Bayesian Variations on the Frisch and Waugh Theme

• Autorzy: Jacek Osiewalski
• Języki publikacji: EN

Abstrakty

EN

The paper is devoted to discussing consequences of the so-called Frisch-Waugh Theorem to posterior inference and Bayesian model comparison. We adopt a generalised normal linear regression framework and weaken its assumptions in order to cover non-normal, jointly elliptical sampling distributions, autoregressive specifications, additional nuisance parameters and multi-equation SURE or VAR models. The main result is that inference based on the original full Bayesian model can be obtained using transformed data and reduced parameter spaces, provided the prior density for scale or precision parameters is appropriately modified. (original abstract)

Słowa kluczowe

EN

Bayesian inference; Regression models; Vector Autoregression Model (VAR)

PL

Wnioskowanie bayesowskie; Modele regresji; Model wektorowej autoregresji

• Czasopismo: Central European Journal of Economic Modelling and Econometrics (CEJEME)
• Rocznik: 2011
• Tom: 3
• Numer: nr 1
• Strony: 39--47

Twórcy

autor

Jacek Osiewalski

• Cracow University of Economics, Poland

Bibliografia

• [1] Frisch R., Waugh F.V., 1933, Partial time regressions as compared with individual trends, Econometrica 1, 387-401.
• [2] Greene W.H., 2003, Econometric Analysis, Fifth Edition, Prentice Hall, Upper Saddle River, NJ.
• [3] Kleibergen F., Paap R., 2002, Priors, posteriors and Bayes factors for Bayesian analysis of cointegration, Journal of Econometrics 111, 223-249.
• [4] Osiewalski J., Steel M.F.J., 1993a, Robust Bayesian inference in elliptical regression models, Journal of Econometrics 57, 345-363.
• [5] Osiewalski J., Steel M.F.J., 1993b, Bayesian marginal equivalence of elliptical regression models, Journal of Econometrics 59, 391-403.
• [6] Zellner, A., 1971, An Introduction to Bayesian Inference in Econometrics, J. Wiley, New York.

Identyfikatory

DOI

10.24425/cejeme.2011.119302