This dissertation collects three independent essays in the area of Macroeconomics and Macroeconomic forecasting. The first chapter introduces and motivates the three essays. Chapter 2 highlights a serious problem of the Bayesian vector autoregressive (BVAR) models with Litterman???s prior cannot be used to get accurate forecasts of the driftless variables in a mixed drift models. BVAR models with Litterman???s prior, because of the diffuse prior on the constant, do not perform well in the long-run forecasting of I(1) variables either, if they have no drift. This is interesting as in practice most of the macro models include both drift and driftless variables. One solution to this problem is using the Bewley (1979) transformation to impose zero drift to driftless variables in a mixed drift VAR models. A novel feature of this chapter is the use of g-prior in BVAR models to alleviate poor estimation of drift parameters of the Traditional BVAR model. Chapter 3 deals with another possible explanation for the poor performance of the Traditional BVAR models in inflation forecasting. BVAR with Litterman???s prior have the disadvantage of a lack of robustness to deterministic shifts, exacerbated by the ill-determination of the intercept. Several structural break tests show that Australian inflation has breaks in the mean. Chapter 3 uses the Kalman filter to allow parameters to vary over time. The novelty of this chapter is modifying the standard BVAR model, where deterministic components evolve over time. Moreover, this chapter set aside the assumption of diagonality in the prior variance-covariance. Hence, another novelty of this chapter is using a BVAR model with modified non-diagonal variance-covariance matrix similar to the g-prior, where the deterministic components are the only source of variation, to forecast Australian inflation. Chapter 4 moves onto DSGE models and estimates a partially microfunded small-open economy (SOE) New-Keynesian model of the Australian economy. In this chapter, structural parameters of the rest of world (ROW), SOE, and closed economy, are estimated using Australian data as the small economy, and the US as the ROW, with the full information maximum likelihood.

This dissertation collects three independent essays in the area of Macroeconomics and Macroeconomic forecasting. The first chapter introduces and motivates the three essays. Chapter 2 highlights a serious problem of the Bayesian vector autoregressive (BVAR) models with Litterman???s prior cannot be used to get accurate forecasts of the driftless variables in a mixed drift models. BVAR models with Litterman???s prior, because of the diffuse prior on the constant, do not perform well in the long-run forecasting of I(1) variables either, if they have no drift. This is interesting as in practice most of the macro models include both drift and driftless variables. One solution to this problem is using the Bewley (1979) transformation to impose zero drift to driftless variables in a mixed drift VAR models. A novel feature of this chapter is the use of g-prior in BVAR models to alleviate poor estimation of drift parameters of the Traditional BVAR model. Chapter 3 deals with another possible explanation for the poor performance of the Traditional BVAR models in inflation forecasting. BVAR with Litterman???s prior have the disadvantage of a lack of robustness to deterministic shifts, exacerbated by the ill-determination of the intercept. Several structural break tests show that Australian inflation has breaks in the mean. Chapter 3 uses the Kalman filter to allow parameters to vary over time. The novelty of this chapter is modifying the standard BVAR model, where deterministic components evolve over time. Moreover, this chapter set aside the assumption of diagonality in the prior variance-covariance. Hence, another novelty of this chapter is using a BVAR model with modified non-diagonal variance-covariance matrix similar to the g-prior, where the deterministic components are the only source of variation, to forecast Australian inflation. Chapter 4 moves onto DSGE models and estimates a partially microfunded small-open economy (SOE) New-Keynesian model of the Australian economy. In this chapter, structural parameters of the rest of world (ROW), SOE, and closed economy, are estimated using Australian data as the small economy, and the US as the ROW, with the full information maximum likelihood.

Can we fruitfully use the same macroeconomic model to forecast and to perform policy analysis? There is a tension between a model’s ability to forecast accurately and its ability to tell a theoretically consistent story. The aim of this dissertation is to propose ways to soothe this tension, combining structural and reduced-form models in order to have models that can effectively do both. / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished

This thesis contains three essays covering different topics in the field of statistics<p>and econometrics of survey data. Chapters one and two analyse two aspects<p>of the Survey of Professional Forecasters (SPF hereafter) dataset. This survey<p>provides a large information on macroeconomic expectations done by the professional<p>forecasters and offers an opportunity to exploit a rich information set.<p>But it poses a challenge on how to extract the relevant information in a proper<p>way. The last chapter addresses the issue of analyzing the opinions on the euro<p>reported in the Flash Eurobaromenter dataset.<p>The first chapter Measuring Uncertainty and Disagreement in the European<p>Survey of Professional Forecasters proposes a density forecast methodology based<p>on the piecewise linear approximation of the individual’s forecasting histograms,<p>to measure uncertainty and disagreement of the professional forecasters. Since<p>1960 with the introduction of the SPF in the US, it has been clear that they were a<p>useful source of information to address the issue on how to measure disagreement<p>and uncertainty, without relying on macroeconomic or time series models. Direct<p>measures of uncertainty are seldom available, whereas many surveys report point<p>forecasts from a number of individual respondents. There has been a long tradition<p>of using measures of the dispersion of individual respondents’ point forecasts<p>(disagreement or consensus) as proxies for uncertainty. Unlike other surveys, the<p>SPF represents an exception. It directly asks for the point forecast, and for the<p>probability distribution, in the form of histogram, associated with the macro variables<p>of interest. An important issue that should be considered concerns how to<p>approximate individual probability densities and get accurate individual results<p>for disagreement and uncertainty before computing the aggregate measures. In<p>contrast to Zarnowitz and Lambros (1987), and Giordani and Soderlind (2003) we<p>overcome the problem associated with distributional assumptions of probability<p>density forecasts by using a non parametric approach that, instead of assuming<p>a functional form for the individual probability law, approximates the histogram<p>by a piecewise linear function. In addition, and unlike earlier works that focus on<p>US data, we employ European data, considering gross domestic product (GDP),<p>inflation and unemployment.<p>The second chapter Optimal Combination of Survey Forecasts is based on<p>a joint work with Christine De Mol and Domenico Giannone. It proposes an<p>approach to optimally combine survey forecasts, exploiting the whole covariance<p>structure among forecasters. There is a vast literature on forecast combination<p>methods, advocating their usefulness both from the theoretical and empirical<p>points of view (see e.g. the recent review by Timmermann (2006)). Surprisingly,<p>it appears that simple methods tend to outperform more sophisticated ones, as<p>shown for example by Genre et al. (2010) on the combination of the forecasts in<p>the SPF conducted by the European Central Bank (ECB). The main conclusion of<p>several studies is that the simple equal-weighted average constitutes a benchmark<p>that is hard to improve upon. In contrast to a great part of the literature which<p>does not exploit the correlation among forecasters, we take into account the full<p>covariance structure and we determine the optimal weights for the combination<p>of point forecasts as the minimizers of the mean squared forecast error (MSFE),<p>under the constraint that these weights are nonnegative and sum to one. We<p>compare our combination scheme with other methodologies in terms of forecasting<p>performance. Results show that the proposed optimal combination scheme is an<p>appropriate methodology to combine survey forecasts.<p>The literature on point forecast combination has been widely developed, however<p>there are fewer studies analyzing the issue for combination density forecast.<p>We extend our work considering the density forecasts combination. Moving from<p>the main results presented in Hall and Mitchell (2007), we propose an iterative<p>algorithm for computing the density weights which maximize the average logarithmic<p>score over the sample period. The empirical application is made for the<p>European GDP and inflation forecasts. Results suggest that optimal weights,<p>obtained via an iterative algorithm outperform the equal-weighted used by the<p>ECB density combinations.<p>The third chapter entitled Opinion surveys on the euro: a multilevel multinomial<p>logistic analysis outlines the multilevel aspects related to public attitudes<p>toward the euro. This work was motivated by the on-going debate whether the<p>perception of the euro among European citizenships after ten years from its introduction<p>was positive or negative. The aim of this work is, therefore, to disentangle<p>the issue of public attitudes considering either individual socio-demographic characteristics<p>and macroeconomic features of each country, counting each of them<p>as two separate levels in a single analysis. Considering a hierarchical structure<p>represents an advantage as it models within-country as well as between-country<p>relations using a single analysis. The multilevel analysis allows the consideration<p>of the existence of dependence between individuals within countries induced by<p>unobserved heterogeneity between countries, i.e. we include in the estimation<p>specific country characteristics not directly observable. In this chapter we empirically<p>investigate which individual characteristics and country specificities are<p>most important and affect the perception of the euro. The attitudes toward the<p>euro vary across individuals and countries, and are driven by personal considerations<p>based on the benefits and costs of using the single currency. Individual<p>features, such as a high level of education or living in a metropolitan area, have<p>a positive impact on the perception of the euro. Moreover, the country-specific<p>economic condition can influence individuals attitudes. / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished