Monte Carlo Evaluation of the Methods Estimating Structural Change Point in Panel Data

Yıl 2019, Cilt: 23 Sayı: 3, 340 - 357, 01.06.2019

Selim Dağlıoğlu , Mehmet Akif Bakır

https://doi.org/10.16984/saufenbilder.441235

Cited By: 2

Öz

In this study, we investigate the existence of
structural break in a panel data consisting of N time series of T unit length,
and the estimation performance of Simple Mean Shift Model, Fluctuation Test,
Wald Statistic Test, Kim Test which are based on common break assumption are
examined to determine the break date. In this context, 108 Monte Carlo
simulations are performed, each of which consisted of 3000 repetitions for the
factors number of cross-sections, time dimension, break size and break rate
factors, which are considered to influence the performance of the tests. As a
result of the Monte Carlo simulations, the Simple Mean Shift Model approach
predicts the break point with a higher performance than the other methods. In
addition, if the breakpoints are at the midpoint of the series, the Wald
Statistic and Kim Tests show the highest performances, while the Fluctuation
Test shows the highest breakpoint predictive performance if break occur in the
third quarter of the series. Generally, as the number of cross-sections
increases, the estimation performance of the tests increases, whereas as the
time dimension increases, the performance of methods other than the Simple Mean
Shift Model decreases. As a final point, it has been observed that there is no significant
effect of the break size on the predictive performance of the methods.

Anahtar Kelimeler

Panel data, structural break, estimation of break point, Monte Carlo simulations

Kaynakça

• Andrews, D. W. K. (1993). Tests for Parameter Instability and Structural Changes with Unknown Change Point, Econometrica, 61, 821-856.
• Andrews, D. W. K. and Ploberger, W. (1994). Optimal Tests When a Nuisance Parameter is Present Only under the Alternative, Econometrica, 62, 1383-1414.
• Bai, J. (1997). Estimation of a Change Point in Multiple Regression Models, Review of Economics and Statistics, 79, 551-563.
• Bai, J. (2010). Common Breaks in Means and Variances for Panel Data, Journal of Econometrics, 157, 78-92.
• Bai, J., Lumsdaine, R. L. and Stock, J. H. (1998). Testing For and Dating Common Breaks in Multivariate Time Series, Review of Economic Studies Limited, 65, 395-432.
• Bai, J. and Perron, P. (1998). Estimating and Testing Linear Models with Multiple Structural Changes, Econometrica, 66, 1, pp. 47-78.
• Bai, J. and Perron, P. (2003). Computation and Analysis of Multiple Structural Change, Journal of Applied Econometrics 18, 1-22.
• Carlion-i-Silvestre, J. L., Barrio-Castro, T. D. and López-Bazo, E. (2005). Breaking the Panels: An application to the GDP per Capita, Econometrics Journal, 8, 159-175.
• Chan, J., Horváth, L. and Hušková, M. (2013). Darling-Erdös Limit Results for Change-Point Detection in Panel Data, Journal of Statistical Planning and Inference, 143, 955-970.
• Chu, C. S. and White, H. (1992). A Direct Test for Changing Trend, Journal of Business and Statistics, 10, 289-299.
• Emerson, J. and Kao, C. (2000). Testing for Structural Change of a Time Trend Regression in Panel Data, Working Paper No. 15, Center for Policy Research, 137.
• Emerson, J. and Kao, C. (2006). Testing for Structural Change in Panel Data: GDP Growth, Consumption Growth, and Productivity Growth, Economics Bulletin, 3, 14, 1-12.
• Feng, Q., Kao, C. and Lazarova, S. (2008). Estimation and Identification of Change Points in Panel Models, Working Paper, Center for Policy Research, Syracuse University, Mimeo.
• Han, A. K. and Park, D. (1989). Testing for Structural Change in Panel Data: Application to a Study of U.S. Foreign Trade in Manufacturing Goods, The Review of Economics and Statistics, 71 (1), 135-142.
• Horváth, L. ve Hušková, M. (2012). Change-Point Detection in Panel Data, Journal of Time Series Analysis, 33, 631-648.
• Joseph, L. and Wolfson, D. B. (1992). Estimation in Multi-Path Change-Point Problems, Communications in Statistics-Theory and Methods, 21 (4), 897-913.
• Joseph, L. and Wolfson, D. B. (1993). Maximum Likelihood Estimation in the Multi-Path Change-Point Problem, Annals of Institute of Statistical Mathematics, 45 (3), 511-530.Joseph, L., Vandal, A. C. and Wolfson, D. B. (1996). Estimation in the multipath change point problem for correlated data, The Canadian Journal of Statistics, 24 (1), 37-53.
• Joseph, L., Wolfson, D. B., Berger, R. D. and Lyle, R. M. (1996). Change-Point Analysis of a Randomized Trial on the Effects of Calcium Supplementation on Blood Pressure, Bayesian Biostatistics, Berry, D. A. and Stangl, D. K. Eddition, Marcel Dekker Inc.
• Joseph, L., Wolfson, D. B., Berger, R. D. and Lyle, R. M. (1997). Analysis of Panel Data With Change-Points, Statistica Sinica 7, 687-703.
• Kao, C., Trapani, L. and Urga, G. (2007). "Modelling and Testing for Structural Changes in Panel Cointegration Models with Common and Idiosyncratic Stochastic Trend", Working Paper, Paper 73, Center for Policy Research, Surface, Syracuse University.
• Kim, D. (2011). Estimating a common deterministic time trend break in large panels with cross sectional dependence, Journal of Econometrics 164, 310-330.
• Li, F., Tian, Z., Xiao, Y. and Chen, Z. (2015). Variance Change-Point Detection in Panel Data Models, Economics Letters, 126, 140-143.
• Liao, W. (2008). Structural Breaks in Panel Data Models: A New Approach, Job Market Paper.
• Lumsdaine, R. L. and Papell, D. H. (1997). Multiple Trend Breaks and the Unit-Root Hypothesis, The Review of Economics and Statistics, 79 (2), 212-218.
• Nelson, C. R. and Plosser, C. I. (1982). Trends and Random Walks in Macroeconomic Time Series: Some Evidence and Implications, Journal of Monetary Economics, 10, 139-162 North-Holland Publishing Company.
• Perron, P. (1989). The Great Crash, The Oil Price Shock and The Unit Root Hypothesis, Econometrica, 57 (6), 1361-1401.
• Perron, P., Zhu, X. (2005). Structural breaks with deterministic and stochastic trends, Journal of Econometrics 129, 65–119.
• Pettitt, A. N. (1979). A Nonparametric Approach to the Change-Point Problem, Applied Statistics 28, 126-135.
• Ploberger, W., Kramer, W. and Kontrus, K. (1989). A New Test for Structural Stability in the Linear Regression Model, Journal of Econometrics, 40, 307-318.
• Smith, A. F. M. (1975). A Bayesian Approach to Inference about a Change-Point in a Sequence of Random Variables, Biometrika 62, 407-416.
• Vogelsang, T. J. (1997). Wald-Type Tests for Detecting Breaks in the Trend Function of a Dynamic Time Series, Econometric Theory, 13, 818-849.
• Zivot, E. and Andrews, D. W. K. (1992). Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis, Journal of Business & Economic Statistics, 10, 3.