Bayesian Semiparametric Regression for Autoregressive Models with Possible Unit Roots

Ricardo Gonçalves Silva ()

Econometrics from EconWPA

Abstract: In this paper we consider bayesian semiparametric regression within the generalized linear model framework. Specifically, we study a class of autoregressive time series where the time trend is incorporated in a nonparametrically way. Estimation and inference where performed through Markov Chain Monte Carlo simulation techniques. Main results show that treating the time trend nonparametrically possible model misspecification and biased results from structural break issues are solved. Empirical applications are conducted using the extended Nelson and Plosser benchmark time series

Keywords: Bayesian Inference; Unit Root; Structural Break; MCMC; Semiparametric Regression; Nonlinear Time Trend; Random Walk Prior; Macroeconomic Time Series (search for similar items in EconPapers)
JEL-codes: C11 C13 C14 C22 C44 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
Date: 2004-05-20
Note: Type of Document - pdf; pages: 16
View list of references

Downloads: (external link)
http://129.3.20.41/eps/em/papers/0405/0405002.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:wpa:wuwpem:0405002

Access Statistics for this paper

More papers in Econometrics from EconWPA
Series data maintained by EconWPA ().