TY - JOUR
T1 - Testing for predictability in panels of any time series dimension
AU - Westerlund, Joakim
AU - Narayan, Paresh
N1 - Funding Information:
The authors would like to thank Rob Hyndman (Handling Editor), an Associate Editor, and two anonymous referees for valuable comments and suggestions. Westerlund would also like to thank the Knut and Alice Wallenberg Foundation and the Jan Wallander and Tom Hedelius Foundation ( P2014-0112:1 ) for financial support.
Publisher Copyright:
© 2016 International Institute of Forecasters.
Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.
PY - 2016/10
Y1 - 2016/10
N2 - 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.
AB - 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.
KW - China
KW - Panel data
KW - Predictive regression
KW - Stock return predictability
UR - http://www.scopus.com/inward/record.url?scp=84971655211&partnerID=8YFLogxK
U2 - 10.1016/j.ijforecast.2016.02.009
DO - 10.1016/j.ijforecast.2016.02.009
M3 - Article
AN - SCOPUS:84971655211
SN - 0169-2070
VL - 32
SP - 1162
EP - 1177
JO - International Journal of Forecasting
JF - International Journal of Forecasting
IS - 4
ER -