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.
: Panel data structural break correct estimation of breakpoint Monte Carlo simulation
Birincil Dil | İngilizce |
---|---|
Konular | Uygulamalı Matematik |
Bölüm | Research Article |
Yazarlar | |
Yayımlanma Tarihi | 30 Haziran 2020 |
Gönderilme Tarihi | 13 Mayıs 2019 |
Kabul Tarihi | 23 Ocak 2020 |
Yayımlandığı Sayı | Yıl 2020 Cilt: 69 Sayı: 1 |
Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics.
This work is licensed under a Creative Commons Attribution 4.0 International License.