Marcus Thatcher

John Mcgregor

Martin Dix

Jack Katzfey
email: jack.katzfey@csiro.au

Martin Mcgregor

Dix

A New Approach for Coupled Regional Climate Modeling Using More than 10,000 Cores

Keywords: geophysical fluid dynamics, MPI, regional climate modeling

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Regional Climate Models (RCMs) are used to study how changes in global climate can affect meteorology at fine spatial scales. Such models are commonly used to dynamically downscale climate change projections from Global Climate Models (GCMs) to regional and local spatial scales appropriate for impact studies. There are four main components in the construction of an RCM:

• The dynamical core that simulates the atmosphere as a geophysical fluid.

• Parameterization of atmospheric physical processes, such as solar radiation, clouds, boundary layer turbulent mixing and aerosols. • Parameterization of surface interactions at fine spatial scales, such as mountain ranges, forests, deserts, ice and oceans, which significantly influence the regional meteorology.

• An assimilation or nudging system to ingest large scale changes in atmospheric circulation and sea surface temperatures, as predicted by GCMs for different global warming scenarios.

The RCM integrates these four components to produce a consistent representation of the meteorology at regional spatial scales (i.e., 1 to 100 km). RCMs face a difficult computational challenge as they are required to simulate atmospheric processes that may last seconds to minutes, whilst integrating the simulations for time periods greater than one hundred years. Computational load-balancing issues arise as the various simulated atmospheric processes are not uniformly distributed over the simulation grid. Furthermore, assimilating climate data also requires the RCM to be reading and writing atmospheric datasets while running on hundreds to thousands of cores.

Traditionally the ocean is simply represented in an RCM by interpolating Sea Surface Temperatures (SSTs) from a GCM dataset. This approach neglects any feedback between the atmosphere and ocean and smoothes out variability arising from ocean surface eddies. Most attempts to construct an atmosphere-ocean coupled RCM are extensions of the GCM approach, where a 'coupler' is used to manage communication between atmosphere and ocean processors. Since the atmosphere and ocean usually use different grids, complex communication patterns can arise, resulting in considerable inter-node message passing that slows down the simulation. To minimize the impact of this communication bottleneck, couplers usually only exchange information between the atmosphere and ocean models every few simulation hours.

This paper proposes an alternative approach to developing an atmosphere-ocean coupled RCM that has good wave dispersion properties and allows frequent coupling without increasing simulation time. Our approach is based on a reversibly staggered grid [START_REF] Mcgregor | Geostrophic adjustment for reversibly staggered grids[END_REF], which was originally developed for the Conformal Cubic Atmospheric Model (CCAM) [START_REF] Mcgregor | C-CAM: Geometric aspects and dynamical formulation[END_REF]. CCAM is a global atmospheric model based on a variable-resolution conformal-cubic grid so that it can simulate regional scales without lateral boundary conditions. The reversibly staggered grid handles horizontal velocity components and was introduced into CCAM to provide good gravity wave dispersion properties. However, the reversibly staggered grid has also been found to possess excellent dispersive properties when simulating the ocean [START_REF] Mcgregor | Geostrophic adjustment for reversibly staggered grids[END_REF], making it suitable as a universal grid for an atmosphere-ocean coupled model. Using a common grid for the atmosphere and ocean avoids the communication bottleneck associated with traditional coupling methods and improves the simulation timings. We have constructed such a reversibly staggered ocean model within CCAM to assess its potential for coupled regional climate modeling. After some optimization, the coupled model prototype was found to exhibit strong scaling beyond 10,000 cores while exchanging fluxes between the atmosphere and ocean models every time-step (e.g., 3 minutes).

CCAM and its variable-resolution conformal-cubic grid are described in Section 2. Section 3 outlines the CCAM software architecture and parallel programming issues. Section 4 explains how the regional ocean model was integrated into CCAM using the reversibly staggered grid. Section 5 describes the performance of the prototype atmosphere-ocean coupled regional model, including timings for different size grids and scaling issues. Conclusions and future work plans are presented in Section 6.

The Conformal Cubic Atmospheric Model

The Conformal Cubic Atmospheric Model (CCAM) is based on the conformal cubic projection [START_REF] Mcgregor | C-CAM: Geometric aspects and dynamical formulation[END_REF] (see Fig. 1). CCAM was the first three-dimensional atmospheric model to use a cube-based grid, which provides significant computational advantages due to avoiding coordinate singularities while still locally resembling a Cartesian grid. The CCAM grid can be focused over a region of interest using the Schmidt transformation [START_REF] Schmidt | Variable fine mesh in spectral global models[END_REF] and was part of the first generation of variable-resolution global atmospheric models [START_REF] Fox-Rabinovitz | Variable resolution general circulation models: Stretched grid model intercomparison project (SGMIP)[END_REF]. By using a global, variable-resolution grid, CCAM avoids lateral boundary conditions and facilitates coupling between the global and regional spatial scales on the same grid. CCAM currently employs a non-hydrostatic, semi-implicit, semi-Lagrangian dynamical core that is efficient for regional climate modeling due to the relatively large time-step allowed by semi-Lagrangian methods. CCAM also has an extensive series of physical parameterizations to describe the behavior of the landsurface, aerosols, convection, cloud microphysics, radiation, gravity wave drag and boundary layer turbulence. For assimilation, CCAM employs a convolution-based scale-selective filter [START_REF] Thatcher | Using a scale-selective filter for dynamical downscaling with the conformal cubic atmospheric model[END_REF], that nudges the atmosphere at large length scales (e.g., greater than 3,000 km) towards a specified GCM projection of future climate. We base our filter on a convolution as it can be easily applied to CCAM's variable-resolution cubic grid and can accommodate irregular coastlines when assimilating ocean data in the atmosphere-ocean coupled RCM.

Software Design

The software components of CCAM are represented in Fig. 2, where initial conditions and configuration data are read by the initialize component at the start of the simulation. Prognostic variables are transported around the model by the dynamics component. Assimilation perturbs CCAM towards a global GCM dataset. Additional or unresolved processes that need to be parameterized are included under physics. Prognostic and diagnostic data are periodically written to a history file from the output routines. Lastly, the finalize component stores all prognostic variables at the end of each simulation month in a restart file. The restart file is read as initial conditions for the next simulation month when CCAM is restarted with new configuration data. Each component in Fig. 2 can be further divided into atmosphere, ocean and landsurface sub-components. CCAM is written in Fortran and uses the Message Passing Interface (MPI) system to communicate between parallel processors [START_REF] Mcgregor | An updated description of the conformal-cubic atmospheric model[END_REF]. The conformal cubic grid is decomposed over the available processors into equal rectangular parts, with no partitioning in the vertical direction. This is because the atmospheric physics, with its faster processes, depends on the entire vertical column during a time-step. Communication between parallel processors is only required for the initialize, dynamics and assimilation components (i.e., the upper half of No Message passing ize routines write separate NetCDF files for each processor. CCAM output variables are selectively re-combined from the parallel output files using post-processing software, which also interpolates the simulation results to a latitude/longitude grid.

Fluxes are exchanged between the CCAM atmosphere and ocean models in the physics component, due to the use of a common reversibly staggered grid. This is because the fluxes between the atmosphere and ocean are independent for each gridpoint on the common grid, in contrast to models where atmosphere and ocean grids are misaligned so that additional message passing is required to communicate fluxes. Atmosphere and ocean wave dispersion properties are not compromised when using a common reversibly staggered grid, as discussed in the Section 4.

To maximize the scaling properties of CCAM, most of the dynamics software routines use non-blocking, point-to-point communication between neighboring processors to exchange portions of atmosphere and ocean data at processor boundaries. However, CCAM's semi-implicit, semi-Lagrangian dynamical core relies on an implicit solution to the Helmholtz equation, with the model performance being largely determined by the numerical algorithm used. We found that a custom-designed geometric multi-grid solver for the Helmholtz equation demonstrated the best timings for more than 1,000 processors, although Conjugate-Gradient and Successive Over-Relaxation methods are also available.

The scale-selective filter, which is used to assimilate host GCM data into CCAM at large spatial scales, naively requires O(N 2 ) computations where N is the number of horizontal grid points. However, the efficiency of the scale-selective filter was improved by approximating the two-dimensional convolution with two separate onedimensional convolutions. Although this approach reduces the number of computations to O(N 3/2 ), processors still require data from all other processors whose gridpoints lie along a common row or column on the CCAM cubic grid. We found that using MPI-2's Remote Memory Access (RMA) allowed us to efficiently pass the required information around the cubic grid, improving the filter timings by a factor of 5 compared to a naïve global 'gatherall' approach.

CCAM can be compiled for single precision as well as double precision computations, where the use of the single precision mode can significantly reduce communication bandwidth problems. Care is taken to avoid any degradation to model performance when using single precision, such as always using double precision for the radiation code and improving the precision for calculating global sums.

A Reversibly Staggered Ocean Model for CCAM

Our atmosphere-ocean coupled regional climate model exploits CCAM's reversibly staggered grid so that a common grid can be used for both the atmosphere and ocean.

The atmosphere and ocean models can then be coupled together in the physics component, avoiding additional message passing overheads associated with exchanging momentum, radiation, temperature and moisture fluxes between models. The reversibly staggered grid determines the location of the velocity fields on the model grid as shown in Fig. 3. Most atmospheric models are based on the staggered Arakawa C-grid due to poor wave dispersion properties associated with the unstaggered A-grid. However, the A-grid is suitable for computing Coriolis terms that couple the U and V velocity components, related to the rotation of the Earth. The A-grid is also convenient for carrying out the physical parameterizations at coincident grid points. CCAM's reversibly staggered grid switches between the Arakawa A-grid and the Cgrid, thereby exploiting the advantages of both grids. Importantly, the reversible transformation can switch between the staggered grids without any loss of information. It has been shown that the reversibly staggered grid approach provides excellent dispersive properties for both atmosphere and ocean geophysical fluid dynamics [START_REF] Mcgregor | Geostrophic adjustment for reversibly staggered grids[END_REF].

Fig. 3. Schematic representation of the reversibly staggered grid that switches between the Arakawa A-grid (left) and the C-grid (right). T represents a scalar field (e.g., temperature) at the center of the grid box, whereas the velocity components U and V can be reversibly staggered with respect to T to improve the dispersive properties of the simulation.

The CCAM ocean model has prognostic equations for the currents, potential temperature, salinity and the surface displacement height of the ocean. We also include a cavitating fluid model of sea-ice, although we do not discuss sea-ice further in this paper. Both the atmosphere and ocean models use a semi-implicit, semi-Lagngian dynamical core, but with some differences such as different equations of state for density. The ocean model adopts bathymetry-following sigma-z coordinates such that that the vertical grid stretches and compresses as the ocean depth changes. Careful treatment of the pressure gradient terms was required to avoid spurious pressure gradients arising from discretization errors.

The Vandermonde formula, used for constructing the reversibly staggered grid in CCAM, is represented as a periodic tri-diagonal matrix that circles the globe. As there is a portion of the tri-diagonal matrix on each processor, we iteratively solve the tri-diagonal matrix to improve the scaling of CCAM, thereby avoiding global MPI communications. Typically six iterations are sufficient for the problem to converge. In the case of the ocean model, special treatment of the reversibly staggered grid is required along coastlines to be consistent with the non-slip boundary condition for currents. In practice, this problem is solved by reducing the order of the Vandermonde formula when approaching coastlines (i.e., reduced to a linear model) so that no information is lost when switching between the Arakawa A and C-grids.

A-grid C-grid T,U,V T U U V V Reversible R-grid
Our atmosphere-ocean coupling method is particularly suited to RCMs, since the spatial resolution of the shared reversibly staggered grid is sufficient to resolve eddies in both the atmosphere and ocean. RCMs can also capitalize on the frequent exchange of information between the atmosphere and ocean models, which can be important for simulating extreme weather events such as cyclones. Although different numbers of land and ocean points on different processors can result in load imbalance issues, this problem already existed with the simple interpolated SST approach used by most RCMs and is not exacerbated by our coupling method.

An example of how the reversibly staggered ocean model modifies the structure of SSTs can be seen in Fig. 4, where ocean surface eddies were spun up by the coupled model and can be seen in the small-scale structure of the simulated SSTs.

5

Performance of the Atmosphere-Ocean Coupled Model

Figure 5 shows the timings for (unstretched) global, atmosphere-ocean coupled CCAM simulations as a function of the number of processors for various grid resolutions. These simulations were performed on the Pawsey Centre's Magnus, which is a Cray XC40 system using Intel Xeon "Haswell" processors. The CCAM simulations employed 35 vertical levels over a 40 km height for the atmosphere and 30 levels over a 5 km depth for the ocean, including a minimum of 10 levels in the top 200 m of the ocean. We used CCAM in single precision mode, which reduced the required internode message passing bandwidth. Finally, we applied the scale-selective filter every six simulation hours to perturb the air temperature, winds, surface pressure and SSTs towards ERA-Interim reanalyses (i.e., an atmospheric dataset that is constrained by observations). For the case of a 13 km resolution simulation, the size of the raw output files is approximately 10 GB per month, although the output is split into separate parallel output files for each processor. models such as the double precision NCAR GCM that produced 2.6 simulation years per day for the same horizontal resolution, but required 200,000 cores [START_REF] Worley | Performance of the community earth system model[END_REF]. Hence CCAM's design allows it to simulate the regional climate with considerably less computing resources than typically required for RCM experiments. We note that the CCAM timings are a consequence of a number of factors including the use of single precision, the semi-Lagrangian dynamical core, the geometric multi-grid algorithm, as well as the reversibly staggered coupling strategy. The multigrid solver and the scale-selective filter were found to be the weakest components of CCAM with respect to scaling, as the atmosphere-ocean coupling has no communication overhead. The scale-selective filter was found to scale proportionally to the square root of the number of processors when using RMA, but is only called every six simulation hours. Therefore the relative cost of the filter remains the same portion of the total run time when the resolution is increased and the model time-step is decreased. We are then left with the geometric multi-grid solver as the primary limitation on model scaling. Although our custom-designed geometric multi-grid solver has competitive timings compared to other multi-grid solvers reported in the literature, it can be difficult to fully exploit the available parallel processing with multi-grid methods. For this reason, we are investigating explicit methods for the dynamical core as described in Section 6.

Conclusions

This paper describes an alternative approach to atmosphere-ocean coupled regional climate modeling that is based on a reversibly staggered grid. The excellent dispersive properties of this grid are suitable for simulating both atmosphere and ocean geophysical fluid dynamics, making it appropriate as a common grid for integrating the two models. The message passing overhead associated with exchanging fluxes between the atmosphere and ocean models can then be eliminated. The approach is valid when both atmosphere and ocean eddies are resolved on the common grid and the frequent exchange of fluxes between the models is important, making the method well suited for RCMs. We have constructed a prototype of the reversibly staggered, atmosphere-ocean coupled regional model using CCAM and show that the single precision version can produce timings of 5 simulation years per day using 13,824 cores. Factors contributing to this result include the use of single precision, the semi-Lagrangian dynamical core, the geometric multi-grid algorithm, as well as the reversibly staggered grid coupling strategy. The CCAM timings are comparable to a stateof-the-art GCM using 200,000 cores [START_REF] Worley | Performance of the community earth system model[END_REF], although CCAM requires considerably fewer cores making it well suited to regional climate modeling.

Although entirely adequate for petascale computing resources currently available in Australia, CCAM scaling will eventually be limited by the implicit geometric multi-grid solver for the Helmholtz equation. This issue is being addressed with the development of a split-explicit, flux-conserving dynamical core that will use an equiangular, gnomonic cubic grid. Nevertheless, the current semi-implicit, semi-Lagrangian prototype successfully demonstrates the viability of the reversible staggering approach for atmosphere-ocean coupled regional climate modeling. It may also be possible to use the reversibly staggered grid for other types of geophysical fluids, although the feasibility of our approach will depend on the dispersive properties of the reversibly staggered grid for the modeled system.

Fig. 1 .

 1 Fig. 1. Example of a stretched conformal cubic grid employed by CCAM, focused over Australia, using a Schmidt factor of 3 (i.e., the front panel of the cube has shrunk by a factor of 3).

Fig. 2 .

 2 Fig. 2. Schematic diagram of the CCAM software components, where the dynamics, assimilation, physics and output routines cycle every model time-step. Message passing between processors is only required in the upper half of the plot, as explained in the text.

  Fig 2). There is no communication between processors in the lower half of Fig.2, as physics routines independently update the prognostic variables for each vertical column of grid-points and the output and final-

Fig. 4 .

 4 Fig. 4. Comparison between interpolated SSTs from ERA-Interim 'observations' (left) and spun-up SSTs by the reversibly staggered ocean model (right) at 10 km resolution over the target domain. SSTs are in units of degrees Celsius.

  Figure5shows the timings for (unstretched) global, atmosphere-ocean coupled CCAM simulations as a function of the number of processors for various grid resolutions. These simulations were performed on the Pawsey Centre's Magnus, which is a Cray XC40 system using Intel Xeon "Haswell" processors. The CCAM simulations employed 35 vertical levels over a 40 km height for the atmosphere and 30 levels over a 5 km depth for the ocean, including a minimum of 10 levels in the top 200 m of the ocean. We used CCAM in single precision mode, which reduced the required internode message passing bandwidth. Finally, we applied the scale-selective filter every six simulation hours to perturb the air temperature, winds, surface pressure and SSTs towards ERA-Interim reanalyses (i.e., an atmospheric dataset that is constrained by observations). For the case of a 13 km resolution simulation, the size of the raw output files is approximately 10 GB per month, although the output is split into separate parallel output files for each processor. The 13 km resolution single precision result of 5 simulation years per day with 13,824 cores is competitive with state-of-the-art

Fig. 5 .

 5 Fig. 5. Scaling results for single precision, atmosphere-ocean coupled CCAM simulations on IVEC's Magnus for three different global grid resolutions. Time-steps of 900 s, 360 s and 240 s were used for the 50 km, 26 km and 13 km resolution simulations, respectively.

adfa, p. 1, 2011. © Springer-Verlag Berlin Heidelberg 2011

Acknowledgements

The authors wish to thank Pawsey Centre's Petascale Pioneer program for providing computing resources for the simulations described in this paper. Thanks to Aaron McDonough and Paul Ryan for their technical advice. We also acknowledge the constructive feedback on the manuscript from Peter Dobrohotoff and the two anonymous reviewers.


Emna Trabelsi
email: emna.trabelsi2007@yahoo.fr.

Central Bank Transparency and the consensus forecast: What does The Economist poll of forecasters tell us?

Keywords: Transparency, Communication, Consensus forecasts, Economist poll of forecasters, Inefficiency JEL codes: B22, C23, E58

 both of which are consistent with overweighting issue to explain the inefficiency of the consensus forecast.

Introduction

Nowadays, transparency practices are seen as a successful premise in achieving the objectives, especially those applied in monetary policy framework. The prompted rise of central bank transparency is derived from the view that transparency is a prerequisite for accountability in an era of independence [START_REF] Dincer | Central Bank transparency and independence: Updates and new measures[END_REF].

We address an empirical focus on the effects that central banks exert on private sector forecasts by means of their transparency and communication measures.

The central bank's public disclosure about monetary policy and regular assessment of economic developments attracts market participants. This is helpful for the markets to understand how systematic, the response of monetary policy to economic development and shocks, and to anticipate the overall stance of monetary policy over the medium term. Markets can thus develop more efficient and accurate forecasts, thereby allowing for a quick implementation of monetary policy in financial variables. In recent years, central banks around the world have increased the volume of information they offer to the public. For example, many central banks have become more explicit about the long-run objectives of monetary policy such as long-run inflation objectives. Central bank transparency is warranted because such a practice generates important benefits including more effective monetary policy [START_REF] Sellon | Monetary policy transparency and private sector forecasts: Evidence from survey data[END_REF]. Markets don't only react to central bank's announcements because they are valuable, but also because other markets participants will react to the same disclosure. However, public disclosure may serve as a "double-edge" sword and could lead to inefficient outcomes [START_REF] Morris | The Social Value of Public Information[END_REF].

In this paper, we use rarely exploited data1 contained in the Economist poll of forecasters and investigate whether, or to what extent central bank communication and increased transparency have affected both the level and the accuracy of private agents' forecasts of inflation and other economic variables.. There exists an extensive literature on forecasts, forecast accuracy, and their response to central bank's policies. Bauer et al. (2008) looked at the effects of changes in Fed's communication policy on macroeconomic forecasts taken from Blue Ship Economic Indicators report and found that the publication of FOMC statements in addition to the Fed's rate decisions didn't reduce the forecast errors. In the same context, [START_REF] Hubert | The influence and policy signaling role of FOMC forecasts[END_REF] examined the usefulness of information contained in FOMC macroeconomic forecasts (central bank's forecasts) using data from the Survey of Professional Forecasters (SPF). Another strand of the literature is closer to our framework. It assessed the effect of increased central bank communication and transparency on private expectations2 . [START_REF] Capistran | Does Inflation Targeting Affect the Dispersion of Inflation Expectations?[END_REF] and [START_REF] Gürkaynak | Does Inflation Targeting Anchor Long-Run Inflation Expectations? Evidence from the U.S., U.K., and Sweden[END_REF], among others, studied the effect of inflation targeting on private forecasts. Most of the previous papers concentrate either on a specific component of central bank's communication and transparency policy (for example the adoption of inflation targeting regime taken as a variable of interest) or they consider the dispersion of inflation expectations as the response variable. We contribute to the existing literature by, first, evaluating the impact of central bank's practices on forecasts both in terms of level and accuracy. The second aim of the paper is to discuss the (in)efficiency issue of the consensus forecast as a proxy of private-sector's expectations. The associated test is carried out by calculating the forecast revisions and their correlation with the forecast errors.

We are aware of two papers of [START_REF] Siklos | Assessing the Impact of Changes in Transparency and Accountability at the Bank of Canada[END_REF][START_REF] Siklos | Sources of disagreement in inflation forecasts: A cross-country empirical investigation[END_REF] exploring data from the Economist poll of forecasters. We deviate, however, from his approach in, at least, five main aspects:

1. Siklos (2003,2010) focused mainly on inflation3 in level and inflation forecast disagreement, while our study addresses the consensus forecast and covers other key economic indicators. More importantly, the current account as a share of GDP is rarely discussed in forecasting literature.

2. We include a set of controls susceptible to affect the forecasts. We fill this gap by including the appropriate determinants to predict future inflation, RGDP and the current account as a share of GDP besides the variables of interest4 ; 3. We extend the analysis to include more transparency and communication variables. Precisely, we use the multidimensional index of transparency5 constructed by Dincer andEichengreen (2010, 2014).

4. It is worth exploring whether increased transparency and communication levels influence private sector process formation. It is, also, of utter importance to test how the forecast accuracy is affected as we need to make sure that most transparent central banks ensure lower levels of forecast errors.

5.We contribute to the literature on the forecast efficiency by seeing how aggregating all the monthly incoming information by the forecasters is a challenging task.

We believe our study could interest both policy makers and researchers as it offers further insights on how effective the central bank transparency and communication are in improving the accuracy of the private sector expectations. The beliefs that the private sector forms about the central bank's announcements in forecasting are important because they determine ultimately the inflation and the output, outcomes, even though those forecasts don't match the central bank's policy rule.

The remainder of the paper is as follows; section 2 presents the theoretical basis of our framework, section 3 describes the empirical analysis, including description of data used and presents the main results of estimations. Section 4 offers further interpretations of those results. Section 5 concludes.

Theoretical deliberations

Before analyzing data, we present an illustrative (and simple) framework that motivates our empirical analysis. Prior research on accuracy and consensus forecasts' efficiency showed that forecasters use both public (common) and private information in forming predictions. Therefore, if these forecasts are efficient, they will incorporate all available information. In a model where analysts possess this information in the form of noisy signals about the analysts' beliefs, these beliefs diverge. The problem arises from two possible facts. (i) either the forecasters take into account irrelevant information (ii) or they underweight the private information. The main result referred the phenomenon to as "inefficiency of the mean forecast". [START_REF] Morris | The Social Value of Public Information[END_REF] considered the same data-generating process described above, but added a beauty contest element6 . They stated that more accurate public information can increase the average error of the private sector because forecasters attempt to second guess each other by placing a weight on the common signal in excess of the optimal Bayesian weight when forming expectations. Hence, the public signal reflected in the consensus forecast plays a dual role: (i) it conveys information about the fundamental state (information role) (ii) it is a coordination device of the views of other forecasters (commonality role). [START_REF] Crowe | Consensus forecasts and inefficient information aggregation[END_REF] provided an excellent reassessment of both arguments and showed that the negative (eventual) consequences of a more transparent public information should not be on the grounds of the presence of a coordination motive (i.e. the beauty contest element) in the guessing-game of [START_REF] Morris | The Social Value of Public Information[END_REF] 78 . Drawing upon both papers, we give a brief description:

There's a continuum of agents who receive two types of signals on an unknown fundamental  , one is a purely private signal that is independent among agents (pri), and the other is a purely public (pu) signal that is perfectly correlated among agents9 :

{ 𝜽 = 𝒑𝒖 + 𝜼 𝜼 ~ 𝑵(𝟎, 𝝈 𝜼 𝟐 ) 𝒑𝒓 𝒊 = 𝜽 + 𝜺 𝒊 𝜺 𝒊 ~ 𝑵(𝟎, 𝝈 𝜺 𝟐 ) (1)
Using Bayesian update rule, the first-order expectation is the weighted sum of both signals shown in Eq.1

  i i pr pu E 2 2 2 2 2 2                  (2)
It follows from Eq. 2, the average expectation of the fundamental (the mean forecast) would be

                   2 2 2 2 2 2       pu di E E i (3)
Agents form forecasts so that they minimize the mean squared forecast error

    2    E E .
By differentiating with respect to 2   , we obtain

    2 2 2 2 0               E E (4)
According to Eq.4, greater transparency ( 0 2     ) can increase the consensus forecast error if the public signal is initially noisier than agents' own signals.

If the consensus forecast overweights public information, it is inefficient. In fact, the mean forecast is negatively correlated with its forecast errors, while the individual forecasts are not.

        0 ) ( 2 2 2 2 4                 E E E
To put the above theoretical arguments into the test, we first assess the response of the consensus forecasts, in level, to central bank's communication and transparency policies. Then, we turn to evaluate the relationship between the same variables of interest and the quality of forecasts in terms of accuracy and examine the forecasting properties (efficiency).

3

Econometric modelling

Model, data and preliminary sketch

One of the impediments to transparency-economic forecasts, relationship was the dearth of data on central bank transparency index. At least, empirical literature dedicated to studying the effects of central bank transparency and communication practices focused on the impact of inflation targeting regime adoption on the forecasts. Crowe (2010a) analyzed the behavior of private sector forecasts of inflation in eleven inflation targeting countries. The findings were consistent with the fact that this regime improves forecasting accuracy of private sector. [START_REF] Crowe | Central Bank Independence and Transparency: Evolution and effectiveness[END_REF] found that enhanced transparency practices are associated with the private sector, making greater use of information of central banks. While most of the empirical studies focus on economic forecasts, [START_REF] Middeldorp | Central bank transparency, the Accuracy of professional forecasts, and interest rate volatility[END_REF] looked at the response of the interest rate using a multi country panel data. [START_REF] Hubert | Disentangling qualitative and quantitative central bank influence[END_REF] conducted an interesting study on the influence of ECB communication variables (by distinguishing quantitative versus qualitative) on inflation forecasts, including another set of controls that could affect the forecasts.

Another more recent strand of literature focused on the determinants of disagreement using individual expert forecasts from the frequent source Consensus Economics (see [START_REF] Ehrmann | The Role of Central Bank Transparency for guiding private sector forecasts[END_REF][START_REF] Dovern | Disagreement Among Forecasters in G7 Countries[END_REF].

In our paper, we explore data from the Economist poll of forecasters covering the period November 2001 to December 2010. They are gathered and downloaded from http://www.oecd.org/eco/surveys/18635847.htm. The Economist surveys a group of 15 financial forecastersincluding The Economist Intelligence Unit and 14 international banks from the financial centers-on a monthly basis and calculates the average of their annual predictions for the key macroeconomic variables: economic growth, inflation, and current account balances for 15 countries and the Euro-Area. The private sector forecasts are, thus, reflected in the consensus (average) forecasts included in the Economist. The polls performed in January and February of every year; contain the forecasts for data of the previous and the current calendar year. From March to December, the forecast reports to the current and the following calendar year. In testing the impact of central bank transparency on macroeconomic forecasts, we introduce both inflation and non-inflation targeting countries since data from the Economist poll of forecasters are available for a short country sample. 10 We add the content and the quality of inflation report as another more specific measure of transparency. It forms the main information tool used by central banks to communicate with the public. The frequency of this report differs among central banks (Table B.2). We created a dummy variable set 1 if there is release of such a report at month t and 0 otherwise. A more general index of transparency is given by Dincer andEichengreen (2010, 2014) 11 . They assign 0, ½ or 1 points to each of the fifteen questions and they sum up them. According to the overall measure, the Sveriges Riksbank (Central bank of Sweden) is the most transparent central bank in 2010 with a score of 14.5. The Federal Reserve joined also the top ten of the most transparent central banks with a score of 11 the same year. Overall, it makes a sense to observe a higher degree of transparency for OECD countries since they are better able to process information (Figure A.1).

Empirically, we assess the effect of transparency and communication variables on the forecasts through the following Eq:

  it it it s it x it s x x E                 1 1 (5) Where 1  s it x
is the actual (annual) lagged variable to be forecast.  contains the three communication and transparency variables of interest: Central bank transparency index of Dincer andEichengreen (2010, 2014), the inflation report (labelled as comm) and inflation targeting dummy.  encompasses the macroeconomic controls: output gap as a percentage of potential GDP, the oil price (Europe Brent spot price) and the unemployment rate is used to represent cyclical positions [START_REF] Minegishi | The Role of Transparency in the Conduct of Monetary Policy[END_REF]. To adhere to the information timing structure, we regress private forecasts on controls at date t-1/s-1 since the pollsters collect and publish their forecasts at the beginning of each month12 . We suppose that forecasts are formed based on the information set  from the previous month/year. Eq. 5 is estimated first for forecasts formed at month t of the current year s made during the current year s. The same Eq is then replicated for forecasts formed at month t of the next year s+1 made in the current year s. In other terms: The Philips curve links inflation to the output gap positively. The oil price shall have a positive incidence on inflation and current account as a share of GDP. i refers to the country and t refers to the monthly period frequency. Details about variables and source are available in Table B.1. Lastly, we run Feasible Generalized Least Square (FGLS) regression. The method requires two conditions: (i) The panel is strongly balanced; (ii) The time dimension exceeds the cross-sectional dimension   N T  .

                    s.
Graphs offer an intuitive way to illustrate the role that transparency may play in forecasting. Note that all the data points are predictions for the same period, for example for the calendar year of 2005. However, they differ since the forecasters continuously update their information set. As seen in the second graph, for example, forecasters became increasingly optimistic about US growth for 2005 during the year of 2004. But, over the course of 2005, their forecasts declined slightly from the peak.

The comparison between forecasts (of the current year and the next year, respectively) and their realized values is made available through their plot in . They show the pollsters' monthly inflation, growth and the current account balance predictions for all countries considered in the panel under the period 2001-2010. The gap between the projections and realized values shrinks at the end of each year in most cases.

Effect of Central bank transparency and communication on forecasts

We estimate different variants of the model (1) for different forecasts. Line (1) contains the estimates testing for the importance of releasing the inflation/monetary policy report. Lines (2) and (3) combine two central bank transparency and communication measures with the lowest correlation coefficients in a joint model for both samples.

Results for the sample (2001-2006)

Multivariate estimations reported in the first part of Tables B.3.1-B.3.3 confirm that comm has a negative effect on both current and next-year inflation forecasts. Overall transparency index of Dincer and Eichengreen ( 2010) is, however, not significant. Introducing inflation targeting (IT) in the model is to alter the behavior of macroeconomic forecasts. This binary variable has a positive and significant impact only on next-year inflation forecasts.

Three interpretations of these results emerge: (i) The disclosure of monetary policy/inflation reports leads forecasters to decrease their inflation expectations by 10 to 13%. (ii) The introduction of inflation targeting regime plays a distinctive role in terms of impact on forecasts (iii) No clear evidence that enhanced transparency (introduced itself in the regression) necessarily (and directly) affects inflation forecasts. From an initial point of view, this result is due, in a part, to the slow change of the transparency index which is annual and is sometimes stable through years in some countries considered in the sample.

A slight change to the original specification is made through including an interaction term (inflation transparency). The rationale behind this new regression is that if greater transparency successfully anchors inflation expectations, they should exhibit a lower response to changes in actual inflation. This point identifies a negative and significant impact of the interaction term (transparency index  actual inflation) on inflation forecasts. The result shows that where central banks are more transparent, expected inflation is less sensitive to changes in inflation outcome.

Our variables of interest (Overall transparency index, communication and inflation targeting dummy) have all a significant impact on RGDP and the current account as a share of GDP and go into the same direction for the latter. That is, they have a positive effect on the above mentioned forecasts, except the release of inflation report (comm) which exerts a negative impact on RGDP.

Moreover, lagged actual outcome, the oil price, the output gap and the unemployment rate appear to add a highly (depending on the control variables) significant value to inflation private expectation formation. The same result holds for the other dependent variables considered in our study (RGDP forecast, current account as a % of GDP).

Results for the sample (2001-2010)

Following the first sample, the first issue examined is whether the index of transparency (T) and the release of inflation reports (labelled as "comm") influence private sector forecasts of inflation, growth and current account balance and whether noticeable differences exist between countries that formally target inflation and other major central banks that haven't adopted inflation targeting regime yet. Regressions were re-estimated in a panel setting to assess whether private sector inflation forecasts are affected by the publication of an inflation report (a one-dimensional indicator of transparency), by the general indicator of transparency updated by [START_REF] Dincer | Central Bank transparency and independence: Updates and new measures[END_REF] (which is multidimensional), and the requirement to meet numerical inflation targets (an indicator of greater accountability). As explained earlier, Eq. 5 allows for controlling the relationship between forecasts and their corresponding actual lagged data, prevailing at the time of projection. The associated coefficient is highly significant and large for all (current and next-year) macroeconomic variables as seen in the second part of Tables B.3.1-B.3.3. The results reveal that most of transparency and communication measures (The variables of interest) have a significant impact on current-year and next-year forecasts of inflation, growth and on the current account as a share of GDP, respectively. Hence, the panel estimates suggest that these types of policy initiatives play a significant role in influencing inflationary expectations, growth and balance projections. There's a stretch also to think that run up on oil prices was driving inflation expectations with a significant increase of about 0.6 (current-year) to 0.9% (next-year) and of about 1% (current-year) and 0.9% (next-year) for the current account as a percentage of GDP. The same variable has a significantbut non monotonic -impact on Real GDP growth. To sum up, the results of the second sample follow (almost) the same pattern as in the first sample. We observe a particular improvement related to the transparency score, which becomes significant and has a positive effect on next-year inflation forecasts. The output gap is apparently an important determinant of inflation forecast with statistically significant positive effects. Note that an important feature of our results shows that the signaling content of a multidimensional index (the transparency score of Dincer andEichengreen (2010, 2014)) and a one-multidimensional communication variable (for example the release of inflation/monetary policy report) is different and suggests that both types of transparency and communication tools are not substitutes.

Effect of Central bank transparency and communication on forecast accuracy

Significant coefficients on transparency and communication measures mean that those measures play a role in forecasting, but don't necessarily imply that transparency really enhances forecasting. Whether central bank transparency is beneficial or harmful, we need a further examination of the former on the reduction of forecast dispersion. To achieve this aim, we need a measure of the forecast accuracy by looking at magnitudes of deviations from benchmarks. For that purpose, one could follow [START_REF] Mankiw | Disagreement About Inflation Expectations[END_REF], [START_REF] Siklos | Sources of disagreement in inflation forecasts: A cross-country empirical investigation[END_REF], [START_REF] Ehrmann | The Role of Central Bank Transparency for guiding private sector forecasts[END_REF] and [START_REF] Dovern | Disagreement Among Forecasters in G7 Countries[END_REF] by analyzing the impact of transparency and communication indicators on forecasts dispersion through the width of the Interquartile (IQR) range 13 (difference between the third and quartile of observations). Doing this seems to be impractical because of the format of private forecasts used throughout the paper. In fact, that measure requires taking into account the disagreementcross sectional dispersion of individual forecastsabout the key economic indicators. Data on individual forecasts, however, are not available in the case of the Economist poll of forecasters. We choose then to work with the Mean Absolute Error (MAE). The latter is used to measure how close mean (consensus) forecasts are to the eventual outcomes.The Eq to be estimated is now given by the following expression:

    it it it it s it s x x E x x E                   1 1 ~ (6)
13 On could use also the standard deviation but IQR are still more robust to outliers than standard deviations.

Where

  x x E it s
 is the difference between the consensus forecast and its realization in absolute value.  contains the three variables of interest: Central bank transparency index of Dincer andEichengreen (2010, 2014), inflation report (labelled as comm) and inflation targeting dummy.  ~encompasses the macroeconomic controls: the absolute change in oil price (Europe Brent spot price). Eq.6 is estimated first for forecasts formed at month t of the current year s made during the current year s. The same equation is then replicated for forecasts formed at month t of the next year s+1 made in the current year s. In other terms:

                        s.

Results for the sample 2001-2006

Tables B.4.1-B.4.3 provide the parameter estimates of Eq.6. Transparency (T) and communication (comm) measures seem to influence significantly RGDP, and a bit the Current Account as a percentage of GDP, while the same measures don't affect inflation. The results of the other control variables show that the deviation of the macroeconomic forecasts from their realized values is indeed persistent. The own lag has statistically significant and sizable coefficient. We also compared the behavior of average absolute forecast errors in targeting and non-targeting countries. This is illustrated by the introduction of the dummy variable IT in the regression. The literature doesn't provide strong evidence that targeting regimes experience lower average absolute forecast errors (or Mean Absolute Error) relative to the non-targeting countries. For example, [START_REF] Johnson | The effect of inflation targeting on the behavior of expected inflation[END_REF] showed a decline in inflation forecasts, but not in errors or variance. [START_REF] Cechetti | Inflation targeting and private sector forecasts[END_REF] didn't find also any convincing evidence of reduction in the dispersion of inflation forecasts in a sample of 15 countries. Both papers focused only on the impact of inflation targeting regime adoption on inflation forecast errors and/or their variability.

Results for the sample 2001-2010

The second part of Tables B.4.1-B.4.3 shows the results of the same measures' impact on the accuracy of forecasts. Eq.6 allows for some persistence in absolute forecast errors by adding lagged values. The corresponding coefficient is highly significant in all cases. The results for the other control variables indicate that they have their expected signs. Oil price changes seem to affect the forecast accuracy of all our key economic indicators. It is clear that there are some cases where more transparency and communication lead to a reduction of the Mean Forecast Error (MAE) by exhibiting a negative coefficient. The reduction remains, however, insignificant in most cases. More precisely, those practices appear to influence a bit significantly RGDP and the current account as a percentage of GDP, respectively. While the same practices don't affect at all inflation mean forecast errors.

We have to be mindful that we don't extract a strong and effective amelioration of forecast accuracy in our econometric estimates. On the one hand, the powerful effect of transparency is somewhat absorbed because the score's level is already high for central banks considered in our sample while transparency shall be particularly powerful at low levels [START_REF] Ehrmann | The Role of Central Bank Transparency for guiding private sector forecasts[END_REF]. On the other hand, realizations must be conceived (normally) in a way against which ex ante expectations are compared to. We only observe forecast error ex post that might be less accurate under greater transparency.

Interpretation and further discussion

The puzzling relationship between central bank transparency and inflation forecast accuracy can be derived from further suspicious reasons and facts:

1. An important caveat in our study is that we tried to detect the effects and the influence of the overall transparency index on forecasts and forecast accuracy, respectively. The overall index is the preferred measure in the empirical analysis because it encapsulates all the dimensions of transparency. The literature provides furthermore, evidence that transparency about economic objective (A particular dimension of transparency according to the typology of [START_REF] Geraats | Central bank transparency[END_REF] has particularly important macroeconomic effects. That special sub-component of transparency has been subject to a test and the results of the corresponding estimations are reported in Table B.514 . We also take into account the endogenous aspect of that variable by regressing it on the rule of law15 . Eq.6 is estimated by instrumental variables (IV) and two-stages-least square (2SLS) to identify the causal effect of economic transparency. We find that this latter exerts a negative and significant influence on current-year inflation forecasts. The same subindex appears also to improve -though insignificantly-the forecast accuracy of inflation 16 . 2. While some evidence provided so far points to a powerful tool of central bank transparency and communication in affecting forecasts and their accuracy; the question turns to investigate whether there are limits to this role. In fact, van der Cruijsen et al.

(2010) found a non-linear effect of transparency on inflation through a quadratic form. They introduce interaction terms: lagged inflation times the transparency index and lagged inflation time the squared transparency index. The relationship is consistent with the existence of an optimal threshold above which more transparency turns to be harmful.

A particular level of 7.5 is found for OECD countries and that level is already exceeded by the central banks considered in our paper. 3. Monetary policy affects inflation with rather long lags (1.5 to 2 years); to reflect possible effects of enhanced central bank transparency (see [START_REF] Ehrmann | The Role of Central Bank Transparency for guiding private sector forecasts[END_REF]. 4. A possible explanation can be attributed to the inefficiency of the consensus forecasts which can be a result of overweighting old information.

A further examination of the fourth point forms the second purpose of this paper. We proceed to an analysis of the forecast revisions through their correlation with forecast errors. Such a statistical exercise can reveal important observations on how efficiently forecasters react to new information. Analyzing the dynamics of forecast revisions, has received particular attention by numerous studies17 , focusing first on developed countries (see [START_REF] Isiklar | How quickly do forecasters incorporate news? Evidence from cross-country surveys[END_REF]. More recent contributions add specific attention to emerging countries. [START_REF] Dovern | Information rigidities: Comparing average and individual forecasts for a large international panel[END_REF], among others, assessed the performance of forecasts in both advanced and emerging economies and arrived at the conclusion that developed countries incorporate information faster than the emerging countries. Yet, the difference is still small. They also observed that GDP consensus forecasts are updated every 6 months. This result concurs with that of [START_REF] Loungani | How accurate are private sector forecasts? Cross-country evidence from consensus forecasts of output growth[END_REF] and [START_REF] Loungani | Information rigidity in growth forecasts: Some cross-country evidence[END_REF] who found that it takes the forecasters 4-6 months to update their forecasts to fully reflect new information. Unlike previous papers, Chen et al. (2016, forthcoming) chose a sample covering Asian economies and showed that forecasters underreact to new information more in advanced economies than in emerging economies. Overall, they noted a forecast improvement from long to short horizons. In the case of interest rate, Middeldorp (2011, p.28) claimed that "it seems plausible that any information asymmetries between policy makers and professional forecasters are likely to be greatest in the short term…"

In our case, we follow Crowe (2010b) methodology and define the forecast error as the difference between the consensus forecast and the realization. The forecast revision (or forecast update) is given by the difference between the current forecast and the previous one. The consensus forecasts present the advantage of an explicit revision of 24 forecasts (with horizons varying from 1 to 24) for every target year that can be observed. To understand the structure of the publication of the monthly consensus forecast, more clearly, we consider the following comprehensive forecast made at the beginning of March of the year s for the target year s (current year). The realized (actual) value is known only 10 months later at the end of the year (h=10). In the same month, forecasters made their predictions of the target year s+1 (next year). The realized value is released 22 months-ahead (h=22) (see Figure A.9 for illustration). The results of the correlations between the forecast revisions and the forecast errors are displayed in Table B.6 in ascending horizon (h=1, 2, …., 24). For RGDP, the hypothesis of a negative correlation cannot be rejected for horizons between 10 and 16 months-ahead. The sign is still negative, but turns to be insignificant for the remaining horizons, suggesting an overall systematic underreaction to new information. In the case of inflation, the conclusions vary. For short horizons, the sign ranges between positive and negative, while still insignificant for the latter. The cut-off point up to which the rejection of the hypothesis of a negative and significant correlation between forecast errors and forecast revisions remains is at horizon=11. That behavior is restored starting from h=17. Finally, the forecasters don't incorporate new information properly into their prediction of the current account as a share of GDP especially at short horizons. The associated coefficient is highly significant. Turning, now, to the results of the inefficiency test across horizons; most of the correlation coefficients have negative signs but significant only for Real output growth. Altogether, the negative estimates mean underweighting new information (similarly overweighting older information); while the insignificance shows that the observed inefficiency cannot be fully attributed to information aggregation [START_REF] Crowe | Testing the Transparency Benefits of Inflation Targeting: Evidence from Private Sector Forecasts[END_REF].

Note that our sample spans a period that comprises two major events: the subprime mortgage crisis of 2007-07 and the financial crisis of 2008-09, both of which triggered the known global financial crisis. So, examining the forecast pattern (before and during the crisis) becomes a crucial exercise. As we did earlier, we re-estimate the pairwise correlations for the sub-sample Nov 2001-Dec 2006. The signs are negative mostly for inflation and RGDP, but less significant compared to the whole sample (especially for RGDP). Apparently, the forecast inefficiency is more pronounced during years with a pernicious financial crisis because forecastsers fail to adjust their predictions in a consistent manner in response to incoming information. This is almost in accordnance with previous studies that rejected efficiency for a wide range of countries based both on consensus forecasts [START_REF] Loungani | How accurate are private sector forecasts? Cross-country evidence from consensus forecasts of output growth[END_REF][START_REF] Isiklar | How quickly do forecasters incorporate news? Evidence from cross-country surveys[END_REF][START_REF] Ager | The accuracy and efficiency of the Consensus Forecasts: A further application and extension of the pooled approach[END_REF] and on individual forecasts [START_REF] Davies | A new framework for analyzing survey forecasts using three-dimensional panel data[END_REF][START_REF] Lahiri | Evolution of forecast disagreement in a Bayesian learning model[END_REF].

Conclusion

We have studied the extent to which central bank transparency and communication have contributed to influencing the expectations of economic agents, by testing their effects on the forecasts of key economic indicators and their corresponding absolute forecast errors, respectively using the Economist poll of forecasters. The findings of the paper indicate that there's a significant (overall) influence of transparency on private sector forecast formation. Forecasters in countries with more transparent and open central banks update their expectations in response to news. At the same time, when the question turns to appraise the contribution of transparency and communication measures in terms of enhancement of forecast accuracy, those practices seem not being always good. While there's a bit evidence of significant communication and/or adoption of inflation targeting regime on RGDP and the current account as a share of GDP, the absolute forecast errors of the inflation rate are not affected at all by the variables of interest considered in this study. A deeper interpretation of this fact can be added: More transparent public information can decrease the accuracy of the consensus forecast and better public information could lead to worse collective opinions. There's also appeal of more caution when using the consensus forecasts as proxies for expectations because they don't form the best (and the most efficient) estimate of the true state of the world. The correlation coefficient between the mean forecast and its revision, though negative, is significant only for RGDP, which means that inefficiency cannot be due only to information aggregation. It is worth noting that the second sample includes an important event which is the global financial crisis 2007-2008. The crisis might have the effect of underweighting new information by the forecasters and lead to an increase in the mean absolute forecast errors. However, this could not be totally attributed to such an event because we observe almost the same pattern followed by the first sample18 , ending before the crisis triggered in the econometric results. The effect of the crisis is, however, pronounced when analyzing statistically the forecast revisions. Overall, we think that there may be a mixture of reasons mentioned in the main text. An incoming research could focus on emerging countries and analyze empirically if their transparency and communication practices affect the forecast accuracy. While the problem of transparency indicator is solved as it is alreadyand generously-available for a large country sample, a handicap persists with the small size of the cross-country19 data set of the Economist poll of forecasters. Thereby, the use of a larger expectation survey data set becomes especially appealing. We can go ahead with a data collection from (www.fx4casts.com), recently explored by Miah et al. (2016, forthcoming)20 to test the efficiency of inflation expectations. In such a case; we expect there may have differences -though slight-in forecast smoothing (efficiency) between advanced and emerging economies [START_REF] Loungani | Information rigidity in growth forecasts: Some cross-country evidence[END_REF][START_REF] Dovern | Information rigidities: Comparing average and individual forecasts for a large international panel[END_REF]. Nonetheless, our results also suggest a potentially promising avenue for future research to understand deeply the major drivers of forecast errors and to better assess the forecast revision behavior by using the same database (The Economist poll of forecasters). Outgap : Output gap. *, **, *** imply statistical significance at 10, 5 and 1%, respectively. Regressions were run using Stata11. RGDP : lagged actual RGDP. *, **, *** imply statistical significance at 10, 5 and 1%, respectively. Regressions were run using Stata11. ) and of the next year made in current year (   ) and of the next year made in current year (

  s it s E inf   1 inf  s it s E   s it s E inf   1 inf  s it s E  -0.
  s it s E inf   1 inf  s it s E   s it s E inf   1 inf  s it s E  -0.
  s it s E inf   1 inf  s it s E   s it s E inf   1 inf  s it s E  0.
  s s it s E inf inf    1 1    s s it s E inf inf   s s it s E inf inf    1 1    s s it s E inf inf  0.
1 1    s s it s E inf inf ).
  1 1    s s it s E inf inf
). *, **, *** imply statistical significance at 10, 5 and 1%, respectively. Regressions were run using Stata11.

Note: This table provides the coefficient estimates from the regression model in Eq.5 estimated by FGLS corrected for both cross-section heteroscedasticity and contemporaneous correlation. Note: p-value between ().*, **, *** imply statistical significance at 10, 5 and 1%, respectively.The forecasts are for annual economic growth over the current and next calendar year: the forecast horizon (dated to the end of the forecast year) therefore varies from 1 to 24 months.

  Figure A.2 presents expected US growth for 2005, the expected US current account/GDP ratio for 2005 and US consumer price inflation for 2005 as predicted by the Economist poll of forecasters from March 2004 to February 2006.

Fig A. 3 .Fig A. 5 .

 35 Fig A.3. Realized versus inflation forecast of the current year: November 2001-December 2010 inf0: inflation forecast of the current year. infc0: actual inflation

  . *, **, *** imply statistical significance at the 10, 5 and 1%, respectively. Regressions were run using Stata11.

  price. *, **, *** imply statistical significance at 10, 5 and 1%, respectively. Regressions were run using Stata11.

  values of the regression on a constant and a rule of law. in the regression is the absolute forecast error of inflation of the current year made in the current year (

  Zstatistics are between parentheses. The data set contains monthly data from November 2001 to December 2010 for a panel of 6 countries. The dependent variable in the regression is the inflation forecast of the current year made in the current year ( , **, *** imply statistical significance at 10, 5 and 1%, respectively. Regressions were run using Stata11.

x x E x E s it s s it s it s

			:
	is	the	consensus	forecast	of	the	macroecono	mic	variable		of	the	current	year	s
	made	during	the	current	year	s.
	E	x	1	:
	is	the	consensus	forecast	of	the	macroecono	mic	variable	x	of	the	next	year	s	1
	made	during	the	current	year

Table B

 B

	Table B.3.1
	Transparency and inflation forecasts .1 Measures of Transparency and communication Current year		Next year	Current year	Next year
	T	Transparency score		Dincer and Eichengreen (2010, 2014)
				Available also at: http://eml.berkeley.edu/~eichengr/data.html
	IT	Dummy =1 starting the period of	Pétursson (2004)
		inflation targeting adoption and 0
		otherwise
	comm	Inflation/monetary policy report as	Central Bank websites (see Table B.2 for details)
		a dummy= if there's release of a
		report and 0 otherwise
	Other control variables
	Outgap	Output gap		OECD outlook database 96
				http://stats.oecd.org/index.aspx?queryid=51396#
	Unemp	Unemployment rate		OECD outlook database 96
				http://stats.oecd.org/index.aspx?queryid=51396#
	Oil	Europe Brent Spot price		St Louis Fed
				http://research.stlouisfed.org/fred2/series/DCOILWTICO/downloa
				ddata
	oil 	The absolute change on oil prices in the preceding month	St Louis Fed http://research.stlouisfed.org/fred2/series/DCOILWTICO/downloa
				ddata
	Inf -1	Actual lagged inflation (annual)	International Financial Statistics (IFS)
				http://elibrary-data.imf.org/DataExplorer.aspx
	RGDP -1	Actual lagged Real Gross		Economic Research Service
		Domestic Product (annual)		http://www.ers.usda.gov/data-products.aspx
	CC/GDP -1	Actual lagged current account as	http://www.economywatch.com/economic-statistics/economic-
		a percentage of GDP (annual)	indicators/Current_Account_Balance_Percentage_GDP/
	Governance Indicators		Worldwide Governance Indicators
				http://info.worldbank.org/governance/wgi/
	Table B.2
	Frequency of inflation/monetary policy report. Source: central bank websites
		Australia	Quarterly	Monetary policy statement
		Canada	Quarterly	Monetary policy report
		Sweden	Quarterly	Inflation report
		Switzerland	Quarterly	Bulletin
		United States Half-yearly Monetary policy report
		Japan	Monthly	Bulletin
		Denmark	Quarterly	Monetary policy review

  This table provides the coefficient estimates from the regression model in Eq.5 estimated by Feasible GLS corrected for both cross-section heteroscedasticity and contemporaneous correlation. Zstatistics are between parentheses. The data set contains monthly data from November 2001 to December 2006 (sample 1) for a panel of 7 countries and from November 2001 to December 2010 (sample 2) for a panel of 6 countries. The dependent variable in the regression is the inflation forecast of the current year made in the current (

										314***	-0.319** -0.303*** -0.304***	0.101	-0.071	0.016	0.103 0.363***	0.272	-0.028 0.373***	0.787***	0.210 0.445***	0.786***
										(-4.06)	(-2.06)	(-3.73	(-3.93)	(1.50	(-1.68)	(0.24)	(1.54)	(3.06)	(1.12)	(-0.22)	(3.18)	(9.55)	(1.23)	(5.22)	(9.67)
	it comm		-0.126*** -0.125***	-0.143** -0.124*** -0.098** -0.106*** -0.133***	-0.096** -0.355*** -0.351*** -0.242*** -0.354*** -0.291***	0.270*** -	0.263*** -	-0.288***
										(-2.63)	(-2.62)	(-2.47)	(-2.58)	(-2.48)	(-2.64)	(-3.27)	(-2.48)	(-4.55)	(-4.44)	(-3.06)	(-4.63)	(-5.33)	(-4.98)	(-5.17)	(-5.35)
											-0.0003				0.017			0.009	0.058***
	T
	it
											(-0.03)				(1.84)			(0.43)	(3.84)
	inf	it	s	1		it T			-0.013**				-0.018***	-0.055***	-0.037***
													(-2.06)				(-4.13)	(-5.28)	(-5.06)
												0.022				0.185***		0.600***	0.523***
	IT
		it
												(0.46)				(5.15)		(6.39)	(9.28)
										0.014*** 0.014*** 0.014***	0.014*** 0.011*** 0.011*** 0.012*** 0.011*** 0.008*** 0.008*** 0.008*** 0.009***	0.006*** 0.005*** 0.006***	0.007***
	Oil	t	1 
										(10.14)	(10.12)	(10.04)	(10.00)	(10.04)	(10.08)	(10.79)	(10.10)	(5.81)	(5.57)	(5.74)	(6.44)	(6.41)	(5.46)	(6.92)	(7.21)
	1 inf  s it		0.767*** 0.767*** 0.761***	0.889*** 0.628*** 0.623*** 0.607*** 0.804*** 0.488*** 0.487*** 0.438*** 1.059***	0.379*** 0.371*** 0.360***	0.772***
										(41.00)	(41.03)	(39.90)	(14.31)	(37.88)	(37.91)	(38.38)	(17.49)	(17.09)	(16.95)	(14.80)	(9.56)	(18.58)	(18.24)	(18.38)	(9.53)
	N°obs					420	420	420	420	350	350	350	350	660	660	660	660	552	552	552	552
	Sample			2001-2006								2001-2010
	Note:  s E inf	it		s	) year and of the next year made in current year (
	s	 inf	it	 1  s

E

).

Table B .3.1

 B Transparency and inflation forecasts (continued 1)

	Current year	Next year	Current year	Next year

  This table provides the coefficient estimates from the regression model in Eq.5 estimated by Feasible GLS corrected for both cross-section heteroscedasticity and contemporaneous correlation. Zstatistics are between parentheses. The data set contains monthly data from November 2001 to December 2006 (sample 1) for a panel of 7 countries and from November 2001 to December 2010 (sample 2) for a panel of 6 countries. The dependent variable in the regression is the inflation forecast of the current year made in the current ( , **, *** imply statistical significance at 10, 5 and 1%, respectively. Regressions were run using Stata11.

	203** -0.406*** (-1.99) (-3.51) -0.079* (-1.87) 0.030*** -0.022* (3.14) (-1.83) 0.171*** (9.94) 0.072*** (4.65) 0.009*** 0.013*** 0.007*** 0.111 -0.591**** (1.43) (-6.41) -0.070 -0.128*** -0.066** 0.012 -0.339*** 0.481*** -0.276*** (0.15) (-4.55) (7.82) (-3.32) -0.066** -0.083** -0.118*** (-1.60) (-2.85) (-2.04) (-2.29) (-2.55) (-3.06) 0.053*** 0.009* (8.56) (1.71) 0.234*** 0.030 0.375*** 0.145*** (5.06) (0.67) (11.74) (4.19) 0.205*** 0.194*** 0.219*** (10.87) (13.95) (14.85) 0.066*** 0.083*** 0.066*** (4.29) (7.77) (5.26) 0.013*** 0.006*** 0.011*** 0.004*** 0.011*** 0.006*** 0.011*** 0.010*** 0.011*** 0.065 -0.523*** -0.106 -1.052*** (0.43) (-3.97) (-0.78) (-6.24) -0.095** -0.101*** -0.265*** -0.206*** -0.093*** 0.153 (1.44) (-2.31) (-2.40) (-3.39) (-2.75) (-2.68) 0.013 -0.024* 0.047*** -0.021** -0.302*** (-3.50) -0.085*** (-2.61) (1.18) (-1.78) (5.78) (-2.24) 0.607*** 0.501*** (6.61) (5.61) 0.085*** 0.040* 0.011 (5.40) (1.72) (0.94) 0.112*** 0.158*** 0.179*** (6.38) (7.12) (13.57) 0.005*** 0.008*** 0.006*** 0.326*** -0.632*** (3.59) (-7.02) -0.091*** -0.072** (-2.53) (-2.33) 0.458*** 0.368*** (15.85) (12.26) 0.034** (2.35) 0.156*** (18.22) 0.008*** (6.97) (10.46) (5.44) (10.27) (6.50) (13.09) (4.61) (10.66) (3.96) (6.91) (6.85) (8.78) (4.57) (8.41) (5.57) (8.37) 0.630*** 0.745*** 0.577*** 0.743*** 0.456*** 0.563*** 0.411*** 0.585*** 0.566*** 0.649*** 0.402*** 0.425*** 0.408*** 0.442*** 0.365*** 0.415*** (28.34) (38.30) (24.53) (37.40) (26.48) (41.66) (23.65) (35.85) (19.39) (28.22) (11.17) (15.20) (19.38) (28.84 (17.26) (24.15) 420 420 420 420 350 350 350 350 654 654 654 654 546 546 546 546 2001-2006 2001-2010 Note:   it comm it T it IT 1  s it Outgap 1  s it Unemp 1  t Oil 1 inf  s it N°obs Sample s it s E inf ) year and of the next year made in current year (   1 inf  s it s E ). it IT : inflation targeting dummy, 1  t Oil : oil price, 1 inf  s it : lagged inflation, 1  s it Outgap : Output gap, 1  s it Unemp : Transparency and inflation forecasts (continued 2) unemployment rate. Table B.3.1 Current year Next year Current year Next year

it comm : communication variable, it T : Transparency variable, *

  This table provides the coefficient estimates from the regression model in Eq.5 estimated by Feasible GLS corrected for both cross-section heteroscedasticity and contemporaneous correlation. Z-statistics are between parentheses. The data set contains monthly data from November 2001 to December 2006 (sample 1) for a panel of 7 countries and from November 2001 to December 2010 (sample 2) for a panel of 6 countries. The dependent variable in the regression is the inflation forecast of the current year made in the current (  

											050	0.387***	0.265**	0.564***
											(0.65)	(5.96)	(1.96)	(6.11)
	it comm				-0.088**	-0.067**	-0.116***	-0.108***
											(-2.04)	(-2.01)	(-2.71)	(-2.93)
	inf	it	s	1		it T	0.011*	0.005	-0.016**	-0.026***
											(1.92)	(1.19)	(-2.32)	(-5.37)
	it Outgap	s	1 	0.168***	0.152***	0.112***	0.026**
											(9.09)	(10.00)	(6.47)	(2.18)
	Oil	t	1 					0.009***	0.008***	0.006***	0.007***
											(7.46)	(7.77)	(3.78)	(5.77)
	1 inf  s it				0.526***	0.460***	0.719***	0.691***
											(8.01)	(9.20)	(10.34)	(13.60)
	N°obs				420	420	654	546
	Sample		2001-2006	2001-2010
	Note: s it s E inf	) year and of the next year made in current year (  inf s E	it	 1 s 	).	it comm : communication
	variable,	it T : Transparency variable,	it IT : inflation targeting dummy,	1  Oil : oil price, t	1 inf  s it	: lagged
	inflation,				it	s	1 

Table B .

 B This table provides the coefficient estimates from the regression model in Eq.5 estimated by Feasible GLS corrected for both cross-section heteroscedasticity and contemporaneous correlation. Z-statistics are between parentheses. The data set contains monthly data from November 2001 to December 2006 (sample 1) for a panel of 7 countries and from November 2001 to December 2006 (sample 2) for a panel of 6 countries.The dependent variable in the regression is the RGDP forecast of the current year made in the current year (  

	3.2
	Transparency and RGDP forecasts
					Current year				Next year	Current year	Next year
							E	 s RGDP it	 s			E	 s RGDP it	 1  s	E	 s RGDP it		s	E	 s RGDP it	 1  s
						0.677***	1.04***	2.099***	2.27***	2.518***	1.533***	2.547***	2.528***
								(4.81)	(9.06)	(18.51)	(25.27)	(5.46)	(6.55)	(11.75)	(22.32)
	it comm				-0.041	-0.051	-0.206***	-0.187***	-0.163	-0.133 -0.378***	-0.333***
							(-0.61)	(-0.75)	(-3.86)	(-3.48)	(-1.17)	(-0.95)	(-5.21)	(-4.61)
						0.050***				0.042***	-0.088**	0.016
	T
	it
								(4.02)				(4.62)	(-2.09)	(0.92)
									0.030			0.271***	-0.021	0.280***
	IT
	it
									(0.40)			(4.87)	(-0.13)	(3.60)
	Oil	t	1 				0.003*	0.004**	-0.014***	-0.012***	0.001	0.000 -0.005***	-0.005***
								(1.83)	(1.98)	(-7.8)	(-6.49)	(0.51)	(0.10)	(-4.08)	(-3.93)
	it RGDP	s	1 	0.546*** 0.578***	0.391***	0.373***	0.220***	0.229***	0.089***	0.072***
							(18.66)	(17.68)	(14.63)	(13.69)	(6.40)	(6.55)	(5.63)	(4.30)
	N°obs					420		420		350	350	648	648	540	540
	Sample		2001-2006						2001-2010
	Note: s it s RGDP E	) and of the next year
	made in current year (  s RGDP it E	 1  s	).	it comm : communication variable,	it T : Transparency variable,	it IT : inflation targeting dummy,	Oil	t	1 
	: oil price,		it	s	1 

Table B .3.3

 B Transparency and CC/GDP forecasts This table provides the coefficient estimates from the regression model in Eq.5 estimated by Feasible GLS corrected for both cross-section heteroscedasticity and contemporaneous correlation. Z-statistics are between parentheses. The data set contains monthly data from November 2001 to December 2006 (sample 1) for a panel of 7 countries and from November 2001 to December 2010 (sample 2) for a panel of 6 countries. The dependent variable in the regression is the current account as a % of GDP forecast of the current year made in the current year (  

	Table B.4.1
	Transparency and inflation forecast accuracy
	Absolute forecast error	Current year Current year									Next year Next year	Current year Current year	Next year Next year
									E	s	 CC	/	it GDP	 s				E	s	 CC	/	it GDP	 1  s	E	s	 CC	/	it GDP	 s	E	s	 CC	/	it GDP	 1  s
												-0.229				-0.226*			0.0002	0.086	0.745***	-0.818***	1.002***	-0.909***
												(-1.36)				(-1.89)			(0.00)	(0.64)	(2.52)	(-7.17)	(2.88)	(-8.26)
	it comm						0.195***				0.279***	0.262***	0.300***	0.175*	0.349***	0.274**	0.513***
												(2.67)					(3.70)			(3.27)	(3.60)	(1.89)	(4.11)	(2.38)	(5.54)
												0.029*									0.036**	-0.160***	-0.176***
	T
	it
												(1.86)										(2.29)	(-6.03)	(-5.91)
																		0.605***				0.607***	-0.152*	0.160*
	IT
	it
																			(7.14)				(6.49)	(-1.93)	(1.70)
	Oil	t	1 						0.008***				0.012***			0.004	0.007***	0.010***	0.008***	0.009***	0.008***
												(3.19)					(4.60)			(1.24)	(2.32)	(6.59)	(5.58)	(5.18)	(6.22)
	it RGDP	s	1 				-0.323***				-0.279***	-0.344*** -0.455***
												(-7.85)				(-9.33)			(-7.19)	(-8.68)
	CC		/	it GDP	s	1 		0.917***				0.924***	0.867***	0.872***	0.978***	0.954***	0.952***	0.942***
												(89.57)				(94.44)			(68.71)	(75.69)	(100.06)	(11.30)	(78.26)	(97.38)
	N°obs								420						420			350	350	660	660	552	552
	Sample					2001-2006													2001-2010
	it Note: s s GDP CC E /	) and of the next year
	made in current year (  s CC E	/	it GDP	 1  s	).	it comm : communication variable,	it T : Transparency variable,	it IT : inflation targeting dummy,	1  Oil : oil t
	price,	1  RGDP : lagged actual RGDP, s it	CC	/	it GDP	s	1 	: lagged actual current account. *, **, *** imply statistical significance at 10, 5 and 1%, respectively.
	Regressions were run using Stata11.

  This table provides the coefficient estimates from the regression model in Eq.6 estimated by Feasible GLS corrected for both cross-section heteroscedasticity and contemporaneous correlation. Z-statistics are between parentheses. The data set contains monthly data from November 2001 to December 2006 (sample 1) for a panel of 7 countries and from November 2001 to December 2010 (sample 2) for a panel of 6 countries. The dependent variable in the regression is the absolute forecast error of inflation of the current year made in the current year (  

															081***	0.054* 0.075*** 0.024***	0.010 0.021** 0.075***	-0.001	0.095 0.022***	0.012	0.015
															(5.48)	(1.70)	(4.34)	(2.85)	(0.57)	(2.10)	(4.94)	(-0.02)	(3.81)	(2.13)	(0.36)	(0.77)
															-0.007	-0.007	-0.005	-0.004	-0.003	-0.003	0.010	0.012	0.008	0.004	0.005	0.004
	comm
							it
															(-0.56)	(-0.55)	(-0.40)	(-0.46)	(-0.39)	(-0.32)	(0.56)	(0.69)	(0.45)	(0.33)	(0.36)	(0.33)
															0.003	0.002	0.007	0.001
	T
	it
															(1.00)	(0.99)	(1.45)	(0.29)
	it IT										0.01	0.005	-0.022	0.007
															(0.76)	(0.55)	(-1.01)	(0.40)
	oil 	t	1 								-0.007*** -0.007*** -0.007***	-0.003* -0.003**	-0.003*	0.002	0.002	0.002 -0.007*** -0.007***	-0.007***
															(-2.43)	(-2.50)	(-2.42)	(-1.94)	(-1.98)	(-1.93)	(1.34)	(1.21)	(1.34)	(-4.76)	(-4.74)	(-4.78)
	E	s	 inf it	1		s		inf	s	0.802*** 0.796*** 0.800***	0.668*** 0.662*** 0.665***
															(27.91)	(27.35)	(27.67)	(22.79)	(22.43)	(22.62)
	E	s	 inf it	1 		s	1 		inf	s	1 	0.953*** 0.945*** 0.952***	0.992*** 0.991***	0.992***
															(98.53)	(90.50)	(98.31)	(104.31)	(102.06)	(103.08)
	N°obs									420	420	420	315	315	315	648	648	648	486	486	486
	Sample								2001-2006	2001-2010
	s Note: s it s E inf inf 

Table B .

 B This table provides the coefficient estimates from the regression model in Eq.6 estimated by Feasible GLS corrected for both cross-section heteroscedasticity and contemporaneous correlation. Z-statistics are between parentheses. The data set contains monthly data from November 2001 to December 2006 (sample 1) for a panel of 7 countries and from November 2001 to December 2010 (sample 2) for a panel of 6 countries. The dependent variable in the regression is the absolute forecast error of RGDP of the current year made in the current year ( This table provides the coefficient estimates from the regression model in Eq.6 estimated by Feasible GLS corrected for both cross-section heteroscedasticity and contemporaneous correlation. Z-statistics are between parentheses. The data set contains monthly data from November 2001 to December 2006 (sample 1) for a panel of 7 countries and from November 2001 to December 2010 (sample 2) for a panel of 6 countries. The dependent variable in the regression is the absolute forecast error of the current account as a % of GDP of the current year made This table provides the coefficient estimates from the regression model in Eq.6 estimated by 2SLS-Instrumental Variable. Z-statistics are between parentheses. The data set contains monthly data from November 2001 to December 2010 for a panel of 6 countries.

					4.2Transparency and RGDP forecast accuracy Table B.4.3 Table B.5
					Absolute Forecast error Transparency and CC/GDP forecast accuracy Current year Alternative estimations: Economic Transparency and inflation forecast accuracy Next year	Current year	Next year
	Absolute forecast error Absolute forecast error 				 RGDP it Current year s E Current year 0.104*** 0.304*** 0.23***  s s RGDP  Next year   s s it s GDP CC GDP CC E / /    s s it s E inf inf    it s E inf	s	 RGDP it Next year  s Inflation 1   RGDP 0.059* s 0.021 0.029** 0.076*** 1  s  s RGDP it E Current year  s RGDP s  Current year -0.149 0.141** 0.041***  RGDP E Next year  1  Next year  it s s 0.017   1 1    s s it s GDP CC GDP CC E / /   s s it s GDP CC GDP CC E / /   s GDP RGDP  1   s it CC E / 1    E 1 s inf   s it s E inf   1 1  s 0.022 GDP s CC / inf  s it s E	1 
			comm															(4.61) 0.019 0.084*** 1.848 (1.46)	(4.78) -0.005 1.049 0.044 (1.38)	(6.53) -0.007 -1.108 0.085*** -0.001 (1.85) -0.663  (1.34) -0.010 0.037 (-0.95) (-1.02)	(2.17) -0.010 0.094	(3.27 0.006 0.038	(-1.49) 0.012 -0.018 (2.39) 0.006	(3.20) -0.018 0.837*** 0.054*** (4.16)	(0.41) -0.017 0.025	(1.14) -0.017 0.584*** -0.006 (3.80)
						it
	it T it comm it IT it T  oil it IT 1   s RGDP 1  t it E 1   t oil  s s it t   T ˆ it eco T it ˆ oil   s E inf inf  1	1		s		s RGDP	(0.98) (3.89) -0.058*** -0.059*** -0.057*** (-0.23) (-0.30) (-0.83) (-0.76) --0.003 (0.63) (3.85) (1.26) -0.037 -0.164 0.109 comm (1.63) (-0.78) -0.038 it (-1.37) (0.99) 0.013*** (-2.35) (-1.04) -0.079*** 0.014 (-3.08) (-3.14) (-3.06) (-1.46) (-1.52) 0.004 -0.006 -0.378 0.287 eco T   it (-1.23) (1.10) (-2.85) (1.10) (0.61) (-1.14) -0.006 -0.003 -0.004 -0.000 -0.000 -0.000 0.008*** 0.006*** 0.008*** (0.257) (0.48) (0.24) 0.023** (1.23) (-0.19) -0.037 -0.043 (2.30) (-1.41) (-1.59) 0.008 -0.004 (-0.14) (0.86) -0.009 -0.002 0.007 0.006 -0.017*** -0.017*** 1 Oil  t (-1.44) (-0.67) (-0.75) (-0.02) (-0.03) (-0.02) (2.75) (2.44) (2.75 (-0.31) (-0.07) (1.27) (0.256) (-3.06) (-3.21) 0.834*** 0.715*** 0.707*** 0.853*** 0.840*** 0.853*** -0.005 -0.004 -0.005 -0.003 -0.003 -0.001 -0.003 0.609*** 0.581*** 1 inf  s it (-0.95) (-0.88) (-0.97) (-0.62) (-0.53) (-0.62) (-1.44) (16.05) (16.07)	(-1.15) (2.88) -0.050* -0.372*** (-4.89) (-1.90) -0.329*** (-4.15) -0.042* 0.014*** 0.010*** (-5.80) (1.60) (9.35) -0.003 0.509*** (-1.31) (18.59)	(-1.06) (0.28) 0.011 0.002 (0.55) (0.31) -0.001 (-0.17) -0.010*** -0.010*** (-1.03) -0.289*** (-0.21) 0.009 (-5.26) (0.27) 0.066 (1.13) 0.022 (1.21) 0.034 0.007*** (-5.75) (0.92) (6.58) (5.82) -0.006** -0.005** 0.382*** (-1.99) (-1.96) (18.64)
	E	s	 GDP it / 1 1   s inf RGDP   1 s it s CC   1 1 s  E s it inf   E	  s	1 CC  	s GDP RGDP /	s	1 	(31.30) 0.911*** 0.809*** (21.83) (46.24) (45.74)	(21.56) 0.911*** 0.951*** 0.954** 0.954*** 0.992*** 0.999*** (46.23) (65.86) (56.68)	(42.22)	(40.00) 0.932*** (42.22) (62.84)	0.975*** 0.975*** 0.975*** 0.927*** (61.46)
	E Sample N°obs	s	N°obs GDP it CC  /	1 		s	1 		CC	/	1  2001-2010 s GDP 648	420	420 648	420 486	(91.08) (92.52) 315 315 0.972*** 0.976*** (93.60) 315 486 N°obs Sample	648 0.972***	648 2001-2010	648	(194.67) (184.10) 486 486 0.979*** 654	(194.81) 486 0.976*** 546
	Note:		Sample															2001-2006	(82.07)	(78.95)	2001-2010 (73.68)	(101.32)	(95.12)
			N°obs Sample Note:  420 2001-2006 s RGDP it E variable, it T : Transparency variable, it IT : inflation targeting dummy, 420 420  s s RGDP  ) and of the next year made in current year ( 315 315 315 2001-2010  RGDP 648  E it s 1   t oil : lagged oil price. *, **, *** imply statistical significance at 10, 5 and 1%, respectively. 648 486 1 1    s RGDP s ). it comm : communication Regressions were run using Stata11. Note: in the current year (   s s it s GDP CC GDP CC E / /  ) and of the next year made in current year (   1 1    s s it s GDP CC GDP CC E / it it / ). comm : communication variable, T : Transparency 486
			variable,	it IT : inflation targeting dummy,	 oil	t	1 	: lagged oil price. *, **, *** imply statistical significance at 10, 5 and 1%, respectively. Regressions were run using Stata11.

Table B .6

 B Results of the correlation between mean forecast error and forecast revision: per horizon and pooled

	horizon	Inflation	RGDP	CC/GDP	Inflation	RGDP	CC/GDP
			2001-2006			2001-2010
	h=1	-0.131	-0.306*	-0.051	0.075	-0.222*	-0.287**
		(0.445)	(0.069)	(0.765)	(0.955)	(0.088)	(0.026)
	h=2	-0.041	0.210	-0.238	0.700***	0.179	-0.213
		(0.828)	(0.265)	(0.204)	(0.000)	(0.195)	(0.121)
	h=3	-0.138	-0.027	0.016	0.185	-0.224*	-0.344***
		(0.467)	(0.885)	(0.931)	(0.181)	(0.102)	(0.01)
	h=4	-0.295	-0.319*	-0.106	-0.077	-0.316**	0.049
		(0.113)	(0.085)	(0.578)	(0.577)	(0.02)	(0.725)
	h=5	0.021	0.215	0.363**	0.753***	0.109	0.265*
		(0.910)	(0.254)	(0.049)	(0.000)	(0.433)	(0.052)
	h=6	-0.137	0.175	-0.073	-0.067	-0.180	-0.078
		(0.469)	(0.355)	(0.701)	(0.631)	(0.192)	(0.573)
	h=7	-0.143	0.199	0.176	-0.217	-0.174	-0.046
		(0.449)	(0.291)	(0.353)	(0.115)	(0.208)	(0.738)
	h=8	-0.371**	0.301	-0.108	-0.005	0.133	0.062
		(0.043)	(0.106)	(0.568)	(0.972)	(0.414)	(0.653)
	h=9	-0.165	0.189	-0.198	-0.158	0.021	0.145
		(0.383)	(0.316)	(0.294)	(0.253)	(0.882)	(0.295)
	h=10	-0.297	0.028	0.173	0.420***	-0.299**	0.248*
		(0.110)	(0.883)	(0.359)	(0.001)	(0.028)	(0.07)
	h=11	0.151	0.043	-0.363**	-0.173	-0.308**	0.068
		(4.23)	(0.822)	(0.049)	(0.211)	(0.023)	(0.621)
	h=12	0.297	-0.186	0.064	-0.260*	-0.506***	-0.160
		(0.111)	(0.324)	(0.738)	(0.057)	(0.000)	(0.247)
	Current year	0.067	-0.018	-0.012	-0.060	-0.205***	0.018
	(pooled)	(0.198)	(0.731)	(0.816)	(0.124)	(0.000)	(0.633)
	h=13	-0.085	0.078	-0.289*	-0.260**	-0.415***	-0.162
		(0.628)	(0.651)	(0.087)	(0.046)	(0.001)	(0.216)
	h=14	0.099	-0.432**	-0.009	-0.229*	-0.328**	-0.043
		(0.600)	(0.017)	(0.960)	(0.096)	(0.015)	(0.757)
	h=15	0.062	-0.501***	-0.047	0.080	-0.679***	0.062
		(0.742)	(0.004)	(0.803)	(0.566)	(0.000)	(0.656)
	h=16	-0.195	-0.111	-0.245	-0.311*	-0.415***	0.054
		(0.301)	(0.558)	(0.191)	(0.022)	(0.002)	(0.699)
	h=17	0.105	-0.024	0.324*	0.547***	-0.103	0.401***
		(0.580)	(0.898)	(0.080)	(0.000)	(0.460)	(0.002)
	h=18	-0.154	0.298	-0.207	0.133	-0.167	-0.046
		(0.414)	(0.110)	(0.271)	(0.337)	(0.226)	(0.738)
	h=19	-0.165	-0.006	0.066	0.188	0.005	0.062
		(0.381)	(0.973)	(0.730)	(0.172)	(0.972)	(0.653)
	h=20	-0.173	0.261	0.004	0.226*	-0.302**	0.191
		(0.360)	(0.163)	(0.983)	(0.100)	(0.026)	(0.166)
	h=21	-0.300*	0.192	-0.114	0.118	-0.025	0.166
		(0.107)	(0.308)	(0.547)	(0.395)	(0.857)	(0.228)
	Next year	-0.048	0.0185	-0.037	0.048	-0.210***	0.124***
	(pooled)	(0.426)	(0.758)	(0.540)	(0.281)	(0.000)	(0.006)

Most of the related papers use data from Consensus Economics, Blue ship Indicators, Survey of Professional Forecasters, Wall Street Journal…, etc

We take a look further at some papers in the empirical analysis (see section.3).

Inflation and GDP are the most variables subject to analysis in previous studies.

We mean central bank transparency and communication variables.

For instance,[START_REF] Siklos | Sources of disagreement in inflation forecasts: A cross-country empirical investigation[END_REF] found that transparency increases the inflation forecast disagreement in a cross-country study of 9 economies

They formalized the Keynesian beauty contest model.

The model assumes that agents form expectations so that they minimize a loss function, which is a weighted sum of the mean squared forecast error and the mean squared deviation of individual forecasts from the consensus. In other terms, not only agents are concerned about the fundamentals, but also other agents' behavior. The argument has been subject to further discussion and developments in many papers both from theoretical and experimental side.

Although we present both theoretcial arguments, we rely more on the work of[START_REF] Morris | The Social Value of Public Information[END_REF] as the landmark that best fits the context of central bank policies and monetary policy (see[START_REF] Morris | Coordinating expectations in monetary policy[END_REF].

[START_REF] Crowe | Consensus forecasts and inefficient information aggregation[END_REF] considered a purely public signal and an imperfectly correlated private signal.

We dropped countries for which the transparency score is not available. Note that after 2006, Denmark is not listed in the set of economies considered by The Economist. Hence, the second sample will comprise only the six remaining countries. Furthermore, the length of both samples is affected by the absence of data prior to 2001.

[START_REF] Dincer | Central Bank Transparency: Causes, Consequences and Updates[END_REF] updated the transparency index until 2006 and then they re-updated it until 2010 in their second paper[START_REF] Dincer | Central Bank transparency and independence: Updates and new measures[END_REF]. The overall score takes values between 0 and 15.

We follow[START_REF] Ehrmann | The Role of Central Bank Transparency for guiding private sector forecasts[END_REF] and[START_REF] Hubert | Disentangling qualitative and quantitative central bank influence[END_REF].

These estimates shall be considered as a robustness check.

Other governance indicators are also tested as instruments. They don'tm howeverm improve the regression fit.

The coefficient sign of both overall and economic transparency becomes negative.

Note that those studies differentiate between the dynamics of individual forecast revisions vs consensus foerecast revisions. Therefore, the associated results are dependent on whether individual forecasts or consensus forecasts are considered in the analysis.

It gives a supplementary justification of the use of two samples in our empirical analysis.

The existing data are relative to advanced economies.

The authors focused on the question of the efficiency of inflation expectations and arrive at the conclusion of forecasts' unbiaisedness while controlling for other sources of information (monetary supply, oil price) for 18 emerging economies.

Acknowledgements

We thank the participants of 10 ème Colloque International ISG Sousse 2015 for useful feedback on an earlier draft. We are grateful to the editor and anonymous referees for comments that significantly improved the manuscript.

Data and their related sources Variable Description Source Macroeconomic forecasts Inflation Percentage increase in Consumer Price Index Economic and Financial indicators http://www.economist.com/ or http://www.oecd.org/eco/surveys/18635847.htm. RGDP Real Gross Domestic Product Economic and Financial indicators http://www.economist.com/ or http://www.oecd.org/eco/surveys/18635847.htm. CC/RGDP Current account as a percentage of GDP Economic and Financial indicators http://www.economist.com/ or http://www.oecd.org/eco/surveys/18635847.htm.

Appendix

A. Figures