himself with a single fact about them. He waits till they have said or done something characteristic, and then he pounces on it with glee. But […] [h]e lies in wait for his own absurdities as artfully as for theirs. Indeed, by the end of the book we realize that the figure which has been most completely and most subtly displayed is that of the author. Each of the supposed subjects holds up in his or her small bright diminishing mirror a different reflection of Harold Nicolson. 5 This mirroring effect is itself akin to the technique Nicolson uses in his earlier sketch of Proust. It relates to a general Modernist apprehension that reality cannot be grasped directly but only through subjective experience. This also concerns the self, and our self, the perception of which can only be indirect.

In Self Impression, Max Saunders offers an extended study of Modernist experiments with life-writing. One particular practice he identifies is composite portraiture, in which the portraits of several people are collapsed into one subject. We can see this clearly with Nicolson's 'mirroring effect,' for instance in his biography of Paul Verlaine. The writing of this book coincided with a difficult time in his private life. His marriage came close to collapsing. When still in England, Harold had noted the frequent presence of Violet Keppel at their cottage: 'Violet there,' his journal mentions simply; and 'Viti [is] seedy.' 6 He noticed that Vita had 'rather got wanderlust.' 7 Harold was right to be wary. Violet Keppel was Vita's intimate friend and lover; she was a poetess, as was Vita. His wife's passion for Violet and her 'wanderlust' combined and explain the reason why she suddenly decided to elope with Violet, instead of joining Harold in Paris. As noted, the literary connections Nicolson was to make in France, combined with his personal story, provide some understanding of the reasons that brought him to compose this biography of the scandalous, decadent, French poet. Was his turning to literature an attempt to regain the attention of the wife and author who had abandoned him? There's a strong moralising tone in Nicolson's biography of Verlaine and Nicolson's depiction of the way Rimbaud brought Verlaine's life down can be read as an open letter to Vita, warning her of the dangers of giving her heart to someone as reckless as the poetess Violet was. Nicolson gives heed to Rimbaud's 'La Vierge folle,' which makes us wonder whether Verlaine's ménage à trois reflects Nicolson's own. Nicolson quotes Rimbaud's closing lines, amplifying the poem's contextual ambiguities.

Je lui suis soumise.-Ah! je suis folle!... Drôle de ménage! 8

Who is the 'foolish virgin'? It's Paul Verlaine, under Rimbaud's pen, but it can also allude to Harold's status. Or is it Vita? Totally subdued to Violet, that 'poisonous orchid,' in Nicolson's terms, 'glimmering and stinking in the recesses of life, and throwing cadaverous sweetness on the morning's breeze. Darling she is evil,' Nicolson once wrote to Vita. 9 When Verlaine abandons his wife Mathilde, Nicolson notes: 'she would get over it; she would come back to him afterwards.' 10 According to Nigel Nicolson's A Portrait of a Marriage, likewise, 'Vita never knew how much [Harold] minded, and guessed, correctly, that no matter what she did, she would always be forgiven.' 11 Vita had 'wanderlust,' and so did the juvenile 'Ange et démon poète,' Rimbaud. Harold writes of him that 'the mania of "Wanderlust" had seized upon his senses' and he goes on to quote Rimbaud's poem, 'Les Poètes de sept ans': 'Du grand désert où luit la Libérie ravie, / Forêts, soleils, rives, savanes!' 12 As in a Francis Galton composite photography, a dazzling, interwoven portrait is assembled in each of Nicolson's subjects, containing, also superimposed, the traits of the various protagonists in Nicolson's own life, without excluding himself.

Some People is also to be valued as a new experimental trend within the genre of portrait-collections, in relation to Nicolson's own position as the explicit biographer in his own text. Saunders calls the texts in which a biographer and his work are at the heart of the narrative 'biografiction,' though he notes that this particular form was not yet common among the modernists, coming into its own as a postmodernist practice. Nicolson's text then belongs to that small collection, alongside Woolf's Orlando, which is cited by Saunders as a case of modernist 'biografiction.' 13 The question of the influence of Nicolson's text on Woolf's is not an insignificant one: Woolf reviewed Some People whilst in the midst of composing Orlando, which she would only finish writing the following year. Some People can be considered as a hybrid between 'biografiction,' in that the narrator is explicitly the biographer and the author, and 'autobiografiction' in that the author's own life is represented in the text. This collection of sketches is built of snippets from the lives of people who count as milestones in Nicolson's own. He was clearly inspired by Proust's À la recherche du temps perdu, a reference text for New Biography, the resemblance with which lies in the anecdotal 'involuntary memories'-to use Proust's term-that serve as a starting point for Nicolson's biographical accounts. The elements of fiction are an essential motif elaborately instilled into the narrative. The whole construction becomes an auto/biographical roman à clef-several subjects public figures-which makes it quite a conundrum for his readers. This, precisely, is what creates the tension within the text. The narrative ends with a completely fictional episode in which Nicolson describes a tortuous trip to Iran in the company of an obnoxious American Behaviourist. The character was inspired by a lady Nicolson had simply spotted once on a boat. Just as in Sartre's La Nausée, written few years later, the underlying message is that all biography necessarily leads to autobiography, which in turn necessarily leads to fiction.

Nicolson's Some People also proved to be a catalyser for New Biography. The text convinced Woolf that something special was going on in life-writing, giving it new momentum, and the enthusiasm Woolf expressed for Nicolson's text was unprecedented. Nicolson had, she claimed, attained some sort of ideal for biography: he was the new Boswell. 14 This open fervour about Nicolson's work was a recent phenomenon, for Nicolson's first encounters with Bloomsbury had been difficult. Bloomsbury was allergic to aristocracy and Nicolson was overtly proud of his rank. Reclusive and exclusive as Bloomsbury was, Lytton Strachey and Virginia Woolf loathed imitators and, back in 1923, they considered Nicolson to be no more than that. Upon reading Nicolson's second biography, Tennyson, Virginia wrote to Strachey's mother telling her she had had to put the book down in disgust and to 'purify myself I said that at once I would write a letter to Pernel. […] Of course, he's [Harold Nicolson] due to Lytton […] What these skilful imitators don't realise is that it is absolutely essential, if you are going to…' 15 ; the sentence breaks off here, uncompleted. As for Lytton Strachey, he had declined to review Tennyson for the Nation and Athenaeum. 'I'm sorry to say I can't face Lord Tennyson,' he wrote to Maynard Keynes who had made the request. 'Harold Nicolson's book is so disgusting and stupid.' 16 At that time, Woolf still did not know what to make of Nicolson's mirroring effect. After reading Byron, Nicolson's following biography, she wrote to Strachey: 'Byron seems to me tawdry and melodramatic. And Claire & Trelawny and so on and so on-I conceive them like a cave at some Earl's court exhibition-a grotto I mean lined with distorting mirrors and plasters with oyster shells. Do not trouble to unwind this metaphor.' 17 Woolf was soon to revise her opinion and the Hogarth Press even offered to publish their own 14 The Boswellian epoch represents a golden age for New Biography. See Woolf's essay 'The New Biography.' Let it be noted, here, that Boswell's papers had only recently been discovered, bringing him back to the foreground of life-writing. 15 Virginia Woolf, The Letters of Virginia Woolf, Nigel Nicolson and Joanne Trautmann (eds.), vol. 3 (1923[START_REF]Some People[END_REF]), London and New York, Harcourt Brace Jovanovich, 1977, p. 62. 16 James Lees-Milnes, Harold Nicolson: Volume One 1886-1929, London, Hamish Hamilton, 1987, p. 202. 17 Woolf, Virginia and Lytton Strachey, Letters of Virginia Woolf and Lytton Strachey, New York, Harcourt, Brace and Company, 1956, p. 149. edition of Byron, which Nicolson declined. They did however publish, in a pamphlet, his sketch of the landlady of Nicolson's student days in Paris, Jeanne de Hénaut, which would later become one of Some People's nine chapters. 18 Considering the reasons for the clash with Bloomsbury's biographers in 1923, it is true that many elements in Nicolson's introduction to Tennyson clearly refer to Strachey's manifesto contained in the preface in his composite biography Eminent Victorians, in which Strachey famously wrote: 'If he [the Historian] is wise, he will adopt a subtler strategy [than his Victorian predecessors]. He will attack his subject in unexpected places; he will fall upon the flank, or the rear; he will shoot a sudden light into obscure recesses.' 19 In similitude, Nicolson writes the following in his introduction to the bard's life: 'The Tennysonian laurels have grown by now into vast thickets, dusty, cumbersome and unvisited. It is necessary to let in the sun.' 20 This may have been the moment both Strachey and Woolf decided to put Nicolson's book down. However, it seems to me that Nicolson's introduction goes further in terms of its reflections on biography and historiography. Nicolson explains the reasons why Tennyson needs to be re-written: to be made accessible to the modern generation which can no longer accept Tennyson as 'the bard.' He emphasises the importance of the environment in understanding a writer, praising Taine's view that Tennyson was but a product of his time. He also believes there is not one historical truth, but rather any number of historical perspectives-and he lists a few of these with their very divergent conclusions. These considerations on the diversity of perspectives in history have no novelty for the dwellers of the postmodern age; but Nicolson's ideas must be placed in their own context if we are to assess the originality of his contribution to modernizing historiography.

In 1928, Nicolson set down his theoretical principles in The Development of English Biography (significantly published by the Hogarth Press, owned by Leonard and Virginia Woolf, as part of their Literature Series). The book stands as the first fulllength study of biography written by a modernist writer. 21 According to Nicolson, biography is as much history as it is literature. History gives truth. The aim of biography, then, is to provide a truthful record of an individual through the art of literature. This leads to his idea of 'pure' and 'impure' biography: the 'pure' achieves this truth and the 'impure' wanders off into the realm of fiction; but it is also 'impure' if the biographer's intent strays away from the one desire to seek out the truth about the subject. This was the case with moralising hagiography and nationalistic hero-worshipboth characteristic of the Victorian age. Autobiography, in Nicolson's terms, can never be 'pure' because of its ineluctable fictionalized aspects. Most of all, 'pure biography' should not only give fact; it must, above all, give experience to the reader. A decade later, Woolf was to develop a similar concept, central to her theory of biography: the 'creative fact.' 22 18 Claire Battershill, 'Biography and Autobiography at the Hogarth Press ', Thesis, n.p., 2012, p. 13, tspace.library.utoronto.ca., 11 November 2015. 19 Lytton Strachey, Eminent Victorians [1918], n.p, The Perfect Library, 2013, Kindle format, l. 9244. 20 H. Nicolson, Tennyson: Aspects of his Life, Character andPoetry [1923], London, Constable, 1925, p. 6. 21 Battershill, 'Biography and Autobiography at the Hogarth Press,' op. cit., p. 87. 22 'The Art of Biography,' The Death of the Moth and Other Essays, Prague, e-artnow ebooks, 2013, l. 2162. This leads to the question of biography's relation to the art of literature. The Russian literary critic and Formalist Victor Shklovsky started developing his theories on the role of literature as art as early as the 1910s. He considered art as being the 'renewal of form,' in which singling out a common object in an unfamiliar way enables us to recover our vision of it. [START_REF] Shklovsky | Literature and Cinematography, trans. Irina Masinovsky[END_REF] This is what he calls 'defamiliarisation' or 'ostranenie.' [START_REF]Shklovsky writes in Literature and Cinematography[END_REF] In his introduction to Tennyson (1923), Nicolson's formulation is similar: when dealing with a subject, the biographer needs to create 'a shock of sudden and delighted surprise.' Nicolson had often written biographies of previously biographized subjects, but each time providing an original perspective, so as to open our eyes on the subject, in a different way. As for Nicolson's 'pure' biography-which is art-'There must be a result for the reader, an active, not merely a passive adjustment of sympathy; there must result for him an acquisition not of facts only but of experience.' Following this thread, we can consider 'defamiliarisation' or 'estrangement' as a reader-oriented strategy in that it aims at getting the truth through by the means of experience: the subject is revealed to the reader in an unexpected and powerful way; it becomes an instance of recognition [START_REF] William | Epstein makes this point in his study, Recognizing Biography[END_REF] and even of revelation. Moreover, according to Shklovsky, the realisation of the process having led to the artistic creation is part of the reader's experience. In Tennyson, Nicolson states something quite equivalent when he writes: 'There must finally be a consciousness of creation, a conviction that some creative mind has selected and composed these facts in such a manner as to give to them a convincing interpretation.' [START_REF] Nicolson | [END_REF] Nicolson's Byron: The Last Journey, published in 1924, is a good example of defamiliarisation. It deconstructs the myth of Byron as being the ever young and seductive romantic poet, the courageous hero in the Greek war of independence. Here, the narrative starts with Byron in his mid-thirties, living in Ravenna, embourgeoisé and over-fed. Poetry is a thing of the past. He is even caught complaining to his military companion, Edward John Trelawny: 'Do people think I have no lucid intervals, that I came to Greece to scribble more nonsense? […] I wish I had never written a line, to have it cast in my teeth at every turn.' [START_REF] Nicolson | Byron: The Last Journey[END_REF] This perspective challenges the conventional vision of the bard. Concerning his sources, Nicolson acknowledges that most of the material he used had previously been collected by Edgcumbe, in his biography Byron the Last Phase (1909). Edgcumbe was a museum curator and the facts in his biography seem to be laid out like a stamp collection in an album; the reader is left to rummage through the 'amorphous mass,' to use Woolf's trope for Victorian biography. [START_REF] Woolf | The New Biography[END_REF] Nicolson constructs a narrative which allows us to understand Byron's acute awareness that he was heading towards his own death, and he resigned himself to it in an act of selfsacrifice, for the sake of the Greek independence movement. Nicolson writes in his preface, 'Lord Byron accomplished nothing at Missolonghi except his own suicide; but by that single act of heroism he secured the liberation of Greece.'29 So aware is Byron of the fate which lies in store for him, that he constantly delays and procrastinates his departure, and then his arrival at Missolonghi. A living legend, Byron is greeted as a demi-God, the news of his arrival spreads like fire and people 'flock' to see him and his companions, who are seven in number. The metaphysical character of his mission is expressed in his seeming indifference regarding what was actually going on in Greece. Resembling Christ's, Byron's mission is to save the Greeks from themselves: 'no good is to be done for these rascally Greeks, […] I am sure to be deceived, disgusted, and all the rest of it. It may be so; but it is chiefly to satisfy myself upon this very point that I am going. I go prepared for anything, expecting a deal of roguery and imposition, but hoping to do some good.'30 At one point in the narrative, Byron comes back from an overnight retreat on his boat, leaving his companions to look for him. Nicolson quotes from one of their journals:

At that moment, I happened to be standing at the window, and saw the object of our anxiety in the act of landing on the beach, about ten or a dozen yards from the house, to which he walked slowly up. He looked like a man under sentence of death, or returning from a funeral of all that he held dear on earth. His person seemed shrunk, his face was pale, and his eyes languid and fixed on the ground. 31 In Nicolson's account, he is depicted as lacking the traits of a military leader, tending to adopt a non-committal, 'if somewhat languid attitude' in the face of all the clamorous solicitations. 32 'Have you read any of the late publications on Greece?' [a companion] asked. 'I never read any accounts of a country to which I can myself go,' said he. 'The Committee have sent me some of their "Crown and Anchor" reports, but I can make nothing of them.' 33 The power of his action derived in fine from his being an icon, which Byron nurtured for instance by paying special heed to his appearance, clad in his majestic cloaks and observing his distinguished dandyish style. The London Committee, who had sent Byron on this mission, blatantly exploited this: a member of the committee wrote to Byron of 'the importance of your Lordship's presence in Greece-for the sake of the Greeks, for the sake of the committee, and on every account. The knowledge of your presence there will increase our funds and our influence at home and abroad. People will have a confidence in the cause itself, and in the appropriation of whatever they will give to the cause.' Byron understood it and agreed to embody his icon image, soon to become martyrized. Through Nicolson's construction, meaning is revealed: in Shklovsky's words, the process of perception has an aesthetic end to it; this is what enables Nicolson to project this particularly morbid representation of Byron on his final journey towards his death in Missolonghi as akin to Christ's last journey back to Jerusalem.

Nicolson occupied the foreground of the experimental modes of life-writing in the 1920s and 1930s, a period during which he also wrote the lives of Swinburne, of his father, Sir Arthur Nicolson, 'First Lord Carnock,' of Lord Curzon, and of the American Dwight Morrow. His work was singled out by Virginia Woolf who acknowledged him to be a leading figure of New Biography. He produced biography, theorised and interacted with other new biographers such as Strachey, Woolf and Maurois, creating a new dynamism which led biography to become accepted as a form of literary art, thus liberating it from the Victorian constraints which came to be seen as a restriction of biography-and history generally-to the domain of natural sciences. In doing so, not only did Nicolson revolutionise the reader's experience of biography, he played a substantial part in modernising the historiography of his time.

8

  Harold Nicolson, Paul Verlaine, London, Constable, 1921, Internet Archive.org, 6 October 2015, p. 74.

9

Nigel Nicolson, A Portrait of a

Marriage (1973), London, Orion Books, 1992, p. 134. 10 H. Nicolson, Paul Verlaine, op. cit., p. 75. 11

N. Nicolson, A Portrait of a Marriage, op. cit., p. 134. 12 H.

Nicolson, Paul Verlaine, op. cit., p. 60. 13 Saunders, Self Impression, op. cit., p. 218.

Virginia Woolf, 'The New Biography,' Granite and Rainbow [1956], London, Forgotten Books, 2015, p. 152.

Norman Rose, Harold Nicolson, London, Jonathan Cape, 2005, p. 82.

TheHarold Nicolson Diaries: 1907-1964[START_REF]The Harold Nicolson Diaries[END_REF], ed. NigelNicolson, London, Phoenix, 2004, p. 21.

Woolf, 'The New Biography,' op. cit., p. 154.

Oxford, Balliol College, Diaries of Harold Nicolson (13 April 1918), digitalised manuscript diaries. By courtesy of Balliol College, University of Oxford, whom I would like to thank for graciously allowing me to quote from the diaries held at Balliol Library.

H.Nicolson, Byron, op. cit., p. viii.

Ibid., p. 115.

Ibid., p. 115.

Ibid., p. 121.

Ibid., p. 114.