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Structural breaks in panel data: large number of panels and short length time series

Antoch, J., Hanousek, J., Horváth, L., Hušková, M. and Wang, S. (2019) Structural breaks in panel data: large number of panels and short length time series. Econometric Reviews. ISSN 1532-4168

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To link to this item DOI: 10.1080/07474938.2018.1454378

Abstract/Summary

The detection of (structural) breaks or the so called change point problem has drawn increasing attention from the theoretical, applied economic and financial fields. Much of the existing research concentrates on the detection of change points and asymptotic properties of their estimators in panels when N, the number of panels, as well as T, the number of observations in each panel are large. In this paper we pursue a different approach, i.e., we consider the asymptotic properties when N→∞ while keeping T fixed. This situation is typically related to large (firm-level) data containing financial information about an immense number of firms/stocks across a limited number of years/quarters/months. We propose a general approach for testing for break(s) in this setup. In particular, we obtain the asymptotic behavior of test statistics. We also propose a wild bootstrap procedure that could be used to generate the critical values of the test statistics. The theoretical approach is supplemented by numerous simulations and by an empirical illustration. We demonstrate that the testing procedure works well in the framework of the four factors CAPM model. In particular, we estimate the breaks in the monthly returns of US mutual funds during the period January 2006 to February 2010 which covers the subprime crises.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Arts, Humanities and Social Science > School of Politics, Economics and International Relations > Economics
ID Code:79661
Publisher:Taylor & Francis

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