Bayesian applications in dynamic econometric models

The purpose of this thesis is to provide a few new ideas to the field of Bayesian econometrics. In particular, the focus of the thesis is on analyzing dynamic econometric models. In the first essay, we provide an easily implementable method for the Bayesian analysis of a simple hybrid DSGE model of Clarida et al. (1999). The forecasting properties of the model are tested against commonly used forecasting tools, such as Bayesian VARs and naïve forecasts based on univariate random walks. In particular, the predictability of three key macroeconomic-variables, inflation, short-term nominal interest rate and a measure of output gap, are studied using quarterly ex post and real-time U.S. data.Our…

Is there support for the sticky information models in the Michigan inflation expectation data?

Robustness of the risk–return relationship in the U.S. stock market

Abstract Using GARCH-in-Mean models, we study the robustness of the risk–return relationship in monthly U.S. stock market returns (1928:1–2004:12) with respect to the specification of the conditional mean equation. The issue is important because in this commonly used framework, unnecessarily including an intercept is known to distort conclusions. The existence of the relationship is relatively robust, but its strength depends on the prior belief concerning the intercept. The latter applies in particular to the first half of the sample, where also the coefficient of the relative risk aversion is smaller and the equity premium greater than in the latter half.

Bayesian two-stage regression with parametric heteroscedasticity

In this paper, we expand Kleibergen and Zivot's (2003) Bayesian two-stage (B2S) model by allowing for unequal variances. Our choice for modeling heteroscedasticity is a fully Bayesian parametric approach. As an application, we present a cross-country Cobb–Douglas production function estimation.

A Naïve Sticky Information Model of Households’ Inflation Expectations

This paper provides a simple epidemiology model where households, when forming their inflation expectations, rationally adopt the past release of inflation with certain probability rather than the forward-looking newspaper forecast as suggested in Carroll [2003, Macroeconomic Expectations of Households and Professional Forecasters, Quarterly Journal of Economics, 118, 269-298]. The posterior model probabilities based on the Michigan survey data strongly support the proposed model. We also extend the agent-based epidemiology model by deriving for it a simple adaptation, which is suitable for estimation. Our results show that this model is able to capture the heterogeneity in households’ expe…

Modelling the General Public's Inflation Expectations Using the Michigan Survey Data

In this article we discuss a few models developed to explain the general public's inflation expectations formation and provide some relevant estimation results. Furthermore, we suggest a simple Bayesian learning model which could explain the expectations formation process on the individual level. When the model is aggregated to the population level it could explain not only the mean values, but also the variance of the public's inflation expectations. The estimation results of the mean and variance equations seem to be consistent with the results of the questionnaire studies in which the respondents were asked to report their thoughts and opinions about inflation.