Title page for ETD etd-07252012-123116

Type of Document Dissertation
Author Amini Moghadam, Shahram
Author's Email Address shahram@vt.edu
URN etd-07252012-123116
Title Model Uncertainty & Model Averaging Techniques
Degree PhD
Department Economics (Science)
Advisory Committee
Advisor Name Title
Ashley, Richard A. Committee Co-Chair
Parmeter, Christopher F. Committee Co-Chair
Cothren, Richard D. Committee Member
Tsang, Kwok Ping Committee Member
  • Frequentist
  • Bayesian
  • Jackknife
  • Model Averaging
  • Mallows
  • Nonparametric
Date of Defense 2012-07-16
Availability unrestricted
The primary aim of this research is to shed more light on the issue of model uncertainty in applied econometrics in general and cross-country growth as well as happiness and well-being regressions in particular. Model uncertainty consists of three main types: theory uncertainty, focusing on which principal determinants of economic growth or happiness should be included in a model; heterogeneity uncertainty, relating to whether or not the parameters that describe growth or happiness are identical across countries; and functional form uncertainty, relating to which growth and well-being regressors enter the model linearly and which ones enter nonlinearly.

Model averaging methods including Bayesian model averaging and Frequentist model averaging are the main statistical tools that incorporate theory uncertainty into the estimation process. To address functional form uncertainty, a variety of techniques have been proposed in the literature. One suggestion, for example, involves adding regressors that are nonlinear functions of the initial set of theory-based regressors or adding regressors whose values are zero below some threshold and non-zero above that threshold. In recent years, however, there has been a rising interest in using nonparametric framework to address nonlinearities in growth and happiness regressions.

The goal of this research is twofold. First, while Bayesian approaches are dominant methods used in economic empirics to average over the model space, I take a fresh look into Frequentist model averaging techniques and propose statistical routines that computationally ease the implementation of these methods. I provide empirical examples showing that Frequentist estimators can compete with their Bayesian peers. The second objective is to use recently-developed nonparametric techniques to overcome the issue of functional form uncertainty while analyzing the variance of distribution of per capita income. Nonparametric paradigm allows for addressing nonlinearities in growth and well-being regressions by relaxing both the functional form assumptions and traditional assumptions on the structure of error terms.

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