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- Articles and reports: 12-001-X200900211040Description:
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 - 2. An integrated modeling approach to unemployment rate estimation for sub-provincial areas of Canada ArchivedArticles and reports: 12-001-X200800110614Geography: CanadaDescription:
The Canadian Labour Force Survey (LFS) produces monthly estimates of the unemployment rate at national and provincial levels. The LFS also releases unemployment estimates for sub-provincial areas such as Census Metropolitan Areas (CMAs) and Urban Centers (UCs). However, for some sub-provincial areas, the direct estimates are not reliable since the sample size in some areas is quite small. The small area estimation in LFS concerns estimation of unemployment rates for local sub-provincial areas such as CMA/UCs using small area models. In this paper, we will discuss various models including the Fay-Herriot model and cross-sectional and time series models. In particular, an integrated non-linear mixed effects model will be proposed under the hierarchical Bayes (HB) framework for the LFS unemployment rate estimation. Monthly Employment Insurance (EI) beneficiary data at the CMA/UC level are used as auxiliary covariates in the model. A HB approach with the Gibbs sampling method is used to obtain the estimates of posterior means and posterior variances of the CMA/UC level unemployment rates. The proposed HB model leads to reliable model-based estimates in terms of CV reduction. Model fit analysis and comparison of the model-based estimates with the direct estimates are presented in the paper.
Release date: 2008-06-26 - 3. An analysis of classification error for the revised Current Population Survey Employment questions ArchivedArticles and reports: 12-001-X20040027747Description:
The reduced accuracy of the revised classification of unemployed persons in the Current Population Survey (CPS) was documented in Biemer and Bushery (2000). In this paper, we provide additional evidence of this anomaly and attempt to trace the source of the error through extended analysis of the CPS data before and after the redesign. The paper presents an novel approach decomposing the error in a complex classification process, such as the CPS labor force status classification, using Markov Latent Class Analysis (MLCA). To identify the cause of the apparent reduction in unemployed classification accuracy, we identify the key question components that determine the classifications and estimate the contribution of each of these question components to the total error in the classification process. This work provides guidance for further investigation into the root causes of the errors in the collection of labor force data in the CPS possibly through cognitive laboratory and/or field experiments.
Release date: 2005-02-03 - 4. Model-based unemployment rate estimation for the Canadian Labour Force Survey: A hierarchical Bayes approach ArchivedArticles and reports: 12-001-X20030016602Description:
The Canadian Labour Force Survey (LFS) produces monthly direct estimates of the unemployment rate at national and provincial levels. The LFS also releases unemployment estimates for subprovincial areas such as census metropolitan areas (CMAs) and census agglomerations (CAs). However, for some subprovincial areas, the direct estimates are not very reliable since the sample size in some areas is quite small. In this paper, a cross-sectional and time-series model is used to borrow strength across areas and time periods to produce model-based unemployment rate estimates for CMAs and CAs. This model is a generalization of a widely used cross-sectional model in small area estimation and includes a random walk or AR(1) model for the random time component. Monthly Employment Insurance (EI) beneficiary data at the CMA or CA level are used as auxiliary covariates in the model. A hierarchical Bayes (HB) approach is employed and the Gibbs sampler is used to generate samples from the joint posterior distribution. Rao-Blackwellized estimators are obtained for the posterior means and posterior variances of the CMA/CA-level unemployment rates. The HB method smoothes the survey estimates and leads to a substantial reduction in standard errors. Base on posterior distributions, bayesian model fitting is also investigated in this paper.
Release date: 2003-07-31 - Articles and reports: 12-001-X20010026097Description:
A compositional time series is defined as a multivariate time series in which each of the series has values bounded between zero and one and the sum of the series equals one at each time point. Data with such characteristics are observed in repeated surveys when a survey variable has a multinomial response but interest lies in the proportion of units classified in each of its categories. In this case, the survey estimates are proportions of a whole subject to a unity-sum constraint. In this paper we employ a state space approach for modelling compositional time series from repeated surveys taking into account the sampling errors. The additive logistic transformation is used in order to guarantee predictions and signal estimates bounded between zero and one which satisfy the unity-sum constraint. The method is applied to compositional data from the Brazilian Labour Force Survey. Estimates of the vector of proportions and the unemployment rate are obtained. In addition, the structural components of the signal vector, such as the seasonals and the trends, are produced.
Release date: 2002-02-28 - 6. Regression composite estimation for the Canadian Labour Force Survey: Evaluation and implementation ArchivedArticles and reports: 12-001-X20010015855Description:
The Canadian Labour Force Survey (LFS) is a monthly survey with a complex rotating panel design. After extensive studies, including the investigation of a number of alternative methods for exploiting the sample overlap to improve the quality of estimates, the LFS has chosen a composite estimation method which achieves this goal while satisfying practical constraints. In addition, for variables where there is a substantial gain in efficiency, the new time series tend to make more sense from a subject-matter perspective. This makes it easier to explain LFS estimates to users and the media. Because of the reduced variance under composite estimation, for some variables it is now possible to publish monthly estimates where only three-month moving averages were published in the past. In addition, a greater number of series can be successfully seasonally adjusted.
Release date: 2001-08-22 - 7. Geographic Structures As Census Variables: Using Geography to Analyse Social and Economic Processes ArchivedArticles and reports: 92F0138M2001001Description:
Traditionally, Statistics Canada uses standard geographic areas as "containers" for the dissemination of statistical data. However, geographic structures are often used as variables in general applications, for example, to document the rural and urban population in a specific area such as an incorporated municipality (census subdivision). They are not often cross-tabulated with each other to illustrate and analyse specific social and economic processes, for example, the settlement patterns of the population inside and outside of larger urban centres broken down by urban and rural areas.The introduction of the census metropolitan area and census agglomeration influenced zone (MIZ) concept presents additional opportunities to use geographic structures as variables to analyse census data.The objectives of this working paper are to illustrate the advantages of using geographic structures as variables to better analyse social and economic processes and to initiate a discussion in the user community about using these variables and the potential of this largely untapped capability of the Census databases. In order to achieve these objectives, four examples of geography as a variable are presented. The examples include Aboriginal persons living on-reserve and off-reserve in urban and rural areas in Canada, the unemployment rate of persons living in urban and rural areas in Canada, the gross rent of renter households in urban and rural areas in Canada, and the migration flows of persons 15 to 24 years of age between major urban centres and rural and small town areas (MIZ).Our intent is to encourage the use of geographic structures as census variables in order to provide users with the tools that will enable them to more accurately analyse the social and economic processes that take place in the geographic areas of Canada.
Release date: 2001-03-16 - Articles and reports: 12-001-X19980024353Description:
This paper studies response errors in the Current Population Survey of the U.S. Bureau of the Census and assesses their impact on the unemployment rates published by the Bureau of Labour Statistics. The measurement of these error rates is obtained from reinterview data, using an extension of the Hui and Walter (1980) procedure for the evaluation of diagnostic tests. Unlike prior studies which assumed that the reconciled reinterview yields the true status, the method estimates the error rates in both interviews. Using these estimated error rates, we show that the misclassification in the original survey creates a cyclical effect on the reported estimated unemployment rates. In particular, the degress of underestimation increases when true unemployment is high. As there was insufficient data to distinguish between a model assuming that the misclassification rates are the same throughout the business cycle, and one that allows the error rates to differ in periods of low, moderate and high unemployment, our findings should be regarded as preliminary. Nonetheless, they indicated that the relationship between the models used to assess the accuracy of diagnostic tests, and those measuring misclassification rates of survey data, deserves further study.
Release date: 1999-01-14
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Analysis (8)
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- Articles and reports: 12-001-X200900211040Description:
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 - 2. An integrated modeling approach to unemployment rate estimation for sub-provincial areas of Canada ArchivedArticles and reports: 12-001-X200800110614Geography: CanadaDescription:
The Canadian Labour Force Survey (LFS) produces monthly estimates of the unemployment rate at national and provincial levels. The LFS also releases unemployment estimates for sub-provincial areas such as Census Metropolitan Areas (CMAs) and Urban Centers (UCs). However, for some sub-provincial areas, the direct estimates are not reliable since the sample size in some areas is quite small. The small area estimation in LFS concerns estimation of unemployment rates for local sub-provincial areas such as CMA/UCs using small area models. In this paper, we will discuss various models including the Fay-Herriot model and cross-sectional and time series models. In particular, an integrated non-linear mixed effects model will be proposed under the hierarchical Bayes (HB) framework for the LFS unemployment rate estimation. Monthly Employment Insurance (EI) beneficiary data at the CMA/UC level are used as auxiliary covariates in the model. A HB approach with the Gibbs sampling method is used to obtain the estimates of posterior means and posterior variances of the CMA/UC level unemployment rates. The proposed HB model leads to reliable model-based estimates in terms of CV reduction. Model fit analysis and comparison of the model-based estimates with the direct estimates are presented in the paper.
Release date: 2008-06-26 - 3. An analysis of classification error for the revised Current Population Survey Employment questions ArchivedArticles and reports: 12-001-X20040027747Description:
The reduced accuracy of the revised classification of unemployed persons in the Current Population Survey (CPS) was documented in Biemer and Bushery (2000). In this paper, we provide additional evidence of this anomaly and attempt to trace the source of the error through extended analysis of the CPS data before and after the redesign. The paper presents an novel approach decomposing the error in a complex classification process, such as the CPS labor force status classification, using Markov Latent Class Analysis (MLCA). To identify the cause of the apparent reduction in unemployed classification accuracy, we identify the key question components that determine the classifications and estimate the contribution of each of these question components to the total error in the classification process. This work provides guidance for further investigation into the root causes of the errors in the collection of labor force data in the CPS possibly through cognitive laboratory and/or field experiments.
Release date: 2005-02-03 - 4. Model-based unemployment rate estimation for the Canadian Labour Force Survey: A hierarchical Bayes approach ArchivedArticles and reports: 12-001-X20030016602Description:
The Canadian Labour Force Survey (LFS) produces monthly direct estimates of the unemployment rate at national and provincial levels. The LFS also releases unemployment estimates for subprovincial areas such as census metropolitan areas (CMAs) and census agglomerations (CAs). However, for some subprovincial areas, the direct estimates are not very reliable since the sample size in some areas is quite small. In this paper, a cross-sectional and time-series model is used to borrow strength across areas and time periods to produce model-based unemployment rate estimates for CMAs and CAs. This model is a generalization of a widely used cross-sectional model in small area estimation and includes a random walk or AR(1) model for the random time component. Monthly Employment Insurance (EI) beneficiary data at the CMA or CA level are used as auxiliary covariates in the model. A hierarchical Bayes (HB) approach is employed and the Gibbs sampler is used to generate samples from the joint posterior distribution. Rao-Blackwellized estimators are obtained for the posterior means and posterior variances of the CMA/CA-level unemployment rates. The HB method smoothes the survey estimates and leads to a substantial reduction in standard errors. Base on posterior distributions, bayesian model fitting is also investigated in this paper.
Release date: 2003-07-31 - Articles and reports: 12-001-X20010026097Description:
A compositional time series is defined as a multivariate time series in which each of the series has values bounded between zero and one and the sum of the series equals one at each time point. Data with such characteristics are observed in repeated surveys when a survey variable has a multinomial response but interest lies in the proportion of units classified in each of its categories. In this case, the survey estimates are proportions of a whole subject to a unity-sum constraint. In this paper we employ a state space approach for modelling compositional time series from repeated surveys taking into account the sampling errors. The additive logistic transformation is used in order to guarantee predictions and signal estimates bounded between zero and one which satisfy the unity-sum constraint. The method is applied to compositional data from the Brazilian Labour Force Survey. Estimates of the vector of proportions and the unemployment rate are obtained. In addition, the structural components of the signal vector, such as the seasonals and the trends, are produced.
Release date: 2002-02-28 - 6. Regression composite estimation for the Canadian Labour Force Survey: Evaluation and implementation ArchivedArticles and reports: 12-001-X20010015855Description:
The Canadian Labour Force Survey (LFS) is a monthly survey with a complex rotating panel design. After extensive studies, including the investigation of a number of alternative methods for exploiting the sample overlap to improve the quality of estimates, the LFS has chosen a composite estimation method which achieves this goal while satisfying practical constraints. In addition, for variables where there is a substantial gain in efficiency, the new time series tend to make more sense from a subject-matter perspective. This makes it easier to explain LFS estimates to users and the media. Because of the reduced variance under composite estimation, for some variables it is now possible to publish monthly estimates where only three-month moving averages were published in the past. In addition, a greater number of series can be successfully seasonally adjusted.
Release date: 2001-08-22 - 7. Geographic Structures As Census Variables: Using Geography to Analyse Social and Economic Processes ArchivedArticles and reports: 92F0138M2001001Description:
Traditionally, Statistics Canada uses standard geographic areas as "containers" for the dissemination of statistical data. However, geographic structures are often used as variables in general applications, for example, to document the rural and urban population in a specific area such as an incorporated municipality (census subdivision). They are not often cross-tabulated with each other to illustrate and analyse specific social and economic processes, for example, the settlement patterns of the population inside and outside of larger urban centres broken down by urban and rural areas.The introduction of the census metropolitan area and census agglomeration influenced zone (MIZ) concept presents additional opportunities to use geographic structures as variables to analyse census data.The objectives of this working paper are to illustrate the advantages of using geographic structures as variables to better analyse social and economic processes and to initiate a discussion in the user community about using these variables and the potential of this largely untapped capability of the Census databases. In order to achieve these objectives, four examples of geography as a variable are presented. The examples include Aboriginal persons living on-reserve and off-reserve in urban and rural areas in Canada, the unemployment rate of persons living in urban and rural areas in Canada, the gross rent of renter households in urban and rural areas in Canada, and the migration flows of persons 15 to 24 years of age between major urban centres and rural and small town areas (MIZ).Our intent is to encourage the use of geographic structures as census variables in order to provide users with the tools that will enable them to more accurately analyse the social and economic processes that take place in the geographic areas of Canada.
Release date: 2001-03-16 - Articles and reports: 12-001-X19980024353Description:
This paper studies response errors in the Current Population Survey of the U.S. Bureau of the Census and assesses their impact on the unemployment rates published by the Bureau of Labour Statistics. The measurement of these error rates is obtained from reinterview data, using an extension of the Hui and Walter (1980) procedure for the evaluation of diagnostic tests. Unlike prior studies which assumed that the reconciled reinterview yields the true status, the method estimates the error rates in both interviews. Using these estimated error rates, we show that the misclassification in the original survey creates a cyclical effect on the reported estimated unemployment rates. In particular, the degress of underestimation increases when true unemployment is high. As there was insufficient data to distinguish between a model assuming that the misclassification rates are the same throughout the business cycle, and one that allows the error rates to differ in periods of low, moderate and high unemployment, our findings should be regarded as preliminary. Nonetheless, they indicated that the relationship between the models used to assess the accuracy of diagnostic tests, and those measuring misclassification rates of survey data, deserves further study.
Release date: 1999-01-14
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