Bayesian Analysis of Linear Inverse Problems with Applications in Economics and Finance

In my PhD thesis I propose a Bayesian nonparametric estimation method for structural econometric models where the functional parameter of interest describes the economic agent's behavior. The structural parameter is characterized as the solution of a functional equation, or by using more technical words, as the solution of an inverse problem that can be either ill-posed or well-posed. From a Bayesian point of view, the parameter of interest is a random function and the solution to the inference problem is the posterior distribution of this parameter. A regular version of the posterior distribution in functional spaces is characterized. However, the infinite dimension of the considered spaces causes a problem of non continuity of the solution and then a problem of inconsistency, from a frequentist point of view, of the posterior distribution (i.e. problem of ill-posedness). The contribution of this essay is to propose new methods to deal with this problem of ill-posedness. The first one consists in adopting a Tikhonov regularization scheme in the construction of the posterior distribution so that I end up with a new object that I call regularized posterior distribution and that I guess it is solution of the inverse problem. The second approach consists in specifying a prior distribution on the parameter of interest of the g-prior type. Then, I detect a class of models for which the prior distribution is able to correct for the ill-posedness also in infinite dimensional problems. I study asymptotic properties of these proposed solutions and I prove that, under some regularity condition satisfied by the true value of the parameter of interest, they are consistent in a "frequentist" sense. Once I have set the general theory, I apply my bayesian nonparametric methodology to different estimation problems. First, I apply this estimator to deconvolution and to hazard rate, density and regression estimation. Then, I consider the estimation of an Instrumental Regression that is useful in micro-econometrics when we have to deal with problems of endogeneity. Finally, I develop an application in finance: I get the bayesian estimator for the equilibrium asset pricing functional by using the Euler equation defined in the Lucas'(1978) tree-type models.

Quantitative evaluation of household nutrition patterns: an econometric assessment of the UK 5-a-day impact on fruit and vegetable consumption

The present work provides an ex-post assessment of the UK 5-a-day information campaign where the positive effects of information on consumption levels are disentangled from the potentially conflicting price dynamics. A model-based estimate of the counterfactual (no-intervention) scenario is computed using data from the Expenditure and Food Survey between 2002 and 2006. For this purpose fruit and vegetable demand is modelled employing Quadratic Almost Ideal Demand System (QUAIDS) specification with demographic effects and controlling for potential endogeneity of prices and total food expenditure.

Essays in Household and Demographic Economics

This dissertation consists of three empirical studies that are believed to provide new contributions to the literature exploring the determinants of children/adolescents achievement test scores (Chapter 2), adolescent health risk behaviors (Chapter 3), and children time use patterns (Chapter 4). The second and third studies look at the separate roles of fathers and of mothers in influencing outcomes, wherein parental time is the resource input of interest quantitatively measured and directly derived from time diaries. The last chapter looks at the time allocation of children and how it varies according to child and household characteristics.

Multilevel Analysis in Household Survey: An Application to Health Condition Data

The aim of this thesis is to apply multilevel regression model in context of household surveys. Hierarchical structure in this type of data is characterized by many small groups. In last years comparative and multilevel analysis in the field of perceived health have grown in size. The purpose of this thesis is to develop a multilevel analysis with three level of hierarchy for Physical Component Summary outcome to: evaluate magnitude of within and between variance at each level (individual, household and municipality); explore which covariates affect on perceived physical health at each level; compare model-based and design-based approach in order to establish informativeness of sampling design; estimate a quantile regression for hierarchical data. The target population are the Italian residents aged 18 years and older. Our study shows a high degree of homogeneity within level 1 units belonging from the same group, with an intraclass correlation of 27% in a level-2 null model. Almost all variance is explained by level 1 covariates. In fact, in our model the explanatory variables having more impact on the outcome are disability, unable to work, age and chronic diseases (18 pathologies). An additional analysis are performed by using novel procedure of analysis :"Linear Quantile Mixed Model", named "Multilevel Linear Quantile Regression", estimate. This give us the possibility to describe more generally the conditional distribution of the response through the estimation of its quantiles, while accounting for the dependence among the observations. This has represented a great advantage of our models with respect to classic multilevel regression. The median regression with random effects reveals to be more efficient than the mean regression in representation of the outcome central tendency. A more detailed analysis of the conditional distribution of the response on other quantiles highlighted a differential effect of some covariate along the distribution.

Essays in Media and Finance

This Ph.D. thesis consists in three research papers focused on the relationship between media industry and the financial sector. The importance of a correct understanding what is the effect of media on financial markets is becoming increasingly important as long as fully informed markets hypothesis has been challenged. Therefore, if financial markets do not have access to complete information, the importance of information professionals, the media, follows. On the other side, another challenge for economic and finance scholar is to understand how financial features are able to influence media and to condition information disclosure. The main aim of this Ph.D. dissertation is to contribute to a better comprehension for both the phenomena. The first paper analyzes the effects of owning equity shares in a newspaper- publishing firm. The main findings show how for a firm being part of the ownership structure of a media firm ends to receive more and better coverage. This confirms the view in which owning a media outlet is a source of conflicts of interest. The second paper focuses on the effect of media-delivered information on financial markets. In the framework of IPO in the U.S. market, we found empirical evidence of a significant effect of the media role in the IPO pricing. Specifically, increasing the quantity and the quality of the coverage increases the first-day returns (i.e. the underpricing). Finally the third paper tries to summarize what has been done in studying the relationship between media and financial industries, putting together contributes from economic, business, and financial scholars. The main finding of this dissertation is therefore to have underlined the importance and the effectiveness of the relationship between media industry and the financial sector, contributing to the stream of research that investigates about the media role and media effectiveness in the financial and business sectors.