Adaptive Nonparametric Regression with Conditional Heteroskedasticity

• 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
• Institution: Singapore Management University

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.