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Nonparametric inference for unbalanced time series data

By Oliver Linton

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

Topics: HB Economic Theory
Publisher: Suntory and Toyota International Centres for Economics and Related Disciplines, London School of Economics and Political Science
Year: 2004
OAI identifier: oai:eprints.lse.ac.uk:2116
Provided by: LSE Research Online

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  1. (1993). A test for the number of factors in an approximate factor model. doi
  2. (2000). Consistency of kernel estimators of heteroskedastic and autocorrelated covariance matrices. doi
  3. (1992). Consistent covariance matrix estimation for dependent heterogenous processes. doi
  4. (2003). Consistent Testing for Stochastic Dominance under General Sampling Schemes. Cowles Foundation Discussion Paper. doi
  5. (1993). Efficient and Adaptive Estimation for Semiparametric Models. doi
  6. (1991). Efficient estimation of linear functionals. doi
  7. (2004). HAC Estimation by Automated Regression. doi
  8. (1991). Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation. doi
  9. (1954). On the estimation of regression coefficients in the case of an autocorrelated disturbance. doi
  10. (2003). Resampling methods for Dependent data. doi
  11. (2004). Robust Covariance Matrix Estimation: “HAC” Estimates with Long Memory/Antipersistence Correction. STICERD discussion paper doi
  12. (1987). Statistical Analysis with Missing Data.
  13. (1999). Subsampling, doi

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