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Essays on model selection using Bayesian inference
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Essays on model selection using Bayesian inference
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ETD_2002
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051791
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eng
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theses
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Economics
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Bayesian statistical decision theory
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Econometrics
Abstract
This dissertation is composed of three essays evaluating Bayesian model selection criteria in various models, and whenever necessary, the Bayesian criteria are compared with sampling theory criteria. In chapter two, I compare the 2-regime threshold ARMA model (TARMA) and 2-state Markov switching model (MSM). Bayesian Markov Chain Monte Carlo (MCMC) algorithms are devised to obtain coefficient estimates, conditional and unconditional predictive densities. Posterior densities and cumulative densities of the mean square error of forecast (MSEF) of two competing models are generated. The main finding is that for one-day conditional prediction, the 2-regime TARMA model predicts the interest rate better than the MSM. Under the unconditional prediction, however, MSM has less prediction error than TARMA.
In chapter three, I compare the MSEF and Pseudo Bayes Factor (PSBF) obtained by 10-fold CV method and those from an out of sample prediction for fixed points. The MSEF suggests there is a slightly superior performance for the CV method in model selection over traditional out-of-sample forecast in the i.i.d sample. However, the same result is not obtained by PSBF. By excluding forecasted data in constructing coefficients within MCMC, the out-of-sample method is further improved by yielding higher probability to select the true model.
In chapter four, I evaluate logit and probit binary choice models. Monte Carlo experiments are conducted to compare the following five criteria in choosing the univariate probit and logit models: the deviance information criterion (DIC), predictive DIC, Akaike information criterion (AIC), weighted and unweighted sums of squared errors. The results show that if data are balanced no model selection criterion can distinguish the probit and logit models. If data are unbalanced and the sample size is large the DIC and AIC choose the correct models better than the other criteria. If unbalanced binary data are generated by a leptokurtic distribution the logit model is preferred over the probit model. The probit model is preferred if unbalanced data are generated by a platykurtic distribution.
In chapter three, I compare the MSEF and Pseudo Bayes Factor (PSBF) obtained by 10-fold CV method and those from an out of sample prediction for fixed points. The MSEF suggests there is a slightly superior performance for the CV method in model selection over traditional out-of-sample forecast in the i.i.d sample. However, the same result is not obtained by PSBF. By excluding forecasted data in constructing coefficients within MCMC, the out-of-sample method is further improved by yielding higher probability to select the true model.
In chapter four, I evaluate logit and probit binary choice models. Monte Carlo experiments are conducted to compare the following five criteria in choosing the univariate probit and logit models: the deviance information criterion (DIC), predictive DIC, Akaike information criterion (AIC), weighted and unweighted sums of squared errors. The results show that if data are balanced no model selection criterion can distinguish the probit and logit models. If data are unbalanced and the sample size is large the DIC and AIC choose the correct models better than the other criteria. If unbalanced binary data are generated by a leptokurtic distribution the logit model is preferred over the probit model. The probit model is preferred if unbalanced data are generated by a platykurtic distribution.
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xiii, 121 p. : ill.
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Ph.D.
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Includes bibliographical references (p. 113-120)
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by Guo Chen
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Chen
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Guo
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1974-
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Guo Chen
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Hiroki
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Hiroki Tsurumi
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John
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John Landon-Lane
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Swanson
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Norman
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Norman Rasmus Swanson
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Ayse
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Ayse Akincigil
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Rutgers University
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Graduate School - New Brunswick
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2009
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2009-10
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xx
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Rutgers University Electronic Theses and Dissertations
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Graduate School - New Brunswick Electronic Theses and Dissertations
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doi:10.7282/T3QR4X9X
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ETD doctoral
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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