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Nonparametric inference for unbalanced time series data

Linton, Oliver (2004) Nonparametric inference for unbalanced time series data. Econometrics; EM/2004/474, EM/04/474. Suntory and Toyota International Centres for Economics and Related Disciplines, London School of Economics and Political Science, London, UK.

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Identification Number: EM/04/474

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.

Item Type: Monograph (Discussion Paper)
Official URL: http://sticerd.lse.ac.uk/dps/em/em474.pdf
Additional Information: © 2004 Professor Oliver Linton
Subjects: H Social Sciences > HB Economic Theory
JEL classification: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C14 - Semiparametric and Nonparametric Methods
C - Mathematical and Quantitative Methods > C2 - Econometric Methods: Single Equation Models; Single Variables > C22 - Time-Series Models
Sets: Research centres and groups > Financial Markets Group (FMG)
Collections > Economists Online
Departments > Economics
Research centres and groups > Suntory and Toyota International Centres for Economics and Related Disciplines (STICERD)
Collections > LSE Financial Markets Group (FMG) Working Papers
Date Deposited: 27 Apr 2007
Last Modified: 27 Feb 2014 15:35
URI: http://eprints.lse.ac.uk/id/eprint/2116

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