On the Estimation of a Linear Time Trend Regression with a One-Way Error Component Model in the Presence of Serially Correlated Errors

Chihwa Kao and Jamie Emerson

No 1, Center for Policy Research Working Papers from Center for Policy Research, Maxwell School, Syracuse University

Abstract: In this paper we study the limiting distributions for ordinary least squares (OLS), fixed effects (FE), first difference (FD), and generalized least squares (GLS) estimators in a linear time trend regression with a one-way error component model in the presence of serially correlated errors. We show that when the error term is I(0), the FE is asymptotically equivalent to the GLS. However, when the error term is I(1) the GLS could be less efficient than the FD or FE estimators, and the FD is the most efficient estimator. However, when the intercept is included in the model and the error term is I(0), the OLS, FE, and GLS are asymptotically equivalent. Monte Carlo experiments are employed to compare the performance of these estimators in finite samples. The main findings are (1) the two-step GLS estimators perform well if the variance component, delta, is small and close to zero when rho

JEL-codes: C22 C23 (search for similar items in EconPapers)
Pages: 44 pages
Date: 1999-03
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Working Paper: On the Estimation of a Linear Time Trend Regression with a One- Way Error Component Model in the Presence of Serially Correlated Errors (1998)
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