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Testing for Shifts in a Time Trend Panel Data Model with Serially Correlated Error Component Disturbances

Badi Baltagi (), Chihwa Kao and Long Liu
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Chihwa Kao: Department of Economics, University of Connecticut
Long Liu: Department of Economics, College of Business, University of Texas at San Antonio

No 213, Center for Policy Research Working Papers from Center for Policy Research, Maxwell School, Syracuse University

Abstract: This paper studies testing of shifts in a time trend panel data model with serially correlated error component disturbances, without any prior knowledge of whether the error term is sta- tionary or nonstationary. This is done in case the shift is known as well as unknown. Following Vogelsang (1997) in the time series literature, we propose a Wald type test statistic that uses a fixed effects feasible generalized least squares (FE-FGLS) estimator derived in Baltagi, et al. (2014). The proposed test has a Chi-square limiting distribution and is valid for both J(O) and J(l) errors. The finite sample size and power of this Wald test is investigated using Monte Carlo simulations.

Keywords: Non-Stationary Panels; Time Trends; Serial Correlation; Wald Type Tests (search for similar items in EconPapers)
JEL-codes: C23 C3 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
Date: 2019-02
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