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COVID-19 A data perspective

COVID-19: A data perspective: Explore key economic trends and social challenges that arise as the COVID-19 situation evolves.

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  • Articles and reports: 12-001-X199400214419
    Description:

    The study was undertaken to evaluate some alternative small areas estimators to produce level estimates for unplanned domains from the Italian Labour Force Sample Survey. In our study, the small areas are the Health Service Areas, which are unplanned sub-regional territorial domains and were not isolated at the time of sample design and thus cut across boundaries of the design strata. We consider the following estimators: post-stratified ratio, synthetic, composite expressed as linear combination of synthetic and of post-stratified ratio, and sample size dependent. For all the estimators considered in this study, the average percent relative biases and the average relative mean square errors were obtained in a Monte Carlo study in which the sample design was simulated using data from the 1981 Italian Census.

    Release date: 1994-12-15

  • Articles and reports: 12-001-X199400214423
    Description:

    Most surveys suffer from the problem of missing data caused by nonresponse. To deal with this problem, imputation is often used to create a “completed data set”, that is, a data set composed of actual observations (for the respondents) and imputations (for the nonrespondents). Usually, imputation is carried out under the assumption of unconfounded response mechanism. When this assumption does not hold, a bias is introduced in the standard estimator of the population mean calculated from the completed data set. In this paper, we pursue the idea of using simple correction factors for the bias problem in the case that ratio imputation is used. The effectiveness of the correction factors is studied by Monte Carlo simulation using artificially generated data sets representing various super-populations, nonresponse rates, nonresponse mechanisms, and correlations between the variable of interest and the auxiliary variable. These correction factors are found to be effective especially when the population follows the model underlying ratio imputation. An option for estimating the variance of the corrected point estimates is also discussed.

    Release date: 1994-12-15

  • Articles and reports: 75-001-X19940041575
    Geography: Canada
    Description:

    An analysis of families in the top percentile of the income distribution, focusing on their sources of income.

    Release date: 1994-12-14
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Articles and reports (3)

Articles and reports (3) ((3 results))

  • Articles and reports: 12-001-X199400214419
    Description:

    The study was undertaken to evaluate some alternative small areas estimators to produce level estimates for unplanned domains from the Italian Labour Force Sample Survey. In our study, the small areas are the Health Service Areas, which are unplanned sub-regional territorial domains and were not isolated at the time of sample design and thus cut across boundaries of the design strata. We consider the following estimators: post-stratified ratio, synthetic, composite expressed as linear combination of synthetic and of post-stratified ratio, and sample size dependent. For all the estimators considered in this study, the average percent relative biases and the average relative mean square errors were obtained in a Monte Carlo study in which the sample design was simulated using data from the 1981 Italian Census.

    Release date: 1994-12-15

  • Articles and reports: 12-001-X199400214423
    Description:

    Most surveys suffer from the problem of missing data caused by nonresponse. To deal with this problem, imputation is often used to create a “completed data set”, that is, a data set composed of actual observations (for the respondents) and imputations (for the nonrespondents). Usually, imputation is carried out under the assumption of unconfounded response mechanism. When this assumption does not hold, a bias is introduced in the standard estimator of the population mean calculated from the completed data set. In this paper, we pursue the idea of using simple correction factors for the bias problem in the case that ratio imputation is used. The effectiveness of the correction factors is studied by Monte Carlo simulation using artificially generated data sets representing various super-populations, nonresponse rates, nonresponse mechanisms, and correlations between the variable of interest and the auxiliary variable. These correction factors are found to be effective especially when the population follows the model underlying ratio imputation. An option for estimating the variance of the corrected point estimates is also discussed.

    Release date: 1994-12-15

  • Articles and reports: 75-001-X19940041575
    Geography: Canada
    Description:

    An analysis of families in the top percentile of the income distribution, focusing on their sources of income.

    Release date: 1994-12-14
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