Several lessons learnt from a Bayesian analysis of basic macroeconomic time series models are presented for the situation where some model parameters have substantial posterior probability near the boundary of the parameter region. This feature refers to near-instability within dynamic models, to forecasting with near-random walk models and to clustering of several economic series in a small number of groups within a data panel. Two canonical models are used: a linear regression model with autocorrelation and a simple variance components model. Several well-known time series models like unit root and error correction models and further state space and panel data models are shown to be simple generalizations of these two canonical models for the purpose of posterior inference. A Bayesian model averaging procedure is presented in order to deal with models with substantial probability both near and at the boundary of the parameter region. Analytical, graphical and empirical results using U.S. macroeconomic data, in particular on GDP growth, are presented.

Bayesian model averaging, Gibb's Sampler, MCMC, autocorrelation, error correction models, nonstationarity, random effects panel data models, reduced rank models, state space methods
Bayesian Analysis (jel C11), Simulation Methods; Monte Carlo Methods; Bootstrap Methods (jel C15), Time-Series Models; Dynamic Quantile Regressions (jel C22), Models with Panel Data (jel C23), Econometric Methods: Multiple/Simultaneous Equation Models; Multiple Variables: General (jel C30)
Emerald Group Publishing Limited
Econometric Institute Reprint Series
Erasmus School of Economics

de Pooter, M.D, Ravazzolo, F, Segers, R, & van Dijk, H.K. (2008). Bayesian Near-Boundary Analysis in Basic Macroeconomic Time-Series Models. In Econometric Institute Reprint Series. Emerald Group Publishing Limited. Retrieved from