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  • Articles and reports: 12-001-X199300114475
    Description:

    In the creation of micro-simulation databases which are frequently used by policy analysts and planners, several datafiles are combined by statistical matching techniques for enriching the host datafile. This process requires the conditional independence assumption (CIA) which could lead to serious bias in the resulting joint relationships among variables. Appropriate auxiliary information could be used to avoid the CIA. In this report, methods of statistical matching corresponding to three methods of imputation, namely, regression, hot deck, and log linear, with and without auxiliary information are considered. The log linear methods consist of adding categorical constraints to either the regression or hot deck methods. Based on an extensive simulation study with synthetic data, sensitivity analyses for departures from the CIA are performed and gains from using auxiliary information are discussed. Different scenarios for the underlying distribution and relationships, such as symmetric versus skewed data and proxy versus nonproxy auxiliary data, are created using synthetic data. Some recommendations on the use of statistical matching methods are also made. Specifically, it was confirmed that the CIA could be a serious limitation which could be overcome by the use of appropriate auxiliary information. Hot deck methods were found to be generally preferable to regression methods. Also, when auxiliary information is available, log linear categorical constraints can improve performance of hot deck methods. This study was motivated by concerns about the use of the CIA in the construction of the Social Policy Simulation Database at Statistics Canada.

    Release date: 1993-06-15
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  • Articles and reports: 12-001-X199300114475
    Description:

    In the creation of micro-simulation databases which are frequently used by policy analysts and planners, several datafiles are combined by statistical matching techniques for enriching the host datafile. This process requires the conditional independence assumption (CIA) which could lead to serious bias in the resulting joint relationships among variables. Appropriate auxiliary information could be used to avoid the CIA. In this report, methods of statistical matching corresponding to three methods of imputation, namely, regression, hot deck, and log linear, with and without auxiliary information are considered. The log linear methods consist of adding categorical constraints to either the regression or hot deck methods. Based on an extensive simulation study with synthetic data, sensitivity analyses for departures from the CIA are performed and gains from using auxiliary information are discussed. Different scenarios for the underlying distribution and relationships, such as symmetric versus skewed data and proxy versus nonproxy auxiliary data, are created using synthetic data. Some recommendations on the use of statistical matching methods are also made. Specifically, it was confirmed that the CIA could be a serious limitation which could be overcome by the use of appropriate auxiliary information. Hot deck methods were found to be generally preferable to regression methods. Also, when auxiliary information is available, log linear categorical constraints can improve performance of hot deck methods. This study was motivated by concerns about the use of the CIA in the construction of the Social Policy Simulation Database at Statistics Canada.

    Release date: 1993-06-15
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