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Author/Editor:
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Denk, Michaela ; Weber, Michael
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Publication Date:
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June 01, 2011
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Electronic Access:
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Free Full text
(PDF file size is 918KB).
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Disclaimer: This Working Paper should not be reported as representing the views of the IMF.
The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate
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Summary:
International organizations collect data from national authorities to create multivariate cross-sectional time series for their analyses. As data from countries with not yet well-established statistical systems may be incomplete, the bridging of data gaps is a crucial challenge. This paper investigates data structures and missing data patterns in the cross-sectional time series framework, reviews missing value imputation techniques used for micro data in official statistics, and discusses their applicability to cross-sectional time series. It presents statistical methods and quality indicators that enable the (comparative) evaluation of imputation processes and completed datasets.
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Order a print copy
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Series:
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Working Paper No. 11/151
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Subject(s):
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Data collection | Time series | Statistics
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Author's Keyword(s):
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Missing or incomplete data | imputation quality | statistical matching |
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