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150128 ||| eng |
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|a 9781455270507
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| 100 |
1 |
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|a Weber, Michael
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| 245 |
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|a Avoid Filling Swiss Cheese with Whipped Cream
|b Imputation Techniques and Evaluation Procedures for Cross-Country Time Series
|c Michael Weber, Michaela Denk
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| 260 |
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|a Washington, D.C.
|b International Monetary Fund
|c 2011
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| 300 |
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|a 27 pages
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| 653 |
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|a Data processing
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| 653 |
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|a Public expenditure review
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| 653 |
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|a Databases
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| 653 |
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|a Public finance & taxation
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| 653 |
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|a Economic statistics
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| 653 |
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|a Data Collection and Data Estimation Methodology
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| 653 |
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|a Data Processing
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| 653 |
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|a National Government Expenditures and Related Policies: General
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| 653 |
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|a Data capture & analysis
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| 653 |
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|a Data collection
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| 653 |
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|a Expenditures, Public
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| 653 |
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|a Computer Programs: General
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| 653 |
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|a Electronic data processing
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| 653 |
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|a Public Finance
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| 700 |
1 |
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|a Denk, Michaela
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| 041 |
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|a eng
|2 ISO 639-2
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| 989 |
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|b IMF
|a International Monetary Fund
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| 490 |
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|a IMF Working Papers
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| 028 |
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|a 10.5089/9781455270507.001
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| 856 |
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|u https://elibrary.imf.org/view/journals/001/2011/151/001.2011.issue-151-en.xml?cid=25007-com-dsp-marc
|x Verlag
|3 Volltext
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|a 330
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| 520 |
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|a 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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