What Macroeconomists Should Know about Unit Roots: A Bayesian Perspective
- 1 August 1994
- journal article
- research article
- Published by Cambridge University Press (CUP) in Econometric Theory
- Vol. 10 (3-4) , 645-671
- https://doi.org/10.1017/s0266466600008719
Abstract
This paper summarizes recent Bayesian research on unit roots for the applied macroeconomist in the way Campbell and Perron [8] summarized the classical unit roots perspective. The appropriate choice of a prior is discussed. In recognizing a consensus distaste for explosive roots, I find the popular Normal-Wishart priors centered at the unit root to be reasonable provided they are modified by concentrating the prior mass for the time trend coefficient toward zero as the largest root approaches unit from below. I discuss that the tails of the predictive density can be sensitive to the prior treatment of explosive roots. Because the focus of an investigation often is on a particular persistence property or medium-term forecasting property of the data, I conclude that Bayesian methods often deliver natural answers to macroeconomic questions.Keywords
This publication has 80 references indexed in Scilit:
- Bayesian model selection and prediction with empirical applicationsJournal of Econometrics, 1995
- Bayesian long-run prediction in time series modelsJournal of Econometrics, 1995
- On the Shape of the Likelihood/Posterior in Cointegration ModelsEconometric Theory, 1994
- Co-integration and trend-stationarity in macroeconomic time series: Evidence from the likelihood functionJournal of Econometrics, 1992
- Bayesian and Likelihood Methods in Statistics and Econometrics: Essays in Honor of George A. Barnard.Journal of the American Statistical Association, 1991
- Unit roots in real GNP: Do we know, and do we care?Carnegie-Rochester Conference Series on Public Policy, 1990
- How Big Is the Random Walk in GNP?Journal of Political Economy, 1988
- Bayesian analysis in econometricsJournal of Econometrics, 1988
- The Role of Approximate Prior Restrictions in Distributed Lag EstimationJournal of the American Statistical Association, 1972
- Finite Sample Monte Carlo Studies: An Autoregressive IllustrationJournal of the American Statistical Association, 1967