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

Year 2019, Volume: 23 Issue: 3, 340 - 357, 01.06.2019

Selim Dağlıoğlu , Mehmet Akif Bakır

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

Cited By: 2

Abstract

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.

Keywords

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

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