Adaptive Nonparametric Regression with Conditional Heteroskedasticity

Adaptive Nonparametric Regression with Conditional Heteroskedasticity

Vector Autoregression (VAR) has been a standard empirical tool used in macroeconomics and finance. In this paper we discuss how to compare alternative VAR models after they are estimated by Bayesian MCMC methods. In particular we apply a robust version of deviance information criterion (RDIC) recent...

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Bibliographic Details
Main Authors: JIN, Sainan, SU, Liangjun, XIAO, Zhijie
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2014
Subjects:
Adaptive Estimation
Conditional Heteroskedasticity
Local Profile Likelihood Estimation
Local Polynomial Estimation
Nonparametric Regression
One-step Estimator
Econometrics
Online Access:https://ink.library.smu.edu.sg/soe_research/1568
https://ink.library.smu.edu.sg/context/soe_research/article/2567/viewcontent/np_profile20140312.pdf
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Institution: Singapore Management University
Language: English
Description
Summary:Vector Autoregression (VAR) has been a standard empirical tool used in macroeconomics and finance. In this paper we discuss how to compare alternative VAR models after they are estimated by Bayesian MCMC methods. In particular we apply a robust version of deviance information criterion (RDIC) recently developed in Li et al. (2014b) to determine the best candidate model. RDIC is a better information criterion than the widely used deviance information criterion (DIC) when latent variables are involved in candidate models. Empirical analysis using US data shows that the optimal model selected by RDIC can be different from that by DIC.

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