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  • Articles and reports: 11-522-X202100100020
    Description: Seasonal adjustment of time series at Statistics Canada is performed using the X-12-ARIMA method. For most statistical programs performing seasonal adjustment, subject matter experts (SMEs) are responsible for managing the program and for verification, analysis and dissemination of the data, while methodologists from the Time Series Research and Analysis Center (TSRAC) are responsible for developing and maintaining the seasonal adjustment process and for providing support on seasonal adjustment to SMEs. A visual summary report called the seasonal adjustment dashboard has been developed in R Shiny by the TSRAC to build capacity to interpret seasonally adjusted data and to reduce the resources needed to support seasonal adjustment. It is currently being made available internally to assist SMEs to interpret and explain seasonally adjusted results. The summary report includes graphs of the series across time, as well as summaries of individual seasonal and calendar effects and patterns. Additionally, key seasonal adjustment diagnostics are presented and the net effect of seasonal adjustment is decomposed into its various components. This paper gives a visual representation of the seasonal adjustment process, while demonstrating the dashboard and its interactive functionality.

    Key Words: Time Series; X-12-ARIMA; Summary Report; R Shiny.

    Release date: 2021-10-15

  • Articles and reports: 12-001-X201700254871
    Description:

    In this paper the question is addressed how alternative data sources, such as administrative and social media data, can be used in the production of official statistics. Since most surveys at national statistical institutes are conducted repeatedly over time, a multivariate structural time series modelling approach is proposed to model the series observed by a repeated surveys with related series obtained from such alternative data sources. Generally, this improves the precision of the direct survey estimates by using sample information observed in preceding periods and information from related auxiliary series. This model also makes it possible to utilize the higher frequency of the social media to produce more precise estimates for the sample survey in real time at the moment that statistics for the social media become available but the sample data are not yet available. The concept of cointegration is applied to address the question to which extent the alternative series represent the same phenomena as the series observed with the repeated survey. The methodology is applied to the Dutch Consumer Confidence Survey and a sentiment index derived from social media.

    Release date: 2017-12-21

  • Articles and reports: 12-001-X201700114819
    Description:

    Structural time series models are a powerful technique for variance reduction in the framework of small area estimation (SAE) based on repeatedly conducted surveys. Statistics Netherlands implemented a structural time series model to produce monthly figures about the labour force with the Dutch Labour Force Survey (DLFS). Such models, however, contain unknown hyperparameters that have to be estimated before the Kalman filter can be launched to estimate state variables of the model. This paper describes a simulation aimed at studying the properties of hyperparameter estimators in the model. Simulating distributions of the hyperparameter estimators under different model specifications complements standard model diagnostics for state space models. Uncertainty around the model hyperparameters is another major issue. To account for hyperparameter uncertainty in the mean squared errors (MSE) estimates of the DLFS, several estimation approaches known in the literature are considered in a simulation. Apart from the MSE bias comparison, this paper also provides insight into the variances and MSEs of the MSE estimators considered.

    Release date: 2017-06-22

  • Articles and reports: 12-001-X200900211040
    Description:

    In this paper a multivariate structural time series model is described that accounts for the panel design of the Dutch Labour Force Survey and is applied to estimate monthly unemployment rates. Compared to the generalized regression estimator, this approach results in a substantial increase of the accuracy due to a reduction of the standard error and the explicit modelling of the bias between the subsequent waves.

    Release date: 2009-12-23

  • Articles and reports: 12-001-X19960022979
    Description:

    This paper empirically compares three estimation methods - regression, restricted regression, and principal person - used in a household survey of consumer expenditures. The three methods are applied to post-stratification which is important in many household surveys to adjust for under-coverage of the target population. Post-stratum population counts are typically available from an external census for numbers of persons but not for numbers of households. If household estimates are needed, a single weight must be assigned to each household while using the person counts for post-stratification. This is easily accomplished with regression estimators of totals or means by using person counts in each household's auxiliary data. Restricted regression estimation refines the weights by controlling extremes and can produce estimators with lower variance than Horvitz-Thompson estimators while still adhering to the population controls. The regression methods also allow controls to be used for both person-level counts and quantitative auxiliaries. With the principal person method, persons are classified into post-strata and person weights are ratio adjusted to achieve population control totals. This leads to each person in a household potentially having a different weight. The weight associated with the "principal person" is then selected as the household weight. We will compare estimated means from the three methods and their estimated standard errors for a number of expenditures from the Consumer Expenditure survey sponsored by the U.S. Bureau of Labor Statistics.

    Release date: 1997-01-30
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  • Articles and reports: 11-522-X202100100020
    Description: Seasonal adjustment of time series at Statistics Canada is performed using the X-12-ARIMA method. For most statistical programs performing seasonal adjustment, subject matter experts (SMEs) are responsible for managing the program and for verification, analysis and dissemination of the data, while methodologists from the Time Series Research and Analysis Center (TSRAC) are responsible for developing and maintaining the seasonal adjustment process and for providing support on seasonal adjustment to SMEs. A visual summary report called the seasonal adjustment dashboard has been developed in R Shiny by the TSRAC to build capacity to interpret seasonally adjusted data and to reduce the resources needed to support seasonal adjustment. It is currently being made available internally to assist SMEs to interpret and explain seasonally adjusted results. The summary report includes graphs of the series across time, as well as summaries of individual seasonal and calendar effects and patterns. Additionally, key seasonal adjustment diagnostics are presented and the net effect of seasonal adjustment is decomposed into its various components. This paper gives a visual representation of the seasonal adjustment process, while demonstrating the dashboard and its interactive functionality.

    Key Words: Time Series; X-12-ARIMA; Summary Report; R Shiny.

    Release date: 2021-10-15

  • Articles and reports: 12-001-X201700254871
    Description:

    In this paper the question is addressed how alternative data sources, such as administrative and social media data, can be used in the production of official statistics. Since most surveys at national statistical institutes are conducted repeatedly over time, a multivariate structural time series modelling approach is proposed to model the series observed by a repeated surveys with related series obtained from such alternative data sources. Generally, this improves the precision of the direct survey estimates by using sample information observed in preceding periods and information from related auxiliary series. This model also makes it possible to utilize the higher frequency of the social media to produce more precise estimates for the sample survey in real time at the moment that statistics for the social media become available but the sample data are not yet available. The concept of cointegration is applied to address the question to which extent the alternative series represent the same phenomena as the series observed with the repeated survey. The methodology is applied to the Dutch Consumer Confidence Survey and a sentiment index derived from social media.

    Release date: 2017-12-21

  • Articles and reports: 12-001-X201700114819
    Description:

    Structural time series models are a powerful technique for variance reduction in the framework of small area estimation (SAE) based on repeatedly conducted surveys. Statistics Netherlands implemented a structural time series model to produce monthly figures about the labour force with the Dutch Labour Force Survey (DLFS). Such models, however, contain unknown hyperparameters that have to be estimated before the Kalman filter can be launched to estimate state variables of the model. This paper describes a simulation aimed at studying the properties of hyperparameter estimators in the model. Simulating distributions of the hyperparameter estimators under different model specifications complements standard model diagnostics for state space models. Uncertainty around the model hyperparameters is another major issue. To account for hyperparameter uncertainty in the mean squared errors (MSE) estimates of the DLFS, several estimation approaches known in the literature are considered in a simulation. Apart from the MSE bias comparison, this paper also provides insight into the variances and MSEs of the MSE estimators considered.

    Release date: 2017-06-22

  • Articles and reports: 12-001-X200900211040
    Description:

    In this paper a multivariate structural time series model is described that accounts for the panel design of the Dutch Labour Force Survey and is applied to estimate monthly unemployment rates. Compared to the generalized regression estimator, this approach results in a substantial increase of the accuracy due to a reduction of the standard error and the explicit modelling of the bias between the subsequent waves.

    Release date: 2009-12-23

  • Articles and reports: 12-001-X19960022979
    Description:

    This paper empirically compares three estimation methods - regression, restricted regression, and principal person - used in a household survey of consumer expenditures. The three methods are applied to post-stratification which is important in many household surveys to adjust for under-coverage of the target population. Post-stratum population counts are typically available from an external census for numbers of persons but not for numbers of households. If household estimates are needed, a single weight must be assigned to each household while using the person counts for post-stratification. This is easily accomplished with regression estimators of totals or means by using person counts in each household's auxiliary data. Restricted regression estimation refines the weights by controlling extremes and can produce estimators with lower variance than Horvitz-Thompson estimators while still adhering to the population controls. The regression methods also allow controls to be used for both person-level counts and quantitative auxiliaries. With the principal person method, persons are classified into post-strata and person weights are ratio adjusted to achieve population control totals. This leads to each person in a household potentially having a different weight. The weight associated with the "principal person" is then selected as the household weight. We will compare estimated means from the three methods and their estimated standard errors for a number of expenditures from the Consumer Expenditure survey sponsored by the U.S. Bureau of Labor Statistics.

    Release date: 1997-01-30
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