Avoid Filling Swiss Cheese with Whipped Cream: Imputation Techniques and Evaluation Procedures for Cross-Country Time Series
IMF Working Papers, June 1, 2011
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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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Bibliographic details
- Authors: Michael Weber, Michaela Denk
- Published: June 1, 2011
- Series: IMF Working Papers
- DOI: https://doi.org/10.5089/9781455270507.001
Overview
- 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.
- Presents statistical methods and quality indicators that enable the (comparative) evaluation of imputation processes and completed datasets.
- 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."
Scope and objectives
- Address the challenge of bridging data gaps for multivariate cross-sectional time series assembled by international organizations from national authorities.
- Focus on countries with not yet well-established statistical systems where data may be incomplete.
- Aim to enable comparative evaluation of imputation processes and completed datasets through statistical methods and quality indicators.
Methods, techniques, and evaluation procedures
- Reviews imputation techniques used for micro data in official statistics for potential applicability to cross-sectional time series.
- Investigates data structures and missing data patterns specific to the cross-sectional time series framework.
- Presents statistical methods and quality indicators for evaluating:
- imputation processes, and
- completed datasets.
Key findings (as described in the paper summary)
- Missing value imputation techniques from micro data contexts are assessed for their applicability to cross-sectional time series.
- Statistical methods and quality indicators are provided to enable comparative evaluation of imputed datasets.