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  • 231. Estimating monthly indicators for Consumer Confidence using Structural Time Series Models - Open in new window/tab
    Articles and reports: 12-001-X202500200003
    Description: In this paper a model-based inference procedure based on a multivariate structural time series model is developed for the production of monthly figures about consumer confidence. The input for the model are five series of direct estimates for the indices that measure consumer confidence, which are derived from the Dutch Consumer Survey. The model improves the accuracy of the direct estimates, since it provides a better separation of measurement errors and sampling errors from estimated target parameters. The standard errors for the month-to-month changes are clearly smaller under the time series model. A second problem addressed in this paper is related to the transition to a new survey process in 2017. Structural time series models in combination with a parallel run are applied to estimate discontinuities induced by the redesign. An algorithm designed for the consumer confidence variables is developed to construct uninterrupted input series for the aforementioned structural time series model. This inference method facilitated a smooth transition to a new survey design and resulted in uninterrupted series about consumer confidence that date back to 1986. The method is implemented for the production of official monthly figures on consumer confidence in the Netherlands.
    Release date: 2025-12-23
  • 232. Collinearity diagnostics in generalized linear models fitted with survey data - Open in new window/tab
    Articles and reports: 12-001-X202500200004
    Description: The class of generalized linear models (GLM) is a flexible generalization of ordinary least squares regression that allows the linear model to be related to the response variable via a link function and assumes the magnitude of the variance of each measurement to be a function of its predicted value. Multicollinearity in GLMs can inflate variances of the estimated coefficients and cause poor prediction in certain regions of the regression space. It may also cause a nonsignificant Wald statistic even when the predictors are highly predictive in a model of the family of GLMs. Little previous research has closely investigated the diagnostics of multicollinearity in GLMs, especially when complex survey data are used. In this paper, we develop variance inflation factors (VIFs) that measure the amount that the variance of a parameter estimator is increased due to multicollinearity in GLMs. We also extend VIFs and condition indexes to apply to complex survey data, accounting for design features, e.g. weights, clusters, and strata. Illustrations of these methods are given using data from a household survey of health and nutrition.
    Release date: 2025-12-23
  • 233. Quantile balancing inverse probability weighting for non-probability samples - Open in new window/tab
    Articles and reports: 12-001-X202500200005
    Description: The use of non-probability data sources for statistical purposes and for official statistics has become increasingly popular in recent years. However, statistical inference based on non-probability samples is made more difficult by nature of their biasedness and lack of representativity. In this paper we propose quantile balancing inverse probability weighting estimator (QBIPW) for non-probability samples. We apply the idea of Harms and Duchesne (2006) allowing the use of quantile information in the estimation process to reproduce known totals and the distribution of auxiliary variables. We discuss the estimation of the QBIPW probabilities and its variance. Our simulation study has demonstrated that the proposed estimators are robust against model mis-specification and, as a result, help to reduce bias and mean squared error. Finally, we applied the proposed methods to estimate the share of job vacancies aimed at Ukrainian workers in Poland using an integrated set of administrative and survey data about job vacancies.
    Release date: 2025-12-23
  • 234. Quality indicators for representation in administrative data - Open in new window/tab
    Articles and reports: 12-001-X202500200006
    Description: National Statistical Institutes (NSIs) are directing resources into advancing the use of administrative data in official statistics. Administrative data, however, are not developed for the purpose of producing statistics rather as a result of an event or transaction relating to administrative procedures of organizations, public administrations and government agencies. Therefore, it is essential to check the quality of the administrative data with respect to sources of error, particularly representativeness to the target population. In this paper, we utilize the strength of probability-based reference samples or censuses that can be used to detect the lack of representativeness in administrative data and introduce quality indicators based on distance metrics and representativity indicators (R-indicators). We demonstrate their application with a simulation study and discuss a real application applied on a UK Office for National Statistics (ONS) administrative dataset.
    Release date: 2025-12-23
  • 235. Integrating probability and non-probability samples through deep learning-based mass imputation - Open in new window/tab
    Articles and reports: 12-001-X202500200007
    Description: Although probability samples have been regarded as the gold standard to collect information for population-based study, non-probability samples have been used frequently in practice due to low cost, convenience, and the lack of the sampling frame for the survey. Naïve estimates based on non-probability samples without any adjustments may be misleading due to selection bias. Recently, a valid data integration approach that includes mass imputation, propensity score weighting, and calibration has been used to improve the representativeness of non-probability samples. The effectiveness of the mass imputation approach depends on the underlying model assumptions. In this paper, we propose using deep learning for the mass imputation in the combining of probability and non-probability samples and compare it with several modern machine learning-based mass imputation approaches, including generalized additive modeling, regression tree, random forest, and XG-boosting. In the simulation study, deep learning-based approaches have been shown to be more robust and effective than other mass imputation approaches against the failure of underlying model assumptions under non-linearity scenarios.
    Release date: 2025-12-23
  • 236. Generalized regression estimation under misspecified sample design - Open in new window/tab
    Articles and reports: 12-001-X202500200008
    Description: Classical design-based survey estimation relies on a properly specified sampling design for valid inference. We consider the properties of regression estimation under a misspecified sample design, in which the nominal and true inclusion probabilities do not necessarily match. This general misspecified sample design setting encompasses many challenges in the modern survey environment. Under this setting, an asymptotic analysis of the regression estimator, an expression of the bias, and an expression of the variance are presented. Further, a consistent variance estimator is derived and an expression which estimates the bias in-part or in-whole is discussed. This later expression may be used as an indicator of the presence of bias due to misspecification by a practitioner. A simulation study is conducted to support the presented theory.
    Release date: 2025-12-23
  • 237. Improved small area inference from data integration using global-local priors - Open in new window/tab
    Articles and reports: 12-001-X202500200009
    Description: We present and apply methodology to improve inference for small area parameters by using data from several sources. This work extends Cahoy and Sedransk (2023) who showed how to integrate summary statistics from several sources. Our methodology uses hierarchical global-local prior distributions to make inferences for the proportion of individuals in Florida’s counties who do not have health insurance. Results from an extensive simulation study show that this methodology will provide improved inference by using several data sources. Among the five model variants evaluated the ones using horseshoe priors for all variances have better performance than the ones using lasso priors for the local variances.
    Release date: 2025-12-23
  • 238. Performance of hierarchical Bayes small area estimators using noninformative and informative priors with an application to the Canadian Labor Force Survey - Open in new window/tab
    Articles and reports: 12-001-X202500200010
    Description: In this paper, we study the performance of hierarchical Bayes (HB) small area estimators using noninformative and informative priors. We apply the Bayesian models of You and Chapman (2006) and You (2021) to the Canadian Labor Force Survey (LFS) data and evaluate the impact of the priors on the HB estimators. A Bayesian model comparison and simulation study are also conducted. Our results indicate that a correct informative prior can lead to very good results, and noninformative priors can also perform very well. Incorrect informative priors can lead to poor results in terms of large bias and large coefficient of variation (CV). Noninformative priors are recommended in practice for HB small area estimation unless correctly specified informative priors are available. Informative priors are particularly useful when the number of small areas is relatively small.
    Release date: 2025-12-23
  • 239. Approximate hierarchical Bayes small area estimation using Natural Exponential Family with Quadratic Variance Function and poststratification - Open in new window/tab
    Articles and reports: 12-001-X202500200011
    Description: We propose an approximate hierarchical Bayes approach that uses the Natural Exponential Family with Quadratic Variance Function (NEF-QVF) in combining information from multiple sources to improve traditional survey estimates of finite population means for small areas. Unlike other Bayesian approaches in finite population sampling, we do not assume a model for all units of the finite population and do not require linking sampled units to the finite population frame. We assume a model only for the finite population units in which the outcome variable is observed; because, for these units, the assumed model can be checked using existing statistical tools. We do not posit an elaborate model on the true means for unobserved units. Instead, we assume that population means of cells with the same combination of factor levels are identical across small areas, and that the population mean for a cell is identical to the mean of the observed units in that cell. We apply our proposed methodology to a real-life survey, linking information from multiple disparate data sources. We also provide practical ways of model selection that can be applied to a wider class of models under similar setting but for a diverse range of scientific problems.
    Release date: 2025-12-23
  • 240. Modified observed best prediction strategies for small area estimation with unit-level data - Open in new window/tab
    Articles and reports: 12-001-X202500200012
    Description: The observed best prediction (OBP) under a nested-error regression (NER) model was previously proposed using a design-based mean squared prediction error (MSPE) as a tool to derive the best predictive estimator (BPE). A recent study showed the OBP under the NER model may suffer from numerical instability when computing the BPE. We propose several modifications of the OBP under the NER model, including ones using a model-based MSPE to derive the BPE, to improve the numerical stability and predictive performance. We compare the performance of the modified OBP strategies with the existing methods in a simulation study. A real-data example is discussed.
    Release date: 2025-12-23
Reference (2,029)

Reference (2,029) (20 to 30 of 2,029 results)

  • 21. Canadian Export Classification - Open in new window/tab
    Classification: 65-209-X
    Description: The Canadian Export Classification is a structured, hierarchical classification system based on the Harmonized Description and Coding System. The HS nomenclature is divided into 21 Sections, which in general, group goods produced in the same sector of the economy.
    Release date: 2025-12-04
  • 22. The Guide to the Post Index - Open in new window/tab
    Surveys and statistical programs – Documentation: 62F0072G
    Description: The Post Indexes are a collection of spatial price indexes for Government of Canada staff posted abroad that compare the cost of purchasing a fixed basket of goods and services between the post locations and Ottawa. These indexes are constructed as part of Foreign Service Directive 55.
    Release date: 2025-11-25
  • 23. Public Sector Universe - Open in new window/tab
    Classification: 68-516-X
    Description: This product presents the Public Sector Universe, defined by Statistics Canada as a list of institutional units that are included in the economic measurement of governments in Canada. An institutional unit is defined as an economic entity that is capable, in its own right, of owning assets, incurring liabilities, and engaging in economic activities and in transactions with other entities. It includes the sectors of education, health, general government and government business enterprise sectors for all levels of government in Canada, annually, since 2008.
    Release date: 2025-11-21
  • 24. Census of Environment: Spatial information products - Open in new window/tab
    Geographic files and documentation: 16-510-X
    Description: Spatial information products provide users with data for visualization, reference, mapping and spatial analysis using geographical information systems (GIS). Available files include spatial environmental data as well as documentation and metadata. This information is released as part of a suite of products associated with the Census of Environment (CoE). The CoE organizes data about Canada’s natural environment using the System of Environmental-Economic Accounting – Ecosystem Accounting international statistical standard, which takes a spatial approach to accounting for Canada’s ecosystems and natural capital.
    Release date: 2025-11-17
  • 25. Urban greenness: Geospatial files - Open in new window/tab
    Geographic files and documentation: 16-510-X2024003
    Description: This product contains gridded datasets of annual estimates of average normalized difference vegetation index (NDVI) and green/grey classification.

    "Urban greenness" is a measure that assesses the condition and health of an urban landscape. Vegetation contributes to more livable, beautiful communities by helping to clean the air, moderate the local climate, control water flow and provide habitats for wildlife.

    Annual average normalized difference vegetation index datasets and annual green/grey classifications are available for the years 2000 to 2025 and cover Canada south of 60°N.
    Release date: 2025-11-17
  • 26. Urban greenness: Data product specifications, 2025 - Open in new window/tab
    Geographic files and documentation: 16-510-X2025006
    Description: This product contains specifications intended for users of the urban greenness geospatial files. This document provides important technical information for users and links to methodology.
    Release date: 2025-11-17
  • 27. Survey on the Official Language Minority Population: User guide, 2022 (2025 revisions) - Open in new window/tab
    Surveys and statistical programs – Documentation: 89-657-X2025002
    Description: The Survey on the Official Language Minority Population (SOLMP) user guide contains a description of the survey, along with survey concepts and definitions and an overview of the content development. The target and survey populations, the sample design and sample size are described in the Methodology section, while the Data Collection module provides the collection period and instrument, modes of collection, collection and communications strategies and response rates.

    Updates to the guide include descriptions of the survey data processing, survey error and weighting, and guidelines for tabulations and analysis. Appendices will provide a listing of questions and variables which changed between the current and previous occasions of the survey, as well as various primers on the survey methodology.
    Release date: 2025-11-14
  • 28. Statistics Canada Fees Report - Open in new window/tab
    Notices and consultations: 89-26-0001
    Description: The Fees Report must be tabled in parliament annually, as per the Service Fees Act, which came into force in June 2017. The Service Fees Act introduces a modern legislative framework that enables cost-effective delivery of services and, through enhanced reporting to Parliament, improved transparency and oversight.
    Release date: 2025-11-07
  • 29. Departmental Results Report - Open in new window/tab
    Notices and consultations: 11-628-X
    Description: Departmental Results Reports (DRRs) are part of the Estimates family of documents. Estimates documents support appropriation acts, which specify the amounts and broad purposes for which funds can be spent by the government. The Estimates document family has three parts.

    Part I (Government Expenditure Plan) provides an overview of federal spending.

    Part II (Main Estimates) lists the financial resources required by individual departments, agencies and Crown corporations for the upcoming fiscal year.

    Part III (Departmental Expenditure Plans) consists of two documents. Departmental Plans (DPs) are expenditure plans for each appropriated department and agency (excluding Crown corporations).

    Release date: 2025-11-07
  • 30. Longitudinal Administrative Data Dictionary - Open in new window/tab
    Surveys and statistical programs – Documentation: 12-585-X
    Description: This product is the dictionary for the Longitudinal Administrative Databank (LAD). The dictionary contains a complete description for each of the income and demographic variables in the LAD, including name, acronym, definition, source, historical availability and historical continuity.

    The following is a partial list of LAD variables: age, sex, marital status, family type, number and age of children, total income, wages and salaries, self-employment, Employment Insurance, Old Age Security, Canada and Quebec Pension Plans, social assistance, investment income, rental income, alimony, registered retirement savings plan (RRSP) income and contributions, low-income status, full-time education deduction, provincial refundable tax credits, goods and service tax (GST) credits, Canada Child Tax Benefits, selected immigration variables, Tax Free Savings (TFSA) information and Canadian Controlled Private Corporations (CCPC) information.

    Release date: 2025-10-31
Other (1)

Other (1) ((1 result))

  • 1. Data Visualization: Best Practices - Open in new window/tab
    89-26-0005
    Description: This document provides best practices in data visualization for basic charts. A data visualization product can be created with very different goals and for different audiences with a wide range of expertise. General information applicable to any data visualization product is provided, as well as detailed information for data visualization by chart. This report covers 5 different categories of charts, and presents graphs from the following 5 general categories: pie charts, bar charts, point charts, line charts and maps.
    Release date: 2023-02-24

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