This research was carried out within the project ‘Integration and cointegration analysis with panel data’ financed by the Deutsche Forschungsgemeinschaft (DFG). An earlier version of the paper was presented at the workshop ‘Unit roots and cointegration in panel data’ in Frankfurt, October 2004. We thank participants of the workshop, in particular Anindya Banerjee, as well as an anonymous referee, for helpful comments.
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Combining Significance of Correlated Statistics with Application to Panel Data†
Article first published online: 15 SEP 2006
DOI: 10.1111/j.1468-0084.2006.00181.x
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How to Cite
Demetrescu, M., Hassler, U. and Tarcolea, A.-I. (2006), Combining Significance of Correlated Statistics with Application to Panel Data. Oxford Bulletin of Economics and Statistics, 68: 647–663. doi: 10.1111/j.1468-0084.2006.00181.x
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Publication History
- Issue published online: 15 SEP 2006
- Article first published online: 15 SEP 2006
- Final Manuscript Received: November 2005
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Keywords:
- C12;
- C23
Abstract
The inverse normal method, which is used to combine P-values from a series of statistical tests, requires independence of single test statistics in order to obtain asymptotic normality of the joint test statistic. The paper discusses the modification by Hartung (1999, Biometrical Journal, Vol. 41, pp. 849–855), which is designed to allow for a certain correlation matrix of the transformed P-values. First, the modified inverse normal method is shown here to be valid with more general correlation matrices. Secondly, a necessary and sufficient condition for (asymptotic) normality is provided, using the copula approach. Thirdly, applications to panels of cross-correlated time series, stationary as well as integrated, are considered. The behaviour of the modified inverse normal method is quantified by means of Monte Carlo experiments.