標題: Bayesian estimation for time-series regressions improved with exact likelihoods
作者: Chen, CWS
Lee, JC
Lee, HY
Niu, WF
統計學研究所
資訊管理與財務金融系 註:原資管所+財金所
Institute of Statistics
Department of Information Management and Finance
關鍵字: autoregressive process;exact likelihood;Markov chain Monte Carlo;partial autocorrelations
公開日期: 1-Oct-2004
摘要: We propose an estimation procedure for time-series regression models under the Bayesian inference framework. With the exact method of Wise [Wise, J. (1955). The autocorrelation function and spectral density function. Biometrika, 42, 151-159], an exact likelihood function can be obtained instead of the likelihood conditional on initial observations. The constraints on the parameter space arising from the stationarity conditions are handled by a reparametrization, which was not taken into consideration by Chib [Chib, S. (1993). Bayes regression with autoregressive errors: A Gibbs sampling approach. J. Econometrics, 58, 275-294] or Chib and Greenberg [Chib, S. and Greenberg, E. (1994). Bayes inference in regression model with ARMA(p, q) errors. J. Econometrics, 64, 183-206]. Simulation studies show that our method leads to better inferential results than their results.
URI: http://dx.doi.org/10.1080/00949650310001643270
http://hdl.handle.net/11536/26320
ISSN: 0094-9655
DOI: 10.1080/00949650310001643270
期刊: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
Volume: 74
Issue: 10
起始頁: 727
結束頁: 740
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