Nonparametric inference for unbalanced time series data

Oliver Linton

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: This paper is concerned with the practical problem of conducting inference in a vector time series setting when the data is unbalanced or incomplete. In this case, one can work only with 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 modelling 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). These data also occur in crosscountry studies.

Keywords: Bootstrap; efficient; HAC estimation; missing data; subsampling. (search for similar items in EconPapers)
JEL-codes: C14 C22 (search for similar items in EconPapers)
Pages: 14 pages
Date: 2004-04
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://eprints.lse.ac.uk/2116/ Open access version. (application/pdf)

Related works:
Journal Article: NONPARAMETRIC INFERENCE FOR UNBALANCED TIME SERIES DATA (2005)
Working Paper: Nonparametric inference for unbalanced time series data (2005)
Working Paper: Nonparametric inference for unbalance time series data (2004)
Working Paper: Nonparametric Inference for Unbalanced Time Series Data (2004)
Working Paper: Nonparametric inference for unbalance time series data (2004)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:2116

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