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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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 stationary 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)
Pages: 66 pages
Date: 2019-02
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Testing for shifts in a time trend panel data model with serially correlated error component disturbances (2020)
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