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All (176)

All (176) (0 to 10 of 176 results)

1. The Canadian Immigrant Labour Market: Recent Trends from 2006 to 2017 Archived
Articles and reports: 71-606-X2018001
Description:
This report uses immigrant data series from the Labour Force Survey to provide a description of immigrants' labour-market outcomes, from 2006 to 2017.
Release date: 2018-12-24
2. Financial and Wealth Accounts on a from-whom-to-whom basis: selected financial instruments Archived
Articles and reports: 13-605-X201800154970
Description:
The 2008 financial crisis demonstrated how quickly economic shocks can spread between sectors and countries, making it apparent that the existing set of macroeconomic statistics contained gaps for identifying such systemic issues. Users therefore require new products from the System of National Accounts that demonstrate the financial linkages between sectors in the economy. The Special Data Dissemination Plus (SDDS+) and G20 Data Gaps initiatives were established by the International Monetary Fund (IMF) to address some of these data gaps, and presented specific recommendations to G20 countries. One of these recommendations asks statistical agencies to begin compiling more detailed data on the interconnectedness of the economy by incorporating a "From-Whom-To-Whom" (FWTW) framework. This FWTW framework includes statistics that make counterparty information explicit, showing how various sectors of the economy are connected by financial interdependencies. In other words, the data presented on this new basis allows users to answer the question "whom is funding whom and with what financial instruments". In this visualisation tool, we present six instruments on a FWTW basis in a fully customizable pivot table.
Release date: 2018-12-21
3. Variance estimation under monotone non-response for a panel survey Archived
Articles and reports: 12-001-X201800254952
Description:
Panel surveys are frequently used to measure the evolution of parameters over time. Panel samples may suffer from different types of unit non-response, which is currently handled by estimating the response probabilities and by reweighting respondents. In this work, we consider estimation and variance estimation under unit non-response for panel surveys. Extending the work by Kim and Kim (2007) for several times, we consider a propensity score adjusted estimator accounting for initial non-response and attrition, and propose a suitable variance estimator. It is then extended to cover most estimators encountered in surveys, including calibrated estimators, complex parameters and longitudinal estimators. The properties of the proposed variance estimator and of a simplified variance estimator are estimated through a simulation study. An illustration of the proposed methods on data from the ELFE survey is also presented.
Release date: 2018-12-20
4. Coordination of spatially balanced samples Archived
Articles and reports: 12-001-X201800254953
Description:
Sample coordination seeks to create a probabilistic dependence between the selection of two or more samples drawn from the same population or from overlapping populations. Positive coordination increases the expected sample overlap, while negative coordination decreases it. There are numerous applications for sample coordination with varying objectives. A spatially balanced sample is a sample that is well-spread in some space. Forcing a spread within the selected samples is a general and very efficient variance reduction technique for the Horvitz-Thompson estimator. The local pivotal method and the spatially correlated Poisson sampling are two general schemes for achieving well-spread samples. We aim to introduce coordination for these sampling methods based on the concept of permanent random numbers. The goal is to coordinate such samples while preserving spatial balance. The proposed methods are motivated by examples from forestry, environmental studies, and official statistics.
Release date: 2018-12-20
5. Using balanced sampling in creel surveys Archived
Articles and reports: 12-001-X201800254954
Description:
These last years, balanced sampling techniques have experienced a recrudescence of interest. They constrain the Horvitz Thompson estimators of the totals of auxiliary variables to be equal, at least approximately, to the corresponding true totals, to avoid the occurrence of bad samples. Several procedures are available to carry out balanced sampling; there is the cube method, see Deville and Tillé (2004), and an alternative, the rejective algorithm introduced by Hájek (1964). After a brief review of these sampling methods, motivated by the planning of an angler survey, we investigate using Monte Carlo simulations, the survey designs produced by these two sampling algorithms.
Release date: 2018-12-20
6. Sample-based estimation of mean electricity consumption curves for small domains Archived
Articles and reports: 12-001-X201800254955
Description:
Many studies conducted by various electric utilities around the world are based on the analysis of mean electricity consumption curves for various subpopulations, particularly geographic in nature. Those mean curves are estimated from samples of thousands of curves measured at very short intervals over long periods. Estimation for small subpopulations, also called small domains, is a very timely topic in sampling theory.
In this article, we will examine this problem based on functional data and we will try to estimate the mean curves for small domains. For this, we propose four methods: functional linear regression; modelling the scores of a principal component analysis by unit-level linear mixed models; and two non-parametric estimators, with one based on regression trees and the other on random forests, adapted to the curves. All these methods have been tested and compared using real electricity consumption data for households in France.
Release date: 2018-12-20
7. Small area estimation for unemployment using latent Markov models Archived
Articles and reports: 12-001-X201800254956
Description:
In Italy, the Labor Force Survey (LFS) is conducted quarterly by the National Statistical Institute (ISTAT) to produce estimates of the labor force status of the population at different geographical levels. In particular, ISTAT provides LFS estimates of employed and unemployed counts for local Labor Market Areas (LMAs). LMAs are 611 sub-regional clusters of municipalities and are unplanned domains for which direct estimates have overly large sampling errors. This implies the need of Small Area Estimation (SAE) methods. In this paper, we develop a new area level SAE method that uses a Latent Markov Model (LMM) as linking model. In LMMs, the characteristic of interest, and its evolution in time, is represented by a latent process that follows a Markov chain, usually of first order. Therefore, areas are allowed to change their latent state across time. The proposed model is applied to quarterly data from the LFS for the period 2004 to 2014 and fitted within a hierarchical Bayesian framework using a data augmentation Gibbs sampler. Estimates are compared with those obtained by the classical Fay-Herriot model, by a time-series area level SAE model, and on the basis of data coming from the 2011 Population Census.
Release date: 2018-12-20
8. How to decompose the non-response variance: A total survey error approach Archived
Articles and reports: 12-001-X201800254957
Description:
When a linear imputation method is used to correct non-response based on certain assumptions, total variance can be assigned to non-responding units. Linear imputation is not as limited as it seems, given that the most common methods – ratio, donor, mean and auxiliary value imputation – are all linear imputation methods. We will discuss the inference framework and the unit-level decomposition of variance due to non-response. Simulation results will also be presented. This decomposition can be used to prioritize non-response follow-up or manual corrections, or simply to guide data analysis.
Release date: 2018-12-20
9. Measuring uncertainty associated with model-based small area estimators Archived
Articles and reports: 12-001-X201800254958
Description:
Domains (or subpopulations) with small sample sizes are called small areas. Traditional direct estimators for small areas do not provide adequate precision because the area-specific sample sizes are small. On the other hand, demand for reliable small area statistics has greatly increased. Model-based indirect estimators of small area means or totals are currently used to address difficulties with direct estimation. These estimators are based on linking models that borrow information across areas to increase the efficiency. In particular, empirical best (EB) estimators under area level and unit level linear regression models with random small area effects have received a lot of attention in the literature. Model mean squared error (MSE) of EB estimators is often used to measure the variability of the estimators. Linearization-based estimators of model MSE as well as jackknife and bootstrap estimators are widely used. On the other hand, National Statistical Agencies are often interested in estimating the design MSE of EB estimators in line with traditional design MSE estimators associated with direct estimators for large areas with adequate sample sizes. Estimators of design MSE of EB estimators can be obtained for area level models but they tend to be unstable when the area sample size is small. Composite MSE estimators are proposed in this paper and they are obtained by taking a weighted sum of the design MSE estimator and the model MSE estimator. Properties of the MSE estimators under the area level model are studied in terms of design bias, relative root mean squared error and coverage rate of confidence intervals. The case of a unit level model is also examined under simple random sampling within each area. Results of a simulation study show that the proposed composite MSE estimators provide a good compromise in estimating the design MSE.
Release date: 2018-12-20
10. Optimizing a mixed allocation Archived
Articles and reports: 12-001-X201800254959
Description:
This article proposes a criterion for calculating the trade-off in so-called “mixed” allocations, which combine two classic allocations in sampling theory. In INSEE (National Institute of Statistics and Economic Studies) business surveys, it is common to use the arithmetic mean of a proportional allocation and a Neyman allocation (corresponding to a trade-off of 0.5). It is possible to obtain a trade-off value resulting in better properties for the estimators. This value belongs to a region that is obtained by solving an optimization program. Different methods for calculating the trade-off will be presented. An application for business surveys is presented, as well as a comparison with other usual trade-off allocations.
Release date: 2018-12-20

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Analysis (176)

Analysis (176) (0 to 10 of 176 results)

1. The Canadian Immigrant Labour Market: Recent Trends from 2006 to 2017 Archived
Articles and reports: 71-606-X2018001
Description:
This report uses immigrant data series from the Labour Force Survey to provide a description of immigrants' labour-market outcomes, from 2006 to 2017.
Release date: 2018-12-24
2. Financial and Wealth Accounts on a from-whom-to-whom basis: selected financial instruments Archived
Articles and reports: 13-605-X201800154970
Description:
The 2008 financial crisis demonstrated how quickly economic shocks can spread between sectors and countries, making it apparent that the existing set of macroeconomic statistics contained gaps for identifying such systemic issues. Users therefore require new products from the System of National Accounts that demonstrate the financial linkages between sectors in the economy. The Special Data Dissemination Plus (SDDS+) and G20 Data Gaps initiatives were established by the International Monetary Fund (IMF) to address some of these data gaps, and presented specific recommendations to G20 countries. One of these recommendations asks statistical agencies to begin compiling more detailed data on the interconnectedness of the economy by incorporating a "From-Whom-To-Whom" (FWTW) framework. This FWTW framework includes statistics that make counterparty information explicit, showing how various sectors of the economy are connected by financial interdependencies. In other words, the data presented on this new basis allows users to answer the question "whom is funding whom and with what financial instruments". In this visualisation tool, we present six instruments on a FWTW basis in a fully customizable pivot table.
Release date: 2018-12-21
3. Variance estimation under monotone non-response for a panel survey Archived
Articles and reports: 12-001-X201800254952
Description:
Panel surveys are frequently used to measure the evolution of parameters over time. Panel samples may suffer from different types of unit non-response, which is currently handled by estimating the response probabilities and by reweighting respondents. In this work, we consider estimation and variance estimation under unit non-response for panel surveys. Extending the work by Kim and Kim (2007) for several times, we consider a propensity score adjusted estimator accounting for initial non-response and attrition, and propose a suitable variance estimator. It is then extended to cover most estimators encountered in surveys, including calibrated estimators, complex parameters and longitudinal estimators. The properties of the proposed variance estimator and of a simplified variance estimator are estimated through a simulation study. An illustration of the proposed methods on data from the ELFE survey is also presented.
Release date: 2018-12-20
4. Coordination of spatially balanced samples Archived
Articles and reports: 12-001-X201800254953
Description:
Sample coordination seeks to create a probabilistic dependence between the selection of two or more samples drawn from the same population or from overlapping populations. Positive coordination increases the expected sample overlap, while negative coordination decreases it. There are numerous applications for sample coordination with varying objectives. A spatially balanced sample is a sample that is well-spread in some space. Forcing a spread within the selected samples is a general and very efficient variance reduction technique for the Horvitz-Thompson estimator. The local pivotal method and the spatially correlated Poisson sampling are two general schemes for achieving well-spread samples. We aim to introduce coordination for these sampling methods based on the concept of permanent random numbers. The goal is to coordinate such samples while preserving spatial balance. The proposed methods are motivated by examples from forestry, environmental studies, and official statistics.
Release date: 2018-12-20
5. Using balanced sampling in creel surveys Archived
Articles and reports: 12-001-X201800254954
Description:
These last years, balanced sampling techniques have experienced a recrudescence of interest. They constrain the Horvitz Thompson estimators of the totals of auxiliary variables to be equal, at least approximately, to the corresponding true totals, to avoid the occurrence of bad samples. Several procedures are available to carry out balanced sampling; there is the cube method, see Deville and Tillé (2004), and an alternative, the rejective algorithm introduced by Hájek (1964). After a brief review of these sampling methods, motivated by the planning of an angler survey, we investigate using Monte Carlo simulations, the survey designs produced by these two sampling algorithms.
Release date: 2018-12-20
6. Sample-based estimation of mean electricity consumption curves for small domains Archived
Articles and reports: 12-001-X201800254955
Description:
Many studies conducted by various electric utilities around the world are based on the analysis of mean electricity consumption curves for various subpopulations, particularly geographic in nature. Those mean curves are estimated from samples of thousands of curves measured at very short intervals over long periods. Estimation for small subpopulations, also called small domains, is a very timely topic in sampling theory.
In this article, we will examine this problem based on functional data and we will try to estimate the mean curves for small domains. For this, we propose four methods: functional linear regression; modelling the scores of a principal component analysis by unit-level linear mixed models; and two non-parametric estimators, with one based on regression trees and the other on random forests, adapted to the curves. All these methods have been tested and compared using real electricity consumption data for households in France.
Release date: 2018-12-20
7. Small area estimation for unemployment using latent Markov models Archived
Articles and reports: 12-001-X201800254956
Description:
In Italy, the Labor Force Survey (LFS) is conducted quarterly by the National Statistical Institute (ISTAT) to produce estimates of the labor force status of the population at different geographical levels. In particular, ISTAT provides LFS estimates of employed and unemployed counts for local Labor Market Areas (LMAs). LMAs are 611 sub-regional clusters of municipalities and are unplanned domains for which direct estimates have overly large sampling errors. This implies the need of Small Area Estimation (SAE) methods. In this paper, we develop a new area level SAE method that uses a Latent Markov Model (LMM) as linking model. In LMMs, the characteristic of interest, and its evolution in time, is represented by a latent process that follows a Markov chain, usually of first order. Therefore, areas are allowed to change their latent state across time. The proposed model is applied to quarterly data from the LFS for the period 2004 to 2014 and fitted within a hierarchical Bayesian framework using a data augmentation Gibbs sampler. Estimates are compared with those obtained by the classical Fay-Herriot model, by a time-series area level SAE model, and on the basis of data coming from the 2011 Population Census.
Release date: 2018-12-20
8. How to decompose the non-response variance: A total survey error approach Archived
Articles and reports: 12-001-X201800254957
Description:
When a linear imputation method is used to correct non-response based on certain assumptions, total variance can be assigned to non-responding units. Linear imputation is not as limited as it seems, given that the most common methods – ratio, donor, mean and auxiliary value imputation – are all linear imputation methods. We will discuss the inference framework and the unit-level decomposition of variance due to non-response. Simulation results will also be presented. This decomposition can be used to prioritize non-response follow-up or manual corrections, or simply to guide data analysis.
Release date: 2018-12-20
9. Measuring uncertainty associated with model-based small area estimators Archived
Articles and reports: 12-001-X201800254958
Description:
Domains (or subpopulations) with small sample sizes are called small areas. Traditional direct estimators for small areas do not provide adequate precision because the area-specific sample sizes are small. On the other hand, demand for reliable small area statistics has greatly increased. Model-based indirect estimators of small area means or totals are currently used to address difficulties with direct estimation. These estimators are based on linking models that borrow information across areas to increase the efficiency. In particular, empirical best (EB) estimators under area level and unit level linear regression models with random small area effects have received a lot of attention in the literature. Model mean squared error (MSE) of EB estimators is often used to measure the variability of the estimators. Linearization-based estimators of model MSE as well as jackknife and bootstrap estimators are widely used. On the other hand, National Statistical Agencies are often interested in estimating the design MSE of EB estimators in line with traditional design MSE estimators associated with direct estimators for large areas with adequate sample sizes. Estimators of design MSE of EB estimators can be obtained for area level models but they tend to be unstable when the area sample size is small. Composite MSE estimators are proposed in this paper and they are obtained by taking a weighted sum of the design MSE estimator and the model MSE estimator. Properties of the MSE estimators under the area level model are studied in terms of design bias, relative root mean squared error and coverage rate of confidence intervals. The case of a unit level model is also examined under simple random sampling within each area. Results of a simulation study show that the proposed composite MSE estimators provide a good compromise in estimating the design MSE.
Release date: 2018-12-20
10. Optimizing a mixed allocation Archived
Articles and reports: 12-001-X201800254959
Description:
This article proposes a criterion for calculating the trade-off in so-called “mixed” allocations, which combine two classic allocations in sampling theory. In INSEE (National Institute of Statistics and Economic Studies) business surveys, it is common to use the arithmetic mean of a proportional allocation and a Neyman allocation (corresponding to a trade-off of 0.5). It is possible to obtain a trade-off value resulting in better properties for the estimators. This value belongs to a region that is obtained by solving an optimization program. Different methods for calculating the trade-off will be presented. An application for business surveys is presented, as well as a comparison with other usual trade-off allocations.
Release date: 2018-12-20

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Date modified:: 2024-06-30