A Bayesian Analysis of Trend Determination in Economic Time Series

Eric Zivot () and Peter C. B. Phillips ()

No 1002, Cowles Foundation Discussion Papers from Cowles Foundation, Yale University

Abstract: In this paper we provide a comprehensive Bayesian posterior analysis of trend determination in general autoregressive models. Multiple lag autoregressive models with fitted drifts and time trends as well as models that allow for certain types of structural change in the deterministic components are considered. We utilize a modified information matrix-based prior that accommodates stochastic nonstationarity, takes into account the interactions between long-run and short-run dynamics and controls the degree of stochastic nonstationarity permitted. We derive analytic posterior densities for all of the trend determining parameters via the Laplace approximation to multivariate integrals. We also address the sampling properties of our posteriors under alternative data generating processes by simulation methods. We apply our Bayesian techniques to the Nelson-Plosser macroeconomic data and various stock price and dividend data.

Keywords: Autoregression; unit root; structural change (search for similar items in EconPapers)
JEL-codes: C11 C22 (search for similar items in EconPapers)
Date: 1991-10
Note: CFP 891.
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Published in Econometric Reviews (1994), 13(3): 296-336

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Related works:
Journal Article: A bayesian analysis of trend determination in economic time series (1994)
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