Estimating growth rate in the presence of serially correlated errors
The aim of this study is to address the difficulties frequently encountered in estimating average growth rates by a log-linear time trend in the presence of serially correlated errors. There are a few studies in the literature that provide some guidance on choosing the appropriate method depending on the degree of first order serial correlation. However, the higher order serial correlation case is generally ignored. This study proposes the Nelder-Mead simplex method as a general solution to estimating linear trend in the presence of serial correlation of any order. The proposed method and the conventional methods are applied to the real GDP per capita series of 27 OECD countries. Twelve series seem to be better modelled by a log-linear trend with AR(2) residuals, and five of them yield remarkably different growth rates.
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Volume (Year): 10 (2003)
Issue (Month): 15 ()
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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Eugene Canjels & Mark W. Watson, 1997.
"Estimating Deterministic Trends In The Presence Of Serially Correlated Errors,"
The Review of Economics and Statistics,
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- Eugene Canjels & Mark W. Watson, 1994. "Estimating Deterministic Trends in the Presence of Serially Correlated Errors," NBER Technical Working Papers 0165, National Bureau of Economic Research, Inc.
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- John Baffes & Jean-Charles Le Vallee, 2003. "Unit roots versus trend stationarity in growth rate estimation," Applied Economics Letters, Taylor & Francis Journals, vol. 10(1), pages 9-14.
- Timothy J. Vogelsang, 1998. "Trend Function Hypothesis Testing in the Presence of Serial Correlation," Econometrica, Econometric Society, vol. 66(1), pages 123-148, January. Full references (including those not matched with items on IDEAS)