Testing for Structural Breaks in Small Samples

By Sergei Antoshin, Andrew Berg, Marcos R Souto

March 1, 2008

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Sergei Antoshin, Andrew Berg, and Marcos R Souto. Testing for Structural Breaks in Small Samples, (USA: International Monetary Fund, 2008) accessed November 21, 2024

Disclaimer: This Working Paper should not be reported as representing the views of the IMF.The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate

Summary

In a recent paper, Bai and Perron (2006) demonstrate that their approach for testing for multiple structural breaks in time series works well in large samples, but they found substantial deviations in both the size and power of their tests in smaller samples. We propose modifying their methodology to deal with small samples by using Monte Carlo simulations to determine sample-specific critical values under the each time the test is run. We draw on the results of our simulations to offer practical suggestions on handling serial correlation, model misspecification, and the use of alternative test statistics for sequential testing. We show that, for most types of data generating processes in samples with as low as 50 observations, our proposed modifications perform substantially better.

Subject: Data processing

Keywords: Autocorrelation coefficient, Prob k, Sample size, Serial correlation, WP

Publication Details

Pages:

27
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2008/075
Stock No:

WPIEA2008075
ISBN:

9781451869378
ISSN:

1018-5941

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