Title
A consistent nonparametric Bayesian procedure for estimating autoregressive conditional densities
Abstract
This article proposes a Bayesian infinite mixture model for the estimation of the conditional density of an ergodic time series. A nonparametric prior on the conditional density is described through the Dirichlet process. In the mixture model, a kernel is used leading to a dynamic nonlinear autoregressive model. This model can approximate any linear autoregressive model arbitrarily closely while imposing no constraint on parameters to ensure stationarity. We establish sufficient conditions for posterior consistency in two different topologies. The proposed method is compared with the mixture of autoregressive model [Wong and Li, 2000. On a mixture autoregressive model. J. Roy. Statist. Soc. Ser. B 62(1), 91-115] and the double-kernel local linear approach [Fan et al., 1996. Estimation of conditional densities and sensitivity measures in nonlinear dynamical systems. Biometrika 83, 189-206] by simulations and real examples. Our method shows excellent performances in these studies.
Year
DOI
Venue
2007
10.1016/j.csda.2006.06.020
Computational Statistics & Data Analysis
Keywords
Field
DocType
autoregressive conditional density,mixture autoregressive model,conditional density,dynamic nonlinear autoregressive model,autoregressive model,double-kernel local linear approach,linear autoregressive model,mixture model,consistent nonparametric bayesian procedure,bayesian infinite mixture model,nonlinear dynamical system,time series
Econometrics,Autoregressive model,Conditional variance,Nonlinear autoregressive exogenous model,Conditional probability distribution,Autoregressive integrated moving average,SETAR,STAR model,Autoregressive conditional heteroskedasticity,Statistics,Mathematics
Journal
Volume
Issue
ISSN
51
9
Computational Statistics and Data Analysis
Citations 
PageRank 
References 
7
1.09
1
Authors
2
Name
Order
Citations
PageRank
Yongqiang Tang1163.38
Subhashis Ghosalb2255.39