Testing for predictability in panels of any time series dimension

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• 作者：Joakim Westerlund^a ; ^b ; ^{joakim.westerlund@nek.lu.se" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Paresh Narayan^bAuthor Vitae
• 关键词：Panel data ; Predictive regression ; Stock return predictability ; China
• 刊名：International Journal of Forecasting
• 出版年：2016
• 出版时间：October-December 2016
• 年：2016
• 卷：32
• 期：4
• 页码：1162-1177
• 全文大小：546 K

文摘

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, class="mathmlsrc">class="formulatext stixSupport mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0169207016300528&_mathId=si26.gif&_user=111111111&_pii=S0169207016300528&_rdoc=1&_issn=01692070&md5=ef5edc6a92b59b39a8531fb9522760d5" title="Click to view the MathML source">Tclass="mathContainer hidden">class="mathCode">$T$, and the number of cross-section units, class="mathmlsrc">class="formulatext stixSupport mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0169207016300528&_mathId=si27.gif&_user=111111111&_pii=S0169207016300528&_rdoc=1&_issn=01692070&md5=8193d43608380b5e73735ae22d3af18c" title="Click to view the MathML source">Nclass="mathContainer hidden">class="mathCode">$N$, 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 class="mathmlsrc">class="formulatext stixSupport mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0169207016300528&_mathId=si27.gif&_user=111111111&_pii=S0169207016300528&_rdoc=1&_issn=01692070&md5=8193d43608380b5e73735ae22d3af18c" title="Click to view the MathML source">Nclass="mathContainer hidden">class="mathCode">$N$ is large and class="mathmlsrc">class="formulatext stixSupport mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0169207016300528&_mathId=si29.gif&_user=111111111&_pii=S0169207016300528&_rdoc=1&_issn=01692070&md5=c71668540fc4965f5a72a0797c7daebe" title="Click to view the MathML source">T≥2class="mathContainer hidden">class="mathCode">$T\ge 2$ can be either small or large, or indeed anything in between. This consideration represents an advancement relative to the usual large-class="mathmlsrc">class="formulatext stixSupport mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0169207016300528&_mathId=si27.gif&_user=111111111&_pii=S0169207016300528&_rdoc=1&_issn=01692070&md5=8193d43608380b5e73735ae22d3af18c" title="Click to view the MathML source">Nclass="mathContainer hidden">class="mathCode">$N$ and large-class="mathmlsrc">class="formulatext stixSupport mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0169207016300528&_mathId=si26.gif&_user=111111111&_pii=S0169207016300528&_rdoc=1&_issn=01692070&md5=ef5edc6a92b59b39a8531fb9522760d5" title="Click to view the MathML source">Tclass="mathContainer hidden">class="mathCode">$T$ 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.