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  • Articles and reports: 12-001-X202300100004
    Description: The Dutch Health Survey (DHS), conducted by Statistics Netherlands, is designed to produce reliable direct estimates at an annual frequency. Data collection is based on a combination of web interviewing and face-to-face interviewing. Due to lockdown measures during the Covid-19 pandemic there was no or less face-to-face interviewing possible, which resulted in a sudden change in measurement and selection effects in the survey outcomes. Furthermore, the production of annual data about the effect of Covid-19 on health-related themes with a delay of about one year compromises the relevance of the survey. The sample size of the DHS does not allow the production of figures for shorter reference periods. Both issues are solved by developing a bivariate structural time series model (STM) to estimate quarterly figures for eight key health indicators. This model combines two series of direct estimates, a series based on complete response and a series based on web response only and provides model-based predictions for the indicators that are corrected for the loss of face-to-face interviews during the lockdown periods. The model is also used as a form of small area estimation and borrows sample information observed in previous reference periods. In this way timely and relevant statistics describing the effects of the corona crisis on the development of Dutch health are published. In this paper the method based on the bivariate STM is compared with two alternative methods. The first one uses a univariate STM where no correction for the lack of face-to-face observation is applied to the estimates. The second one uses a univariate STM that also contains an intervention variable that models the effect of the loss of face-to-face response during the lockdown.
    Release date: 2023-06-30

  • Articles and reports: 12-001-X202200200010
    Description:

    Multilevel time series (MTS) models are applied to estimate trends in time series of antenatal care coverage at several administrative levels in Bangladesh, based on repeated editions of the Bangladesh Demographic and Health Survey (BDHS) within the period 1994-2014. MTS models are expressed in an hierarchical Bayesian framework and fitted using Markov Chain Monte Carlo simulations. The models account for varying time lags of three or four years between the editions of the BDHS and provide predictions for the intervening years as well. It is proposed to apply cross-sectional Fay-Herriot models to the survey years separately at district level, which is the most detailed regional level. Time series of these small domain predictions at the district level and their variance-covariance matrices are used as input series for the MTS models. Spatial correlations among districts, random intercept and slope at the district level, and different trend models at district level and higher regional levels are examined in the MTS models to borrow strength over time and space. Trend estimates at district level are obtained directly from the model outputs, while trend estimates at higher regional and national levels are obtained by aggregation of the district level predictions, resulting in a numerically consistent set of trend estimates.

    Release date: 2022-12-15

  • Articles and reports: 12-001-X202100100008
    Description:

    Changes in the design of a repeated survey generally result in systematic effects in the sample estimates, which are further referred to as discontinuities. To avoid confounding real period-to-period change with the effects of a redesign, discontinuities are often quantified by conducting the old and the new design in parallel for some period of time. Sample sizes of such parallel runs are generally too small to apply direct estimators for domain discontinuities. A bivariate hierarchical Bayesian Fay-Herriot (FH) model is proposed to obtain more precise predictions for domain discontinuities and is applied to a redesign of the Dutch Crime Victimization Survey. This method is compared with a univariate FH model where the direct estimates under the regular approach are used as covariates in a FH model for the alternative approach conducted on a reduced sample size and a univariate FH model where the direct estimates for the discontinuities are modeled directly. An adjusted step forward selection procedure is proposed that minimizes the WAIC until the reduction of the WAIC is smaller than the standard error of this criteria. With this approach more parsimonious models are selected, which prevents selecting complex models that tend to overfit the data.

    Release date: 2021-06-24

  • Articles and reports: 12-001-X201900300005
    Description:

    Monthly estimates of provincial unemployment based on the Dutch Labour Force Survey (LFS) are obtained using time series models. The models account for rotation group bias and serial correlation due to the rotating panel design of the LFS. This paper compares two approaches of estimating structural time series models (STM). In the first approach STMs are expressed as state space models, fitted using a Kalman filter and smoother in a frequentist framework. As an alternative, these STMs are expressed as time series multilevel models in an hierarchical Bayesian framework, and estimated using a Gibbs sampler. Monthly unemployment estimates and standard errors based on these models are compared for the twelve provinces of the Netherlands. Pros and cons of the multilevel approach and state space approach are discussed. Multivariate STMs are appropriate to borrow strength over time and space. Modeling the full correlation matrix between time series components rapidly increases the numbers of hyperparameters to be estimated. Modeling common factors is one possibility to obtain more parsimonious models that still account for cross-sectional correlation. In this paper an even more parsimonious approach is proposed, where domains share one overall trend, and have their own independent trends for the domain-specific deviations from this overall trend. The time series modeling approach is particularly appropriate to estimate month-to-month change of unemployment.

    Release date: 2019-12-17

  • 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-X201600114544
    Description:

    In the Netherlands, statistical information about income and wealth is based on two large scale household panels that are completely derived from administrative data. A problem with using households as sampling units in the sample design of panels is the instability of these units over time. Changes in the household composition affect the inclusion probabilities required for design-based and model-assisted inference procedures. Such problems are circumvented in the two aforementioned household panels by sampling persons, who are followed over time. At each period the household members of these sampled persons are included in the sample. This is equivalent to sampling with probabilities proportional to household size where households can be selected more than once but with a maximum equal to the number of household members. In this paper properties of this sample design are described and contrasted with the Generalized Weight Share method for indirect sampling (Lavallée 1995, 2007). Methods are illustrated with an application to the Dutch Regional Income Survey.

    Release date: 2016-06-22

  • Articles and reports: 12-001-X201500214231
    Description:

    Rotating panels are widely applied by national statistical institutes, for example, to produce official statistics about the labour force. Estimation procedures are generally based on traditional design-based procedures known from classical sampling theory. A major drawback of this class of estimators is that small sample sizes result in large standard errors and that they are not robust for measurement bias. Two examples showing the effects of measurement bias are rotation group bias in rotating panels, and systematic differences in the outcome of a survey due to a major redesign of the underlying process. In this paper we apply a multivariate structural time series model to the Dutch Labour Force Survey to produce model-based figures about the monthly labour force. The model reduces the standard errors of the estimates by taking advantage of sample information collected in previous periods, accounts for rotation group bias and autocorrelation induced by the rotating panel, and models discontinuities due to a survey redesign. Additionally, we discuss the use of correlated auxiliary series in the model to further improve the accuracy of the model estimates. The method is applied by Statistics Netherlands to produce accurate official monthly statistics about the labour force that are consistent over time, despite a redesign of the survey process.

    Release date: 2015-12-17

  • Articles and reports: 12-001-X201300211870
    Description:

    At national statistical institutes experiments embedded in ongoing sample surveys are frequently conducted, for example to test the effect of modifications in the survey process on the main parameter estimates of the survey, to quantify the effect of alternative survey implementations on these estimates, or to obtain insight into the various sources of non-sampling errors. A design-based analysis procedure for factorial completely randomized designs and factorial randomized block designs embedded in probability samples is proposed in this paper. Design-based Wald statistics are developed to test whether estimated population parameters, like means, totals and ratios of two population totals, that are observed under the different treatment combinations of the experiment are significantly different. The methods are illustrated with a real life application of an experiment embedded in the Dutch Labor Force Survey.

    Release date: 2014-01-15

  • 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
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Articles and reports (12)

Articles and reports (12) (0 to 10 of 12 results)

  • Articles and reports: 12-001-X202300100004
    Description: The Dutch Health Survey (DHS), conducted by Statistics Netherlands, is designed to produce reliable direct estimates at an annual frequency. Data collection is based on a combination of web interviewing and face-to-face interviewing. Due to lockdown measures during the Covid-19 pandemic there was no or less face-to-face interviewing possible, which resulted in a sudden change in measurement and selection effects in the survey outcomes. Furthermore, the production of annual data about the effect of Covid-19 on health-related themes with a delay of about one year compromises the relevance of the survey. The sample size of the DHS does not allow the production of figures for shorter reference periods. Both issues are solved by developing a bivariate structural time series model (STM) to estimate quarterly figures for eight key health indicators. This model combines two series of direct estimates, a series based on complete response and a series based on web response only and provides model-based predictions for the indicators that are corrected for the loss of face-to-face interviews during the lockdown periods. The model is also used as a form of small area estimation and borrows sample information observed in previous reference periods. In this way timely and relevant statistics describing the effects of the corona crisis on the development of Dutch health are published. In this paper the method based on the bivariate STM is compared with two alternative methods. The first one uses a univariate STM where no correction for the lack of face-to-face observation is applied to the estimates. The second one uses a univariate STM that also contains an intervention variable that models the effect of the loss of face-to-face response during the lockdown.
    Release date: 2023-06-30

  • Articles and reports: 12-001-X202200200010
    Description:

    Multilevel time series (MTS) models are applied to estimate trends in time series of antenatal care coverage at several administrative levels in Bangladesh, based on repeated editions of the Bangladesh Demographic and Health Survey (BDHS) within the period 1994-2014. MTS models are expressed in an hierarchical Bayesian framework and fitted using Markov Chain Monte Carlo simulations. The models account for varying time lags of three or four years between the editions of the BDHS and provide predictions for the intervening years as well. It is proposed to apply cross-sectional Fay-Herriot models to the survey years separately at district level, which is the most detailed regional level. Time series of these small domain predictions at the district level and their variance-covariance matrices are used as input series for the MTS models. Spatial correlations among districts, random intercept and slope at the district level, and different trend models at district level and higher regional levels are examined in the MTS models to borrow strength over time and space. Trend estimates at district level are obtained directly from the model outputs, while trend estimates at higher regional and national levels are obtained by aggregation of the district level predictions, resulting in a numerically consistent set of trend estimates.

    Release date: 2022-12-15

  • Articles and reports: 12-001-X202100100008
    Description:

    Changes in the design of a repeated survey generally result in systematic effects in the sample estimates, which are further referred to as discontinuities. To avoid confounding real period-to-period change with the effects of a redesign, discontinuities are often quantified by conducting the old and the new design in parallel for some period of time. Sample sizes of such parallel runs are generally too small to apply direct estimators for domain discontinuities. A bivariate hierarchical Bayesian Fay-Herriot (FH) model is proposed to obtain more precise predictions for domain discontinuities and is applied to a redesign of the Dutch Crime Victimization Survey. This method is compared with a univariate FH model where the direct estimates under the regular approach are used as covariates in a FH model for the alternative approach conducted on a reduced sample size and a univariate FH model where the direct estimates for the discontinuities are modeled directly. An adjusted step forward selection procedure is proposed that minimizes the WAIC until the reduction of the WAIC is smaller than the standard error of this criteria. With this approach more parsimonious models are selected, which prevents selecting complex models that tend to overfit the data.

    Release date: 2021-06-24

  • Articles and reports: 12-001-X201900300005
    Description:

    Monthly estimates of provincial unemployment based on the Dutch Labour Force Survey (LFS) are obtained using time series models. The models account for rotation group bias and serial correlation due to the rotating panel design of the LFS. This paper compares two approaches of estimating structural time series models (STM). In the first approach STMs are expressed as state space models, fitted using a Kalman filter and smoother in a frequentist framework. As an alternative, these STMs are expressed as time series multilevel models in an hierarchical Bayesian framework, and estimated using a Gibbs sampler. Monthly unemployment estimates and standard errors based on these models are compared for the twelve provinces of the Netherlands. Pros and cons of the multilevel approach and state space approach are discussed. Multivariate STMs are appropriate to borrow strength over time and space. Modeling the full correlation matrix between time series components rapidly increases the numbers of hyperparameters to be estimated. Modeling common factors is one possibility to obtain more parsimonious models that still account for cross-sectional correlation. In this paper an even more parsimonious approach is proposed, where domains share one overall trend, and have their own independent trends for the domain-specific deviations from this overall trend. The time series modeling approach is particularly appropriate to estimate month-to-month change of unemployment.

    Release date: 2019-12-17

  • 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-X201600114544
    Description:

    In the Netherlands, statistical information about income and wealth is based on two large scale household panels that are completely derived from administrative data. A problem with using households as sampling units in the sample design of panels is the instability of these units over time. Changes in the household composition affect the inclusion probabilities required for design-based and model-assisted inference procedures. Such problems are circumvented in the two aforementioned household panels by sampling persons, who are followed over time. At each period the household members of these sampled persons are included in the sample. This is equivalent to sampling with probabilities proportional to household size where households can be selected more than once but with a maximum equal to the number of household members. In this paper properties of this sample design are described and contrasted with the Generalized Weight Share method for indirect sampling (Lavallée 1995, 2007). Methods are illustrated with an application to the Dutch Regional Income Survey.

    Release date: 2016-06-22

  • Articles and reports: 12-001-X201500214231
    Description:

    Rotating panels are widely applied by national statistical institutes, for example, to produce official statistics about the labour force. Estimation procedures are generally based on traditional design-based procedures known from classical sampling theory. A major drawback of this class of estimators is that small sample sizes result in large standard errors and that they are not robust for measurement bias. Two examples showing the effects of measurement bias are rotation group bias in rotating panels, and systematic differences in the outcome of a survey due to a major redesign of the underlying process. In this paper we apply a multivariate structural time series model to the Dutch Labour Force Survey to produce model-based figures about the monthly labour force. The model reduces the standard errors of the estimates by taking advantage of sample information collected in previous periods, accounts for rotation group bias and autocorrelation induced by the rotating panel, and models discontinuities due to a survey redesign. Additionally, we discuss the use of correlated auxiliary series in the model to further improve the accuracy of the model estimates. The method is applied by Statistics Netherlands to produce accurate official monthly statistics about the labour force that are consistent over time, despite a redesign of the survey process.

    Release date: 2015-12-17

  • Articles and reports: 12-001-X201300211870
    Description:

    At national statistical institutes experiments embedded in ongoing sample surveys are frequently conducted, for example to test the effect of modifications in the survey process on the main parameter estimates of the survey, to quantify the effect of alternative survey implementations on these estimates, or to obtain insight into the various sources of non-sampling errors. A design-based analysis procedure for factorial completely randomized designs and factorial randomized block designs embedded in probability samples is proposed in this paper. Design-based Wald statistics are developed to test whether estimated population parameters, like means, totals and ratios of two population totals, that are observed under the different treatment combinations of the experiment are significantly different. The methods are illustrated with a real life application of an experiment embedded in the Dutch Labor Force Survey.

    Release date: 2014-01-15

  • 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
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