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

In the first chapter of this thesis, I use the smooth ambiguity model developed by Klibanoff, Marinacci, and Mukerji (2005) to define the concepts of ambiguity and uncertainty premia in a way analogous to what Pratt (1964) did in the risk theory literature. I show that these concepts may be useful to quantify the effect ambiguity has on the welfare of economic agents. I also define several other concepts such as the unambiguous probability equivalent or the ambiguous utility premium, provide local approximations of these different premia and show the link that exists between them when comparing different degrees of ambiguity aversion not only in the small, but also in the large. <p><p>In the second chapter, I analyze the effect of ambiguity on self-insurance and self-protection, that are tools used to deal with the uncertainty of facing a monetary loss when market insurance is not available (in the self-insurance model, the decision maker has the opportunity to furnish an effort to reduce the size of the loss occurring in the bad state of the world, while in the self-protection – or prevention – model, the effort reduces the probability of being in the bad state). <p>In a short note, in the context of a two-period model I first examine the links between risk-aversion, prudence and self-insurance/self-protection activities under risk. Contrary to the results obtained in the static one-period model, I show that the impacts of prudence and of risk-aversion go in the same direction and generate a higher level of prevention in the more usual situations. I also show that the results concerning self-insurance in a single period framework may be easily extended to a two-period context. <p>I then consider two-period self-insurance and self-protection models in the presence of ambiguity and analyze the effect of ambiguity aversion. I show that in most common situations, ambiguity prudence is a sufficient condition to observe an increase in the level of effort. I propose an interpretation of the model in the context of climate change, so that self-insurance and self-protection are respectively seen as adaptation and mitigation efforts a policy-maker should provide to deal with an uncertain catastrophic event, and interpret the results obtained as an expression of the Precautionary Principle. <p><p>In the third chapter, I introduce the economic theory developed to deal with ambiguity in the context of medical decision-making. I show that, under diagnostic uncertainty, an increase in ambiguity aversion always leads a physician whose goal is to act in the best interest of his patient, to choose a higher level of treatment. In the context of a dichotomic choice (treatment versus no treatment), this result implies that taking into account the attitude agents generally manifest towards ambiguity may induce a physician to change his decision by opting for treatment more often. I further show that under therapeutic uncertainty, the opposite happens, i.e. an ambiguity averse physician may eventually choose not to treat a patient who would have been treated under ambiguity neutrality. <p> / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished