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

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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

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Paper provided by Center for Policy Research, Maxwell School, Syracuse University in its series Center for Policy Research Working Papers with number 1.

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Length: 44 pages
Date of creation: Mar 1999
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Handle: RePEc:max:cprwps:1

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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.
  2. Baltagi, Badi H. & Chang, Young-Jae & Li, Qi, 1992. "Monte Carlo evidence on panel data regressions with AR(1) disturbances and an arbitrary variance on the initial observations," Journal of Econometrics, Elsevier, vol. 52(3), pages 371-380, June.
  3. Kramer, Walter, 1982. "Note on Estimating Linear Trend When Residuals are Autocorrelated," Econometrica, Econometric Society, vol. 50(4), pages 1065-67, July.
  4. Summers, Robert & Heston, Alan, 1991. "The Penn World Table (Mark 5): An Expanded Set of International Comparisons, 1950-1988," The Quarterly Journal of Economics, MIT Press, vol. 106(2), pages 327-68, May.
  5. Baltagi, Badi H., 1981. "Pooling : An experimental study of alternative testing and estimation procedures in a two-way error component model," Journal of Econometrics, Elsevier, vol. 17(1), pages 21-49, September.
  6. Chihwa Kao & Suzanne McCoskey, 1997. "A Residual-Based Test Of The Null Of Cointegration In Panel Data," Econometrics 9711002, EconWPA.
  7. Baltagi, Badi H. & Li, Qi, 1991. "A transformation that will circumvent the problem of autocorrelation in an error-component model," Journal of Econometrics, Elsevier, vol. 48(3), pages 385-393, June.
  8. Timothy J. Vogelsang, 1998. "Trend Function Hypothesis Testing in the Presence of Serial Correlation," Econometrica, Econometric Society, vol. 66(1), pages 123-148, January.
  9. Park, Rolla Edward & Mitchell, Bridger M., 1980. "Estimating the autocorrelated error model with trended data," Journal of Econometrics, Elsevier, vol. 13(2), pages 185-201, June.
  10. Chipman, John S, 1979. "Efficiency of Least-Squares Estimation of Linear Trend when Residuals are Autocorrelated," Econometrica, Econometric Society, vol. 47(1), pages 115-28, January.
  11. Baltagi, Badi H., 1989. "Applications of a necessary and sufficient condition for OLS to be BLUE," Statistics & Probability Letters, Elsevier, vol. 8(5), pages 457-461, October.
  12. Kao, Chihwa, 1999. "Spurious regression and residual-based tests for cointegration in panel data," Journal of Econometrics, Elsevier, vol. 90(1), pages 1-44, May.
  13. Maeshiro, Asatoshi, 1976. "Autoregressive Transformation, Trended Independent Variables and Autocorrelated Disturbance Terms," The Review of Economics and Statistics, MIT Press, vol. 58(4), pages 497-500, November.
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