Nonparametric inference for unbalanced time series data
AbstractThis paper is concerned with the practical problem of conducting inference in a vector time series setting when the data are unbalanced or incomplete. In this case, one can work with only the common sample, to which a standard HAC/ bootstrap theory applies, but at the expense of throwing away data and perhaps losing efficiency. An alternative is to use some sort of imputation method, but this requires additional modeling assumptions, which we would rather avoid. We show how the sampling theory changes and how to modify the resampling algorithms to accommodate the problem of missing data. We also discuss efficiency and power. Unbalanced data of the type we consider are quite common in financial panel data; see, for example, Connor and Korajczyk (1993, Journal of Finance 48, 1263–1291). These data also occur in cross-country studies.
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Bibliographic InfoPaper provided by London School of Economics and Political Science in its series Open Access publications from London School of Economics and Political Science with number http://eprints.lse.ac.uk/322/.
Date of creation: Feb 2005
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Publication status: Published in Econometric theory (2005-02) v.21, p.143-157
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Other versions of this item:
- Linton, Oliver, 2005. "Nonparametric Inference For Unbalanced Time Series Data," Econometric Theory, Cambridge University Press, vol. 21(01), pages 143-157, February.
- Oliver Linton, 2004. "Nonparametric Inference for Unbalanced Time Series Data," STICERD - Econometrics Paper Series /2004/474, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
- Oliver Linton, 2004. "Nonparametric inference for unbalance time series data," CeMMAP working papers CWP06/04, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
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