An evaluation of some methods used for determination of homogenous structural break point in mean of panel data

Abstract

In this study, performances of correct break point estimation of Simple Mean Shift Model Method, Fluctuation Test, Wald Statistic Test and Kim Test methods used to investigate presence of structural break and determine the date of break in a panel data consisting of N time series, each of T length, belonging to N cross-section have been investigated. In this context 108 Monte Carlo simulations with each 3000 repeats have been carried out for 3, 3, 4 and 3 levels of factors, respectively number of cross-section units, length of series, size of break and proportion of break, to evaluate the performance of these tests used for determination of structural break in panel data. According to the Monte Carlo simulations it is concluded that Simple Mean Shift Model approach has better performance of break point estimation than other methods. Moreover, while Wald Test puts forth its best performance in the case where the breaks in series are at the half of the series, Fluctuation and Kim Tests showed their best performances in the case that the breaks are at the third quarter of series. Generally, correct break point estimation performances of tests decrease as the number of cross-section or length of series increases, even if it is limited. The changes at the levels of the proportion of break factor also lead to high accuracy estimation performance of different methods. Moreover, increases at the size of break usually decreases rates of correct estimation of methods and they approach to zero while means of the series changed 40% and over after break.

Keywords

• : Panel data,structural break,correct estimation of breakpoint,Monte Carlo simulation

References

1. Nelson, C. R. and Plosser, C. I., Trends and Random Walks in Macroeconomic Time Series: Some Evidence and Implications, Journal of Monetary Economics, 10 (1982), 139-162.
2. Perron, P., The Great Crash, The Oil Price Shock and The Unit Root Hypothesis, Econometrica, 57 (6) (1989), 1361-1401.
3. Zivot, E. and Andrews, D. W. K., Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis, Journal of Business & Economic Statistics, 10 (3) (1992), 251-270.
4. Lumsdaine, R. L. and Papell, D. H., Multiple Trend Breaks and the Unit-Root Hypothesis, The Review of Economics and Statistics, 79 (2) (1997), 212-218.
5. Bai, J., Common Breaks in Means and Variances for Panel Data, Journal of Econometrics, 157 (2010), 78-92.
6. Liao, W., Structural Breaks in Panel Data Models: A New Approach, Job Market Paper (2008).
7. Bai, J. and Perron, P., Estimating and Testing Linear Models with Multiple Structural Changes, Econometrica, 66 (1) (1998), 47-78.
8. Carlion-i-Silvestre, J. L., Barrio-Castro, T. D. and López-Bazo, E., Breaking the Panels: An application to the GDP per Capita, Econometrics Journal, 8 (2005), 159-175.

9. Feng, Q., Kao, C. and Lazarova, S.. Estimation and Identification of Change Points in Panel Models, Working Paper (2008), Center for Policy Research, Syracuse University, Mimeo.
10. Han, A. K. and Park, D., 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) (1989), 135-142.
11. Joseph, L. and Wolfson, D. B., Estimation in Multi-Path Change-Point Problems, Communications in Statistics-Theory and Methods, 21 (4) (1992), 897-913.
12. Bai, J., Estimation of a Change Point in Multiple Regression Models, Review of Economics and Statistics, 79 (1997), 551-563.
13. Bai, J., Lumsdaine, R. L. and Stock, J. H., Testing For and Dating Common Breaks in Multivariate Time Series, Review of Economic Studies Limited, 65 (1998), 395-432.
14. Emerson, J. and Kao, C., Testing for Structural Change of a Time Trend Regression in Panel Data, Working Paper No. 15 (2000), Center for Policy Research, 137.
15. Bai, J. and Perron, P., Computation and Analysis of Multiple Structural Change, Journal of Applied Econometrics 18 (2003), 1-22.
16. Kao, C., Trapani, L. and Urga, G., "Modelling and Testing for Structural Changes in Panel Cointegration Models with Common and Idiosyncratic Stochastic Trend", Working Paper, Paper 73, Center for Policy Research (2007), Surface, Syracuse University.
17. Kim, D., Estimating a common deterministic time trend break in large panels with cross sectional dependence, Journal of Econometrics 164 (2011), 310-330.
18. Horváth, L. and Hušková, M., Change-Point Detection in Panel Data, Journal of Time Series Analysis, 33 (2012), 631-648.
19. Chan, J., Horváth, L. and Hušková, M., Darling-Erdös Limit Results for Change-Point Detection in Panel Data, Journal of Statistical Planning and Inference, 143 (2013), 955-970.
20. Li, F., Tian, Z., Xiao, Y. and Chen, Z., Variance Change-Point Detection in Panel Data Models, Economics Letters, 126 (2015), 140-143.
21. Joseph, L. and Wolfson, D. B., Maximum Likelihood Estimation in the Multi-Path Change-Point Problem, Annals of Institute of Statistical Mathematics, 45 (3) (1993), 511-530.
22. Joseph, L., Vandal, A. C. and Wolfson, D. B., Estimation in the multipath change point problem for correlated data, The Canadian Journal of Statistics, 24 (1) (1996), 37-53.
23. Joseph, L., Wolfson, D. B., Berger, R. D. and Lyle, R. M., Change-Point Analysis of a Randomized Trial on the Effects of Calcium Supplementation on Blood Pressure, Bayesian Biostatistics (1996), Berry, D. A. and Stangl, D. K. Eddition, Marcel Dekker Inc.
24. Joseph, L., Wolfson, D. B., Berger, R. D. and Lyle, R. M., Analysis of Panel Data With Change-Points, Statistica Sinica 7 (1997), 687-703.
25. Dağlıoğlu, S, Bakır, M. A., Monte Carlo Evaluation of the Methods Estimating Structural Change Point in Panel Data. Sakarya University Journal of Science, 23 (3) (2019), 340-357.
26. Ploberger, W., Kramer, W. and Kontrus, K., A New Test for Structural Stability in the Linear Regression Model, Journal of Econometrics, 40(1989), 307-318.
27. Andrews, D. W. K. and Ploberger, W., Optimal Tests When a Nuisance Parameter is Present Only under the Alternative, Econometrica, 62 (1994), 1383-1414.
28. Andrews, D. W. K., Tests for Parameter Instability and Structural Changes with Unknown Change Point, Econometrica, 61 (1993), 821-856.
29. Perron, P., Zhu, X., Structural breaks with deterministic and stochastic trends, Journal of Econometrics 129 (2005), 65–119.