Testing for Structural Change of a Time Trend Regression in Panel Data

Center for Policy Research, Maxwell School, Syracuse University
04/2000; DOI: 10.2139/ssrn.1808001
Source: RePEc

ABSTRACT In this paper we propose two classes of test statistics for detecting a break at an unknown date in panel data models with time trend. The first one is the fluctuation test of Ploberger-Kramer-Kontrus (1989). The second one is based on the mean and exponential Wald statistics of Andrew and Ploberger (1994) and maximum Wald statistic of Andrew (1993). We derive the limiting distributions of the proposed test and tabulate the critical values. Asymptotic results were derived I(0), I(1) and nearly I(1) error terms. We also show that these tests have non-trivial local power only when the error terms are I(0).

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    ABSTRACT: The Phase I analysis of data when the quality of a process or product is characterized by a linear function is studied in this dissertation. It is assumed that each sample collected over time in the historical data set consists of several bivariate observations for which a simple linear regression model is appropriate, a situation common in calibration applications. Using a simulation study, the researcher compares the performance of some of the recommended approaches used to assess the stability of the process. Also in this dissertation, a method based on using indicator variables in a multiple regression model is proposed. This dissertation also proposes a change point approach based on the segmented regression technique for testing the constancy of the regression parameters in a linear profile data set. The performance of the proposed change point method is compared to that of the most effective Phase I linear profile control chart approaches using a simulation study. The advantage of the proposed change point method over the existing methods is greatly improved detection of sustained step changes in the process parameters. System requirements: PC, World Wide Web browser and PDF reader. Available electronically via Internet. Title from electronic submission form. Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 2004. Vita. Abstract. Includes bibliographical references.
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    ABSTRACT: In this paper, we propose an estimation and testing framework for parameter instability in cointegrated panel regressions with common and idiosyncratic trends. We develop tests for structural change for the slope parameters under the null hypothesis of no structural break against the alternative hypothesis of (at least) one common change point, which is possibly unknown. The limiting distributions of the proposed test statistics are derived. Monte Carlo simulations examine size and power of the proposed tests. We are grateful for discussions with Robert De Jong, Long-Fei Lee, Zongwu Cai, and Yupin Hu. We would also like to thank participants in the International Conferences on "Common Features in London" (Cass, 16-17 December 2004), 2006 New York Econometrics Camp and Breaks and Persistence in Econometrics (Cass, 11-12 December 2006), and econometrics seminars at Ohio State University and Academia Sinica for helpful comments. Part of this work was done while Chihwa Kao was visiting the Centre for Econometric Analysis at Cass (CEA@Cass). Financial support from City University 2005 Pump Priming Fund and CEA@Cass is gratefully acknowledged. Lorenzo Trapani acknowledges financial support from Cass Business School under the RAE Development Fund Scheme.
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    ABSTRACT: Summary  This paper studies the asymptotic properties of standard panel data estimators in a simple panel regression model with random error component disturbances. Both the regressor and the remainder disturbance term are assumed to be autoregressive and possibly non-stationary. Asymptotic distributions are derived for the standard panel data estimators including ordinary least squares (OLS), fixed effects (FE), first-difference (FD) and generalized least squares (GLS) estimators when both T and n are large. We show that all the estimators have asymptotic normal distributions and have different convergence rates dependent on the non-stationarity of the regressors and the remainder disturbances. We show using Monte Carlo experiments that the loss in efficiency of the OLS, FE and FD estimators relative to true GLS can be substantial.
    Econometrics Journal 10/2008; 11(3):554 - 572. · 1.00 Impact Factor

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