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All (26,575) (0 to 10 of 26,575 results)

  • 1. Service industries remain resilient amid economic uncertainty in 2025
    Articles and reports: 11-621-M2026009
    Description: This study presents advance estimates for key service industries for 2025, leveraging alternative data sources such as goods and services tax revenue, to assess how selected industries navigated uncertainty amid ongoing trade tensions with the United States, softening labour markets and generally weaker growth in the broader economy.
    Release date: 2026-06-22
  • 2. Investment in building construction April 2026
    Stats in brief: 11-001-X202617319277
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2026-06-22
  • 3. Consumer Price Index May 2026
    Stats in brief: 11-001-X20261733665
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2026-06-22
  • 4. Study: Flash estimates of selected services industries 2025
    Stats in brief: 11-001-X202617338749
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2026-06-22
  • 5. Consumer Price Index Data Visualization Tool
    Data Visualization: 71-607-X2018016
    Description: This interactive dashboard provides access to current and historical Consumer Price Index (CPI) data in a dynamic and customizable format. Key indicators such as the 12-month and 1-month inflation rates and price trends are presented in interactive charts, allowing users to compare and analyze price changes of all the goods and services in the CPI basket over time as well as across geography (national, provincial and territorial levels).

    Other CPI indicators available in this tool include the Bank of Canada’s core measures of inflation, seasonally adjusted inflation rates, and CPI basket weights.

    This web-based application is updated monthly, as soon as the data for the latest reference month is released in The Daily.

    Release date: 2026-06-22
  • 6. Analysis in Brief
    Journals and periodicals: 11-621-M
    Geography: Canada
    Description: The papers published in the Analysis in Brief analytical series shed light on current economic issues. Aimed at a general audience, they cover a wide range of topics including National Accounts, business enterprises, trade, transportation, agriculture, the environment, manufacturing, science and technology, services, etc.
    Release date: 2026-06-22
  • 7. Statistics Canada - Data Visualization Products
    Data Visualization: 71-607-X
    Description: Statistics Canada produces a variety of interactive visualization tools that present data in a graphical form. These tools provide a useful way of interpreting trends behind our data on various social and economic topics.
    Release date: 2026-06-22
  • 8. Chartered banks, total claims and liabilities booked worldwide vis-à-vis non-residents, Bank of Canada
    Table: 10-10-0004-01
    Geography: Canada
    Frequency: Quarterly
    Description:

    This table contains 354 series, with data starting from 1978 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada);  Claims and deposits (2 items: Claims; Deposits);  Type of non-resident (3 items: Total, non-residents; Non-residents of which: banks; Non-residents of which: non-local);  Country of non-resident (59 items: Total, booked; United States; Austria; Belgium; ...).

    Release date: 2026-06-22
  • 9. Chartered banks, assets and liabilities, month-end, Bank of Canada
    Table: 10-10-0109-01
    Geography: Canada
    Frequency: Monthly
    Description: This table contains 110 series, with data starting from 1946 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Assets and liabilities (110 items: Total assets; Total; Canadian dollar assets; Total of foregoing assets; Total; foreign currency assets ...).
    Release date: 2026-06-22
  • 10. Currency outside banks and chartered bank deposits, month-end, Bank of Canada
    Table: 10-10-0112-01
    Geography: Canada
    Frequency: Monthly
    Description: This table contains 75 series, with data starting from 1926 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Currency outside banks and chartered bank deposits (75 items: Total; currency and deposits; Currency outside banks; coin; Currency outside banks; notes; Currency outside banks ...).
    Release date: 2026-06-22
Data (13,287)

Data (13,287) (20 to 30 of 13,287 results)

Analysis (10,787)

Analysis (10,787) (240 to 250 of 10,787 results)

  • 241. Who’s asking? Interviewer effects on unit nonresponse in the Household Finance and Consumption Survey
    Articles and reports: 12-001-X202500200002
    Description: This study examines interviewer effects on household nonresponse in three waves of the Household Finance and Consumption Survey (HFCS) in Austria using a multilevel model. Addressing nonresponse at its source is crucial for maintaining survey data quality and representativeness. Our findings indicate that the variation in response behavior explained by interviewer effects decreased from about one-third in the first wave to 7% in the third wave. Effective interviewers tend to have a university degree, be married, homeowners, and have a larger workload. Additionally, higher mean wages in the household’s municipality negatively affect survey participation. These insights suggest targeted interviewer selection and training strategies to improve response rates.
    Release date: 2025-12-23
  • 242. Estimating monthly indicators for Consumer Confidence using Structural Time Series Models
    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
  • 243. Collinearity diagnostics in generalized linear models fitted with survey data
    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
  • 244. Quantile balancing inverse probability weighting for non-probability samples
    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
  • 245. Quality indicators for representation in administrative data
    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
  • 246. Integrating probability and non-probability samples through deep learning-based mass imputation
    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
  • 247. Generalized regression estimation under misspecified sample design
    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
  • 248. Improved small area inference from data integration using global-local priors
    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
  • 249. Performance of hierarchical Bayes small area estimators using noninformative and informative priors with an application to the Canadian Labor Force Survey
    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
  • 250. Approximate hierarchical Bayes small area estimation using Natural Exponential Family with Quadratic Variance Function and poststratification
    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
Reference (2,029)

Reference (2,029) (40 to 50 of 2,029 results)

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