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Estimating deterministic trends in the presence of serially correlated errors

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  • Eugene Canjels
  • Mark W. Watson

Abstract

This paper studies the problems of estimation and inference in the linear trend model y t = α + βt + u t, where u t follows an autoregressive process with largest root ρ and β is the parameter of interest. We contrast asymptotic results for the cases |ρ| < 1 and ρ = 1 and argue that the most useful asymptotic approximations obtain from modeling ρ 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 κ. 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 ρ and κ. The paper develops methods for constructing confidence intervals for β that account for uncertainty in ρ and κ. We use these results to estimate growth rates for real per-capita GDP in 128 countries. © 1997 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology

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Bibliographic Info

Paper provided by Federal Reserve Bank of Chicago in its series Working Paper Series, Macroeconomic Issues with number 94-19.

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Date of creation: 1994
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Handle: RePEc:fip:fedhma:94-19

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Keywords: Econometrics;

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  1. Beach, Charles M & MacKinnon, James G, 1978. "A Maximum Likelihood Procedure for Regression with Autocorrelated Errors," Econometrica, Econometric Society, vol. 46(1), pages 51-58, January.
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