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Avoid Filling Swiss Cheese with Whipped Cream: Imputation Techniques and Evaluation Procedures for Cross-Country Time Series

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  • Mr. Michael Weber
  • Ms. Michaela Denk

Abstract

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.

Suggested Citation

  • Mr. Michael Weber & Ms. Michaela Denk, 2011. "Avoid Filling Swiss Cheese with Whipped Cream: Imputation Techniques and Evaluation Procedures for Cross-Country Time Series," IMF Working Papers 2011/151, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2011/151
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    References listed on IDEAS

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    1. Ferreiro, Osvaldo, 1987. "Methodologies for the estimation of missing observations in time series," Statistics & Probability Letters, Elsevier, vol. 5(1), pages 65-69, January.
    2. Greg Kaplan & Sam Schulhofer-Wohl, 2012. "Interstate Migration Has Fallen Less Than You Think: Consequences of Hot Deck Imputation in the Current Population Survey," Demography, Springer;Population Association of America (PAA), vol. 49(3), pages 1061-1074, August.
    3. Agostino Tarsitano & Marianna Falcone, 2010. "Missing-Values Adjustment For Mixed-Type Data," Working Papers 201015, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
    4. Rubin, Donald B, 1986. "Statistical Matching Using File Concatenation with Adjusted Weights and Multiple Imputations," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 87-94, January.
    5. Andrew Gelman & Iven Van Mechelen & Geert Verbeke & Daniel F. Heitjan & Michel Meulders, 2005. "Multiple Imputation for Model Checking: Completed-Data Plots with Missing and Latent Data," Biometrics, The International Biometric Society, vol. 61(1), pages 74-85, March.
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    Cited by:

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    2. Katrin Wölfel & Christoph S. Weber, 2017. "Searching for the Fed’s reaction function," Empirical Economics, Springer, vol. 52(1), pages 191-227, February.
    3. David N Margolis, 2014. "By Choice and by Necessity: Entrepreneurship and Self-Employment in the Developing World," The European Journal of Development Research, Palgrave Macmillan;European Association of Development Research and Training Institutes (EADI), vol. 26(4), pages 419-436, September.

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