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All (5)

All (5) ((5 results))

  • 1. Estimating TFP in the Presence of Outliers and Leverage Points: An Examination of the KLEMS Dataset Archived
    Articles and reports: 11F0027M2007047
    Geography: Canada
    Description:

    This paper examines the effect of aberrant observations in the Capital, Labour, Energy, Materials and Services (KLEMS) database and a method for dealing with them. The level of disaggregation, data construction and economic shocks all potentially lead to aberrant observations that can influence estimates and inference if care is not exercised. Commonly applied pre-tests, such as the augmented Dickey-Fuller and the Kwaitkowski, Phillips, Schmidt and Shin tests, need to be used with caution in this environment because they are sensitive to unusual data points. Moreover, widely known methods for generating statistical estimates, such as Ordinary Least Squares, may not work well when confronted with aberrant observations. To address this, a robust method for estimating statistical relationships is illustrated.

    Release date: 2007-12-05
  • 2. Finding and Using Statistics Archived
    Surveys and statistical programs – Documentation: 11-533-X
    Description:

    This guide has been created especially for users needing a step-by-step review on how to find, read and use data, with quick tips on locating information on the Statistics Canada website. Originally published in paper format in the 1980s, revised as part of the 1994 Statistics Canada Catalogue, and then transformed into an electronic version, this guide is continually being updated to maintain its currency and usefulness.

    Release date: 2007-11-19
  • 3. Communicating variances for interpretation of changes and turning points in repeated surveys Archived
    Articles and reports: 11-522-X20050019460
    Description:

    Users will analyse and interpret the time series of estimates in various ways often involving estimates for several time periods. Despite the large sample sizes and degree of overlap between the sample for some periods the sampling errors can still substantially affect the estimates of movements and functions of them used to interpret the series of estimates. We consider how to account for sampling errors in the interpretation of the estimates from repeated surveys and how to inform the users and analysts of their possible impact.

    Release date: 2007-03-02
  • 4. Dealing with missing survey data in longitudinal analysis Archived
    Articles and reports: 11-522-X20050019467
    Description:

    This paper reviews techniques for dealing with missing data from complex surveys when conducting longitudinal analysis. In addition to incurring the same types of missingness as cross sectional data, longitudinal observations also suffer from drop out missingness. For the purpose of analyzing longitudinal data, random effects models are most often used to account for the longitudinal nature of the data. However, there are difficulties in incorporating the complex design with typical multi-level models that are used in this type of longitudinal analysis, especially in the presence of drop-out missingness.

    Release date: 2007-03-02
  • 5. Taking stock: the future of longitudinal surveys Archived
    Articles and reports: 11-522-X20050019469
    Description:

    The 1990s was the decade of longitudinal surveys in Canada. The focus was squarely on the benefits that could be derived from the increased analytical power of longitudinal surveys. This presentation explores issues of insights gained, timeliness, data access, survey design, complexity, research capacity, survey governance and knowledge mobilisation. This presentation outlines some of the issues that are likely to be raised in any debate regarding longitudinal surveys.

    Release date: 2007-03-02
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Analysis (4)

Analysis (4) ((4 results))

  • 1. Estimating TFP in the Presence of Outliers and Leverage Points: An Examination of the KLEMS Dataset Archived
    Articles and reports: 11F0027M2007047
    Geography: Canada
    Description:

    This paper examines the effect of aberrant observations in the Capital, Labour, Energy, Materials and Services (KLEMS) database and a method for dealing with them. The level of disaggregation, data construction and economic shocks all potentially lead to aberrant observations that can influence estimates and inference if care is not exercised. Commonly applied pre-tests, such as the augmented Dickey-Fuller and the Kwaitkowski, Phillips, Schmidt and Shin tests, need to be used with caution in this environment because they are sensitive to unusual data points. Moreover, widely known methods for generating statistical estimates, such as Ordinary Least Squares, may not work well when confronted with aberrant observations. To address this, a robust method for estimating statistical relationships is illustrated.

    Release date: 2007-12-05
  • 2. Communicating variances for interpretation of changes and turning points in repeated surveys Archived
    Articles and reports: 11-522-X20050019460
    Description:

    Users will analyse and interpret the time series of estimates in various ways often involving estimates for several time periods. Despite the large sample sizes and degree of overlap between the sample for some periods the sampling errors can still substantially affect the estimates of movements and functions of them used to interpret the series of estimates. We consider how to account for sampling errors in the interpretation of the estimates from repeated surveys and how to inform the users and analysts of their possible impact.

    Release date: 2007-03-02
  • 3. Dealing with missing survey data in longitudinal analysis Archived
    Articles and reports: 11-522-X20050019467
    Description:

    This paper reviews techniques for dealing with missing data from complex surveys when conducting longitudinal analysis. In addition to incurring the same types of missingness as cross sectional data, longitudinal observations also suffer from drop out missingness. For the purpose of analyzing longitudinal data, random effects models are most often used to account for the longitudinal nature of the data. However, there are difficulties in incorporating the complex design with typical multi-level models that are used in this type of longitudinal analysis, especially in the presence of drop-out missingness.

    Release date: 2007-03-02
  • 4. Taking stock: the future of longitudinal surveys Archived
    Articles and reports: 11-522-X20050019469
    Description:

    The 1990s was the decade of longitudinal surveys in Canada. The focus was squarely on the benefits that could be derived from the increased analytical power of longitudinal surveys. This presentation explores issues of insights gained, timeliness, data access, survey design, complexity, research capacity, survey governance and knowledge mobilisation. This presentation outlines some of the issues that are likely to be raised in any debate regarding longitudinal surveys.

    Release date: 2007-03-02
Reference (1)

Reference (1) ((1 result))

  • 1. Finding and Using Statistics Archived
    Surveys and statistical programs – Documentation: 11-533-X
    Description:

    This guide has been created especially for users needing a step-by-step review on how to find, read and use data, with quick tips on locating information on the Statistics Canada website. Originally published in paper format in the 1980s, revised as part of the 1994 Statistics Canada Catalogue, and then transformed into an electronic version, this guide is continually being updated to maintain its currency and usefulness.

    Release date: 2007-11-19
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