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.
JEL Classification: C11, C22
Keywords: Autoregression, unit root, structural change
Recently Perron (1989) has carried out tests of the unit root hypothesis against the alternative hypothesis of trend stationarity with a break in the trend occurring at the Great Crash of 1929 or at the 1973 oil price shock. His analysis covers the Nelson-Plosser macroeconomic data series as well as a post-war quarter real GNP series. His tests reject the unit root null hypothesis for most of the series.
This paper takes issue with the assumption used by Perron that the Great Crash and the oil price shock can be treated as exogenous events. A variation of Perron’s test is considered in which the break point is estimated rather than fixed. We argue this test is more appropriate than Perron’s, since it circumvents the problem of data-mining.
The asymptotic distribution of the “estimated break point” test statistic is determined. The data series considered by Perron are reanalyzed using this test statistic. The empirical results make use of the asymptotics developed for the test statistic as well as extensive finite sample corrections obtained by simulation. The effect on the empirical results of fat-tailed and temporally dependent innovations is investigated. In brief, by treating the break point as endogenous, we find that there is less evidence against the unit root hypothesis than Perron finds for many of the data series, but stronger evidence against it for several of the series, including the Nelson-Plosser industrial production, nominal GNP, and real GNP series.
Keywords: Asymptotic distribution, breqak point, Gaussian process, macroeconomic time series, structural change, test statistic, time trend, trend stationary, unit root hypothesis, weak convergence