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- 1. Social media as a data source for official statistics; the Dutch Consumer Confidence Index ArchivedArticles and reports: 12-001-X201700254871Description:
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-X201700114819Description:
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-X201700114820Description:
Measurement errors can induce bias in the estimation of transitions, leading to erroneous conclusions about labour market dynamics. Traditional literature on gross flows estimation is based on the assumption that measurement errors are uncorrelated over time. This assumption is not realistic in many contexts, because of survey design and data collection strategies. In this work, we use a model-based approach to correct observed gross flows from classification errors with latent class Markov models. We refer to data collected with the Italian Continuous Labour Force Survey, which is cross-sectional, quarterly, with a 2-2-2 rotating design. The questionnaire allows us to use multiple indicators of labour force conditions for each quarter: two collected in the first interview, and a third collected one year later. Our approach provides a method to estimate labour market mobility, taking into account correlated errors and the rotating design of the survey. The best-fitting model is a mixed latent class Markov model with covariates affecting latent transitions and correlated errors among indicators; the mixture components are of mover-stayer type. The better fit of the mixture specification is due to more accurately estimated latent transitions.
Release date: 2017-06-22 - Articles and reports: 89-657-X2016002Geography: Census metropolitan areaDescription:
This study examines the settlement patterns of the immigrant population as well as certain social integration components. It starts by outlining recent trends in the settlement patterns of the immigrant population in Canadian census metropolitan areas, namely Toronto, Montréal and Vancouver. Based on data from the 2013 General Social Survey on Social Identity, it then looks at residence characteristics, such as type of municipality and concentration of immigrant population, according to four social integration components: personal network characteristics, relationships with neighbours, social participation and involvement in community activities, and sense of belonging.
Release date: 2017-05-08 - Articles and reports: 82-003-X201700314781Description: This study describes residential exposure to ambient fine particulate matter, by visible minority, immigrant and socioeconomic status in Canada, while stratifying the analysis across the urban-rural divide.Release date: 2017-03-15
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Articles and reports (5)
Articles and reports (5) ((5 results))
- 1. Social media as a data source for official statistics; the Dutch Consumer Confidence Index ArchivedArticles and reports: 12-001-X201700254871Description:
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-X201700114819Description:
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-X201700114820Description:
Measurement errors can induce bias in the estimation of transitions, leading to erroneous conclusions about labour market dynamics. Traditional literature on gross flows estimation is based on the assumption that measurement errors are uncorrelated over time. This assumption is not realistic in many contexts, because of survey design and data collection strategies. In this work, we use a model-based approach to correct observed gross flows from classification errors with latent class Markov models. We refer to data collected with the Italian Continuous Labour Force Survey, which is cross-sectional, quarterly, with a 2-2-2 rotating design. The questionnaire allows us to use multiple indicators of labour force conditions for each quarter: two collected in the first interview, and a third collected one year later. Our approach provides a method to estimate labour market mobility, taking into account correlated errors and the rotating design of the survey. The best-fitting model is a mixed latent class Markov model with covariates affecting latent transitions and correlated errors among indicators; the mixture components are of mover-stayer type. The better fit of the mixture specification is due to more accurately estimated latent transitions.
Release date: 2017-06-22 - Articles and reports: 89-657-X2016002Geography: Census metropolitan areaDescription:
This study examines the settlement patterns of the immigrant population as well as certain social integration components. It starts by outlining recent trends in the settlement patterns of the immigrant population in Canadian census metropolitan areas, namely Toronto, Montréal and Vancouver. Based on data from the 2013 General Social Survey on Social Identity, it then looks at residence characteristics, such as type of municipality and concentration of immigrant population, according to four social integration components: personal network characteristics, relationships with neighbours, social participation and involvement in community activities, and sense of belonging.
Release date: 2017-05-08 - Articles and reports: 82-003-X201700314781Description: This study describes residential exposure to ambient fine particulate matter, by visible minority, immigrant and socioeconomic status in Canada, while stratifying the analysis across the urban-rural divide.Release date: 2017-03-15
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