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- Articles and reports: 11-522-X202100100009Description:
Use of auxiliary data to improve the efficiency of estimators of totals and means through model-assisted survey regression estimation has received considerable attention in recent years. Generalized regression (GREG) estimators, based on a working linear regression model, are currently used in establishment surveys at Statistics Canada and several other statistical agencies. GREG estimators use common survey weights for all study variables and calibrate to known population totals of auxiliary variables. Increasingly, many auxiliary variables are available, some of which may be extraneous. This leads to unstable GREG weights when all the available auxiliary variables, including interactions among categorical variables, are used in the working linear regression model. On the other hand, new machine learning methods, such as regression trees and lasso, automatically select significant auxiliary variables and lead to stable nonnegative weights and possible efficiency gains over GREG. In this paper, a simulation study, based on a real business survey sample data set treated as the target population, is conducted to study the relative performance of GREG, regression trees and lasso in terms of efficiency of the estimators.
Key Words: Model assisted inference; calibration estimation; model selection; generalized regression estimator.
Release date: 2021-10-29 - Articles and reports: 11-522-X202100100021Description: Istat has started a new project for the Short Term statistical processes, to satisfy the coming new EU Regulation to release estimates in a shorter time. The assessment and analysis of the current Short Term Survey on Turnover in Services (FAS) survey process, aims at identifying how the best features of the current methods and practices can be exploited to design a more “efficient” process. In particular, the project is expected to release methods that would allow important economies of scale, scope and knowledge to be applied in general to the STS productive context, usually working with a limited number of resources. The analysis of the AS-IS process revealed that the FAS survey incurs substantial E&I costs, especially due to intensive follow-up and interactive editing that is used for every type of detected errors. In this view, we tried to exploit the lessons learned by participating to the High-Level Group for the Modernisation of Official Statistics (HLG-MOS, UNECE) about the Use of Machine Learning in Official Statistics. In this work, we present a first experiment using Random Forest models to: (i) predict which units represent “suspicious” data, (ii) to assess the prediction potential use over new data and (iii) to explore data to identify hidden rules and patterns. In particular, we focus on the use of Random Forest modelling to compare some alternative methods in terms of error prediction efficiency and to address the major aspects for the new design of the E&I scheme.Release date: 2021-10-15
- Articles and reports: 12-001-X201600114542Description:
The restricted maximum likelihood (REML) method is generally used to estimate the variance of the random area effect under the Fay-Herriot model (Fay and Herriot 1979) to obtain the empirical best linear unbiased (EBLUP) estimator of a small area mean. When the REML estimate is zero, the weight of the direct sample estimator is zero and the EBLUP becomes a synthetic estimator. This is not often desirable. As a solution to this problem, Li and Lahiri (2011) and Yoshimori and Lahiri (2014) developed adjusted maximum likelihood (ADM) consistent variance estimators which always yield positive variance estimates. Some of the ADM estimators always yield positive estimates but they have a large bias and this affects the estimation of the mean squared error (MSE) of the EBLUP. We propose to use a MIX variance estimator, defined as a combination of the REML and ADM methods. We show that it is unbiased up to the second order and it always yields a positive variance estimate. Furthermore, we propose an MSE estimator under the MIX method and show via a model-based simulation that in many situations, it performs better than other ‘Taylor linearization’ MSE estimators proposed recently.
Release date: 2016-06-22 - Articles and reports: 11-522-X201700014714Description:
The Labour Market Development Agreements (LMDAs) between Canada and the provinces and territories fund labour market training and support services to Employment Insurance claimants. The objective of this paper is to discuss the improvements over the years in the impact assessment methodology. The paper describes the LMDAs and past evaluation work and discusses the drivers to make better use of large administrative data holdings. It then explains how the new approach made the evaluation less resource-intensive, while results are more relevant to policy development. The paper outlines the lessons learned from a methodological perspective and provides insight into ways for making this type of use of administrative data effective, especially in the context of large programs.
Release date: 2016-03-24 - Articles and reports: 11-522-X201700014718Description:
This study assessed whether starting participation in Employment Assistance Services (EAS) earlier after initiating an Employment Insurance (EI) claim leads to better impacts for unemployed individuals than participating later during the EI benefit period. As in Sianesi (2004) and Hujer and Thomsen (2010), the analysis relied on a stratified propensity score matching approach conditional on the discretized duration of unemployment until the program starts. The results showed that individuals who participated in EAS within the first four weeks after initiating an EI claim had the best impacts on earnings and incidence of employment while also experiencing reduced use of EI starting the second year post-program.
Release date: 2016-03-24 - 6. Estimating the Impact of Active Labour Market Programs using Administrative Data and Matching Methods ArchivedArticles and reports: 11-522-X201700014740Description:
In this paper, we discuss the impacts of Employment Benefit and Support Measures delivered in Canada under the Labour Market Development Agreements. We use linked rich longitudinal administrative data covering all LMDA participants from 2002 to 2005. We Apply propensity score matching as in Blundell et al. (2002), Gerfin and Lechner (2002), and Sianesi (2004), and produced the national incremental impact estimates using difference-in-differences and Kernel Matching estimator (Heckman and Smith, 1999). The findings suggest that, both Employment Assistance Services and employment benefit such as Skills Development and Targeted Wage Subsidies had positive effects on earnings and employment.
Release date: 2016-03-24 - Articles and reports: 11-522-X201700014757Description:
The Unified Brazilian Health System (SUS) was created in 1988 and, with the aim of organizing the health information systems and databases already in use, a unified databank (DataSUS) was created in 1991. DataSUS files are freely available via Internet. Access and visualization of such data is done through a limited number of customized tables and simple diagrams, which do not entirely meet the needs of health managers and other users for a flexible and easy-to-use tool that can tackle different aspects of health which are relevant to their purposes of knowledge-seeking and decision-making. We propose the interactive monthly generation of synthetic epidemiological reports, which are not only easily accessible but also easy to interpret and understand. Emphasis is put on data visualization through more informative diagrams and maps.
Release date: 2016-03-24 - Articles and reports: 82-003-X201401014098Geography: Province or territoryDescription:
This study compares registry and non-registry approaches to linking 2006 Census of Population data for Manitoba and Ontario to Hospital data from the Discharge Abstract Database.
Release date: 2014-10-15 - 9. A nonparametric method to generate synthetic populations to adjust for complex sampling design features ArchivedArticles and reports: 12-001-X201400114003Description:
Outside of the survey sampling literature, samples are often assumed to be generated by simple random sampling process that produces independent and identically distributed (IID) samples. Many statistical methods are developed largely in this IID world. Application of these methods to data from complex sample surveys without making allowance for the survey design features can lead to erroneous inferences. Hence, much time and effort have been devoted to develop the statistical methods to analyze complex survey data and account for the sample design. This issue is particularly important when generating synthetic populations using finite population Bayesian inference, as is often done in missing data or disclosure risk settings, or when combining data from multiple surveys. By extending previous work in finite population Bayesian bootstrap literature, we propose a method to generate synthetic populations from a posterior predictive distribution in a fashion inverts the complex sampling design features and generates simple random samples from a superpopulation point of view, making adjustment on the complex data so that they can be analyzed as simple random samples. We consider a simulation study with a stratified, clustered unequal-probability of selection sample design, and use the proposed nonparametric method to generate synthetic populations for the 2006 National Health Interview Survey (NHIS), and the Medical Expenditure Panel Survey (MEPS), which are stratified, clustered unequal-probability of selection sample designs.
Release date: 2014-06-27 - Articles and reports: 12-002-X201200111642Description:
It is generally recommended that weighted estimation approaches be used when analyzing data from a long-form census microdata file. Since such data files are now available in the RDC's, there is a need to provide researchers there with more information about doing weighted estimation with these files. The purpose of this paper is to provide some of this information - in particular, how the weight variables were derived for the census microdata files and what weight should be used for different units of analysis. For the 1996, 2001 and 2006 censuses the same weight variable is appropriate regardless of whether people, families or households are being studied. For the 1991 census, recommendations are more complex: a different weight variable is required for households than for people and families, and additional restrictions apply to obtain the correct weight value for families.
Release date: 2012-10-25
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- Articles and reports: 11-522-X202100100009Description:
Use of auxiliary data to improve the efficiency of estimators of totals and means through model-assisted survey regression estimation has received considerable attention in recent years. Generalized regression (GREG) estimators, based on a working linear regression model, are currently used in establishment surveys at Statistics Canada and several other statistical agencies. GREG estimators use common survey weights for all study variables and calibrate to known population totals of auxiliary variables. Increasingly, many auxiliary variables are available, some of which may be extraneous. This leads to unstable GREG weights when all the available auxiliary variables, including interactions among categorical variables, are used in the working linear regression model. On the other hand, new machine learning methods, such as regression trees and lasso, automatically select significant auxiliary variables and lead to stable nonnegative weights and possible efficiency gains over GREG. In this paper, a simulation study, based on a real business survey sample data set treated as the target population, is conducted to study the relative performance of GREG, regression trees and lasso in terms of efficiency of the estimators.
Key Words: Model assisted inference; calibration estimation; model selection; generalized regression estimator.
Release date: 2021-10-29 - Articles and reports: 11-522-X202100100021Description: Istat has started a new project for the Short Term statistical processes, to satisfy the coming new EU Regulation to release estimates in a shorter time. The assessment and analysis of the current Short Term Survey on Turnover in Services (FAS) survey process, aims at identifying how the best features of the current methods and practices can be exploited to design a more “efficient” process. In particular, the project is expected to release methods that would allow important economies of scale, scope and knowledge to be applied in general to the STS productive context, usually working with a limited number of resources. The analysis of the AS-IS process revealed that the FAS survey incurs substantial E&I costs, especially due to intensive follow-up and interactive editing that is used for every type of detected errors. In this view, we tried to exploit the lessons learned by participating to the High-Level Group for the Modernisation of Official Statistics (HLG-MOS, UNECE) about the Use of Machine Learning in Official Statistics. In this work, we present a first experiment using Random Forest models to: (i) predict which units represent “suspicious” data, (ii) to assess the prediction potential use over new data and (iii) to explore data to identify hidden rules and patterns. In particular, we focus on the use of Random Forest modelling to compare some alternative methods in terms of error prediction efficiency and to address the major aspects for the new design of the E&I scheme.Release date: 2021-10-15
- Articles and reports: 12-001-X201600114542Description:
The restricted maximum likelihood (REML) method is generally used to estimate the variance of the random area effect under the Fay-Herriot model (Fay and Herriot 1979) to obtain the empirical best linear unbiased (EBLUP) estimator of a small area mean. When the REML estimate is zero, the weight of the direct sample estimator is zero and the EBLUP becomes a synthetic estimator. This is not often desirable. As a solution to this problem, Li and Lahiri (2011) and Yoshimori and Lahiri (2014) developed adjusted maximum likelihood (ADM) consistent variance estimators which always yield positive variance estimates. Some of the ADM estimators always yield positive estimates but they have a large bias and this affects the estimation of the mean squared error (MSE) of the EBLUP. We propose to use a MIX variance estimator, defined as a combination of the REML and ADM methods. We show that it is unbiased up to the second order and it always yields a positive variance estimate. Furthermore, we propose an MSE estimator under the MIX method and show via a model-based simulation that in many situations, it performs better than other ‘Taylor linearization’ MSE estimators proposed recently.
Release date: 2016-06-22 - Articles and reports: 11-522-X201700014714Description:
The Labour Market Development Agreements (LMDAs) between Canada and the provinces and territories fund labour market training and support services to Employment Insurance claimants. The objective of this paper is to discuss the improvements over the years in the impact assessment methodology. The paper describes the LMDAs and past evaluation work and discusses the drivers to make better use of large administrative data holdings. It then explains how the new approach made the evaluation less resource-intensive, while results are more relevant to policy development. The paper outlines the lessons learned from a methodological perspective and provides insight into ways for making this type of use of administrative data effective, especially in the context of large programs.
Release date: 2016-03-24 - Articles and reports: 11-522-X201700014718Description:
This study assessed whether starting participation in Employment Assistance Services (EAS) earlier after initiating an Employment Insurance (EI) claim leads to better impacts for unemployed individuals than participating later during the EI benefit period. As in Sianesi (2004) and Hujer and Thomsen (2010), the analysis relied on a stratified propensity score matching approach conditional on the discretized duration of unemployment until the program starts. The results showed that individuals who participated in EAS within the first four weeks after initiating an EI claim had the best impacts on earnings and incidence of employment while also experiencing reduced use of EI starting the second year post-program.
Release date: 2016-03-24 - 6. Estimating the Impact of Active Labour Market Programs using Administrative Data and Matching Methods ArchivedArticles and reports: 11-522-X201700014740Description:
In this paper, we discuss the impacts of Employment Benefit and Support Measures delivered in Canada under the Labour Market Development Agreements. We use linked rich longitudinal administrative data covering all LMDA participants from 2002 to 2005. We Apply propensity score matching as in Blundell et al. (2002), Gerfin and Lechner (2002), and Sianesi (2004), and produced the national incremental impact estimates using difference-in-differences and Kernel Matching estimator (Heckman and Smith, 1999). The findings suggest that, both Employment Assistance Services and employment benefit such as Skills Development and Targeted Wage Subsidies had positive effects on earnings and employment.
Release date: 2016-03-24 - Articles and reports: 11-522-X201700014757Description:
The Unified Brazilian Health System (SUS) was created in 1988 and, with the aim of organizing the health information systems and databases already in use, a unified databank (DataSUS) was created in 1991. DataSUS files are freely available via Internet. Access and visualization of such data is done through a limited number of customized tables and simple diagrams, which do not entirely meet the needs of health managers and other users for a flexible and easy-to-use tool that can tackle different aspects of health which are relevant to their purposes of knowledge-seeking and decision-making. We propose the interactive monthly generation of synthetic epidemiological reports, which are not only easily accessible but also easy to interpret and understand. Emphasis is put on data visualization through more informative diagrams and maps.
Release date: 2016-03-24 - Articles and reports: 82-003-X201401014098Geography: Province or territoryDescription:
This study compares registry and non-registry approaches to linking 2006 Census of Population data for Manitoba and Ontario to Hospital data from the Discharge Abstract Database.
Release date: 2014-10-15 - 9. A nonparametric method to generate synthetic populations to adjust for complex sampling design features ArchivedArticles and reports: 12-001-X201400114003Description:
Outside of the survey sampling literature, samples are often assumed to be generated by simple random sampling process that produces independent and identically distributed (IID) samples. Many statistical methods are developed largely in this IID world. Application of these methods to data from complex sample surveys without making allowance for the survey design features can lead to erroneous inferences. Hence, much time and effort have been devoted to develop the statistical methods to analyze complex survey data and account for the sample design. This issue is particularly important when generating synthetic populations using finite population Bayesian inference, as is often done in missing data or disclosure risk settings, or when combining data from multiple surveys. By extending previous work in finite population Bayesian bootstrap literature, we propose a method to generate synthetic populations from a posterior predictive distribution in a fashion inverts the complex sampling design features and generates simple random samples from a superpopulation point of view, making adjustment on the complex data so that they can be analyzed as simple random samples. We consider a simulation study with a stratified, clustered unequal-probability of selection sample design, and use the proposed nonparametric method to generate synthetic populations for the 2006 National Health Interview Survey (NHIS), and the Medical Expenditure Panel Survey (MEPS), which are stratified, clustered unequal-probability of selection sample designs.
Release date: 2014-06-27 - Articles and reports: 12-002-X201200111642Description:
It is generally recommended that weighted estimation approaches be used when analyzing data from a long-form census microdata file. Since such data files are now available in the RDC's, there is a need to provide researchers there with more information about doing weighted estimation with these files. The purpose of this paper is to provide some of this information - in particular, how the weight variables were derived for the census microdata files and what weight should be used for different units of analysis. For the 1996, 2001 and 2006 censuses the same weight variable is appropriate regardless of whether people, families or households are being studied. For the 1991 census, recommendations are more complex: a different weight variable is required for households than for people and families, and additional restrictions apply to obtain the correct weight value for families.
Release date: 2012-10-25
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