Testing for predictability in panels of any time series dimension

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title = "Testing for predictability in panels of any time series dimension",
abstract = "The few panel data tests for 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, these tests are impossible for stock markets where lengthy time series data are scarce. In response to this, the current paper develops a new test for predictability in panels where $N$ is large and $T \geq 2$ can be small or large, or indeed anything in between the two extremes. This consideration represents an advancement when compared to the usual large-$N$ and large-$T$ requirement. The new test is also very general, especially when it comes to the allowable predictors, and it is easy to implement. As an illustration, we consider the Chinese stock market, for which data is only available for 17 years but where the number firms is relatively large, 160.",
keywords = "Panel data, Predictive regression, Stock return predictability, China",
author = "Joakim Westerlund and Paresh Narayan",
year = "2016",
doi = "10.1016/j.ijforecast.2016.02.009",
language = "English",
volume = "32",
pages = "1162–1177",
journal = "International Journal of Forecasting",
issn = "1872-8200",
publisher = "Elsevier",
number = "4",