Testing for predictability in panels of any time series dimension

Westerlund, Joakim and Narayan, Paresh 2016, Testing for predictability in panels of any time series dimension, International journal of forecasting, vol. 32, no. 4, pp. 1162-1177, doi: 10.1016/j.ijforecast.2016.02.009.

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Title Testing for predictability in panels of any time series dimension
Author(s) Westerlund, JoakimORCID iD for Westerlund, Joakim orcid.org/0000-0002-8030-5706
Narayan, PareshORCID iD for Narayan, Paresh orcid.org/0000-0001-7934-8146
Journal name International journal of forecasting
Volume number 32
Issue number 4
Start page 1162
End page 1177
Total pages 16
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2016-10
ISSN 0169-2070
Keyword(s) Panel data
Predictive regression
Stock return predictability
Summary The few panel data tests for the predictability of returns that exist are based on the prerequisite that both the number of time series observations, T, and the number of cross-section units, N, are large. As a result, it is impossible to apply these tests to stock markets, where lengthy time series of data are scarce. In response to this, the current paper develops a new test for predictability in panels where N is large and T≥. 2 can be either small or large, or indeed anything in between. This consideration represents an advancement relative to the usual large-. N and large-. T requirement. The new test is also very general, especially when it comes to allowable predictors, and is easy to implement. As an illustration, we consider the Chinese stock market, for which data are available for only 17 years, but where the number of firms is relatively large, 160.
Language eng
DOI 10.1016/j.ijforecast.2016.02.009
Field of Research 1403 Econometrics
1505 Marketing
150202 Financial Econometrics
Socio Economic Objective 9000101
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30084505

Document type: Journal Article
Collections: Faculty of Business and Law
Department of Economics
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