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Bayesian Semiparametric Regression for Autoregressive Models with Possible Unit Roots

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  • Ricardo Gonçalves Silva

    (Instituto de Ciências Matemáticas e de Computação)

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

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File URL: http://128.118.178.162/eps/em/papers/0405/0405002.pdf
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Bibliographic Info

Paper provided by EconWPA in its series Econometrics with number 0405002.

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Length: 16 pages
Date of creation: 20 May 2004
Date of revision:
Handle: RePEc:wpa:wuwpem:0405002

Note: Type of Document - pdf; pages: 16
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Web page: http://128.118.178.162

Related research

Keywords: Bayesian Inference; Unit Root; Structural Break; MCMC; Semiparametric Regression; Nonlinear Time Trend; Random Walk Prior; Macroeconomic Time Series;

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  1. Efstathios Paparoditis & Dimitris N. Politis, 2003. "Residual-Based Block Bootstrap for Unit Root Testing," Econometrica, Econometric Society, vol. 71(3), pages 813-855, 05.
  2. Peter C.B. Phillips, 1991. "Bayesian Routes and Unit Roots: de rebus prioribus semper est disputandum," Cowles Foundation Discussion Papers 986, Cowles Foundation for Research in Economics, Yale University.
  3. John Y. Campbell & Pierre Perron, 1991. "Pitfalls and Opportunities: What Macroeconomists Should Know About Unit Roots," NBER Technical Working Papers 0100, National Bureau of Economic Research, Inc.
  4. Robinson, Peter M, 1982. "On the Asymptotic Properties of Estimators of Models Containing Limited Dependent Variables," Econometrica, Econometric Society, vol. 50(1), pages 27-41, January.
  5. Perron, P., 1986. "Trends and Random Walks in Macroeconomic Time Series: Further Evidence From a New Approach," Cahiers de recherche 8650, Universite de Montreal, Departement de sciences economiques.
  6. Christopher A. Sims, 1988. "Bayesian skepticism on unit root econometrics," Discussion Paper / Institute for Empirical Macroeconomics 3, Federal Reserve Bank of Minneapolis.
  7. Zivot, Eric & Andrews, Donald W K, 2002. "Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 25-44, January.
  8. Peter C. Schotman & Herman K. van Dijk, 1991. "On Bayesian routes to unit roots," Discussion Paper / Institute for Empirical Macroeconomics 43, Federal Reserve Bank of Minneapolis.
  9. Dahl Christian M. & Gonzalez-Rivera Gloria, 2003. "Identifying Nonlinear Components by Random Fields in the US GNP Growth. Implications for the Shape of the Business Cycle," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 7(1), pages 1-35, April.
  10. Gourieroux, C. & Monfort, A. & Trognon, A., 1985. "A General Approach to Serial Correlation," Econometric Theory, Cambridge University Press, vol. 1(03), pages 315-340, December.
  11. Lawrence J. Christiano, 1988. "Searching For a Break in GNP," NBER Working Papers 2695, National Bureau of Economic Research, Inc.
  12. Gourieroux Christian & Monfort Alain & Trognon A, 1984. "General approach of serial correlation (a)," CEPREMAP Working Papers (Couverture Orange) 8424, CEPREMAP.
  13. Corradi, Valentina & Swanson, Norman R., 2006. "Bootstrap conditional distribution tests in the presence of dynamic misspecification," Journal of Econometrics, Elsevier, vol. 133(2), pages 779-806, August.
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