Estimating Deterministic Trends in the Presence of Serially Correlated Errors
Eugene Canjels, Mark W. Watson
NBER Technical Working Paper No. 165
This paper studies the problems of estimation and inference in the linear trend model: yt=à+þt+ut, where ut follows an autoregressive process with largest root þ, and þ is the parameter of interest. We contrast asymptotic results for the cases þþþ < 1 and þ=1, and argue that the most useful asymptotic approximations obtain from modeling þ as local-to-unity. Asymptotic distributions are derived for the OLS, first-difference, infeasible GLS and three feasible GLS estimators. These distributions depend on the local-to-unity parameter and a parameter that governs the variance of the initial error term, þ. The feasible Cochrane-Orcutt estimator has poor properties, and the feasible Prais-Winsten estimator is the preferred estimator unless the researcher has sharp a priori knowledge about þ and þ. The paper develops methods for constructing confidence intervals for þ that account for uncertainty in þ and þ. We use these results to estimate growth rates for real per capita GDP in 128 countries.
Published: Canjels, Eugene and Mark W. Watson. "Estimating Deterministic Trends In The Presence Of Serially Correlated Errors," Review of Economics and Statistics, 1997, v79(2,May), 184-200.