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

Emerson, Jamie; Kao, Chihwa

doi:10.2139/ssrn.1808001

Cited by 17 publications

(30 citation statements)

References 12 publications

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“…[21][22][23][24][25][26][27]. Our simulations results (not reported here) show that their approximation gives very liberal bounds unless m is considerably large.…”

Section: Approximate Thresholdsmentioning

confidence: 62%

A change point method for linear profile data

Mahmoud

Parker

Woodall

et al. 2006

Quality & Reliability Eng

250

177

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We propose a change point approach based on the segmented regression technique for testing the constancy of the regression parameters in a linear profile data set. Each sample collected over time in the historical data set consists of several bivariate observations for which a simple linear regression model is appropriate. The change point approach is based on the likelihood ratio test for a change in one or more regression parameters. We compare the performance of this method to that of the most effective Phase I linear profile control chart approaches using a simulation study. The advantages of the change point method over the existing methods are greatly improved detection of sustained step changes in the process parameters and improved diagnostic tools to determine the sources of profile variation and the location(s) of the change point(s). Also, we give an approximation for appropriate thresholds for the test statistic. The use of the change point method is demonstrated using a data set from a calibration application at

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“…[21][22][23][24][25][26][27]. Our simulations results (not reported here) show that their approximation gives very liberal bounds unless m is considerably large.…”

Section: Approximate Thresholdsmentioning

confidence: 62%

A change point method for linear profile data

Mahmoud

Parker

Woodall

et al. 2006

Quality & Reliability Eng

250

177

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“…The entries under the column heading income (emission) are the computed IPS t-statistic values for the cointegration unit root test when income (emission) was taken as the dependent variable. Here also in each case the cointegration test 11 was done twice -viz., once assuming presence of a deterministic time trend in the residuals of the cointegrating regression equation and again without making such an assumption. In Table 3 country group-specific values of these four test statistics are presented.…”

Section: Data Description and Resultsmentioning

confidence: 99%

Income and emission: A panel data-based cointegration analysis

Dinda¹,

Coondoo

2006

Ecological Economics

394

162

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This paper presents the results of an investigation of the causality issue of incomeemission relationship based on time series econometric techniques of unit root test, cointegration and related error correction model for a panel data set. Here, the nature of causality between per capita CO 2 emission (PCCO2) and per capita GDP (PCGDP) has been examined using a cross country panel data set covering 88 countries for the period 1960 -90. Using the panel unit root test procedure of Im et al. (1997) (IPS), we have found that the hypothesis of unit root (i.e., non-stationarity) of the time series of PCGDP and PCCO2 can not be rejected for individual country groups. As both the variables are found to follow I(1) process, we next have performed the panel data co-integration test and finally, we have estimated the ECM (for these country groups for which significant income-emission cointegration was obtained) to explore the nature of dynamics implicit in the given panel data set. Our findings suggest that there is more or less a bi-directional causal relationship between income (PCGDP) and CO 2 emission (PCCO2) for Africa, Central America, America as a whole, Eastern Europe, Western Europe, Europe as a whole and the World as a whole. That means, the movement of the one variable directly affects the other variable through a feedback system. Thus, the policy makers should be cautious to make proper decision about the control of emission level.JEL Classification: C33, O40, and Q25.

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“…Han and Park (1989) [12] develop a multivariate CUSUM test in order to test for a structural break in panel data, and they apply the test to U.S. manufacturing goods trade data. Kao (2000) [13] proposes two classes of test statistics for detecting a break at an unknown date in panel data models with the time trend. The first is a fluctuation test, while the second is based on the mean and exponential Wald statistics of Andrews and Ploberger (1994) [3] and the maximum Wald statistic of Andrews (1993) [2].…”

Section: Introductionmentioning

confidence: 99%

Testing for a Structural Break in a Spatial Panel Model

Sengupta¹

2017

Econometrics

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Abstract:We consider the problem of testing for a structural break in the spatial lag parameter in a panel model (spatial autoregressive). We propose a likelihood ratio test of the null hypothesis of no break against the alternative hypothesis of a single break. The limiting distribution of the test is derived under the null when both the number of individual units N and the number of time periods T is large or N is fixed and T is large. The asymptotic critical values of the test statistic can be obtained analytically. We also propose a break-date estimator that can be employed to determine the location of the break point following evidence against the null hypothesis. We present Monte Carlo evidence to show that the proposed procedure performs well in finite samples. Finally, we consider an empirical application of the test on budget spillovers and interdependence in fiscal policy within the U.S. states.

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

Cited by 17 publications

References 12 publications

A change point method for linear profile data

A change point method for linear profile data

Income and emission: A panel data-based cointegration analysis

Testing for a Structural Break in a Spatial Panel Model

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