Linton, Oliver (2005) Nonparametric inference for unbalanced time series data. Econometric Theory, 21 (1). pp. 143-157. ISSN 1469-4360
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Abstract
This 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.
| Item Type: | Article |
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| Official URL: | http://uk.cambridge.org/journals/ect/ |
| Additional Information: | Copyright © 2005 Cambridge University Press. LSE has developed LSE Research Online so that users may access research output of the School. Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Users may download and/or print one copy of any article(s) in LSE Research Online to facilitate their private study or for non-commercial research. You may not engage in further distribution of the material or use it for any profit-making activities or any commercial gain. You may freely distribute the URL (http://eprints.lse.ac.uk) of the LSE Research Online website. |
| Divisions: | Financial Markets Group STICERD Economics |
| Subjects: | H Social Sciences > HB Economic Theory |
| Date Deposited: | 17 Feb 2008 |
| Last Modified: | 23 Mar 2024 00:04 |
| URI: | http://eprints.lse.ac.uk/id/eprint/322 |
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