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Avoid Filling Swiss Cheese with Whipped Cream :, Imputation Techniques and Evaluation Procedures for Cross-Country Time Series, Michael Weber, 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
Table Of Contents
Cover; Contents; I. Introduction; II. Data structures and missing data patterns; Figures; Figure 1. Missing data patterns for standard micro (=observation by variable) data; Figure 2. Missing data patterns for multivariate time series and univariate cross-sectional time series; III. Missing data techniques; Figure 3. Missing data patterns for multivariate cross-sectional time series; A. Traditional approaches; B. Statistical Matching; C. Multiple imputation; IV. Applicability of missing data techniques to time series data; V. Evaluation with statistical quality measures
Language
eng
Literary Form
non fiction
Note
Description based upon print version of record
Physical Description
1 online resource (29 p.)
Specific Material Designation
remote
Form Of Item
online
Isbn
9786613874450

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