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Test of Hypotheses in a Time Trend Panel Data Model with Serially Correlated Error Component Disturbances

Badi Baltagi (), Chihwa Kao () and Long Liu
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Long Liu: University of Texas at San Antonio

No 170, Center for Policy Research Working Papers from Center for Policy Research, Maxwell School, Syracuse University

Abstract: This paper studies test of hypotheses for the slope parameter in a linear time trend panel data model with serially correlated error component disturbances. We propose a test statistic that uses a bias corrected estimator of the serial correlation parameter. The proposed test statistic which is based on the corresponding fixed effects feasible generalized least squares (FE-FGLS) estimator of the slope parameter has the standard normal limiting distribution which is valid whether the remainder error is I(0) or I(1). This performs well in Monte Carlo experiments and is recommended.

Keywords: Panel Data; Generalized Least Squares; Time Trend Model; Fixed Effects; First Difference; and Nonstationarity (search for similar items in EconPapers)
JEL-codes: C23 C33 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
Date: 2014-07
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Related works:
Working Paper: Test of Hypotheses in a Time Trend Panel Data Model with Serially Correlated Error Component Disturbances (2015) Downloads
Chapter: Test of Hypotheses in a Time Trend Panel Data Model with Serially Correlated Error Component Disturbances (2014) Downloads
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