Analysis

COVID-19 A data perspective

COVID-19: A data perspective: Explore key economic trends and social challenges that arise as the COVID-19 situation evolves.

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All (14) (0 to 10 of 14 results)

  • Articles and reports: 62F0014M2020017
    Description:

    This article presents the data sources and methodology for Lending Services Price Index (LSPI), to accompany the first release of this price index. The LSPI measures the monthly price change for existing lending services in Canada and its primary purpose is to provide supplemental information to help inform the deflation of the Banking industry's output.

    Release date: 2020-10-15

  • Articles and reports: 62F0014M2020010
    Description:

    Using various sources of expenditure data, Statistics Canada, in partnership with the Bank of Canada, has estimated Consumer Price Index (CPI) basket expenditures that reflect shifts in consumption patterns during the COVID-19 pandemic. The data cover the majority of CPI goods and services, and provide a snapshot estimate of expenditure weights for March, April and May, 2020. These estimates, updated to reflect recent expenditures during the pandemic and concurrent period of physical distancing, can provide insight into the impact of COVID-19 on the headline CPI.

    Release date: 2020-07-13

  • Articles and reports: 62F0014M2019005
    Description:

    This document describes the updated methodology for Investment Banking Services Price Index (IBSPI).

    Release date: 2019-07-08

  • Articles and reports: 62F0014M2017002
    Description:

    This document offers information on changes to the Mortgage Interest Cost Index (MICI), which is one of the Consumer Price Index (CPI) components. It describes the new approach for estimating MICI price movements.

    Release date: 2017-11-17

  • Articles and reports: 12-001-X201300211871
    Description:

    Regression models are routinely used in the analysis of survey data, where one common issue of interest is to identify influential factors that are associated with certain behavioral, social, or economic indices within a target population. When data are collected through complex surveys, the properties of classical variable selection approaches developed in i.i.d. non-survey settings need to be re-examined. In this paper, we derive a pseudo-likelihood-based BIC criterion for variable selection in the analysis of survey data and suggest a sample-based penalized likelihood approach for its implementation. The sampling weights are appropriately assigned to correct the biased selection result caused by the distortion between the sample and the target population. Under a joint randomization framework, we establish the consistency of the proposed selection procedure. The finite-sample performance of the approach is assessed through analysis and computer simulations based on data from the hypertension component of the 2009 Survey on Living with Chronic Diseases in Canada.

    Release date: 2014-01-15

  • Stats in brief: 99-014-X201100311861
    Description:

    These two short articles provide complementary analysis to the 2011 National Household Survey (NHS) analytical document on the composition of income in Canada. They focus on specific topics of interest. The first NHS in Brief is entitled Education and occupations of high-income Canadians, and the second, Persons living in low-income neighbourhoods.

    Release date: 2013-09-11

  • Articles and reports: 12-001-X201200211753
    Description:

    Nonresponse in longitudinal studies often occurs in a nonmonotone pattern. In the Survey of Industrial Research and Development (SIRD), it is reasonable to assume that the nonresponse mechanism is past-value-dependent in the sense that the response propensity of a study variable at time point t depends on response status and observed or missing values of the same variable at time points prior to t. Since this nonresponse is nonignorable, the parametric likelihood approach is sensitive to the specification of parametric models on both the joint distribution of variables at different time points and the nonresponse mechanism. The nonmonotone nonresponse also limits the application of inverse propensity weighting methods. By discarding all observed data from a subject after its first missing value, one can create a dataset with a monotone ignorable nonresponse and then apply established methods for ignorable nonresponse. However, discarding observed data is not desirable and it may result in inefficient estimators when many observed data are discarded. We propose to impute nonrespondents through regression under imputation models carefully created under the past-value-dependent nonresponse mechanism. This method does not require any parametric model on the joint distribution of the variables across time points or the nonresponse mechanism. Performance of the estimated means based on the proposed imputation method is investigated through some simulation studies and empirical analysis of the SIRD data.

    Release date: 2012-12-19

  • Articles and reports: 75F0002M2011003
    Description:

    Existing studies on Canadian poverty (or low-income) dynamics are mainly based on 1990s data from the Longitudinal Administrative Database or the Survey of Labour and Income Dynamics (SLID). These studies typically rely on a single low-income threshold.

    Our work extends the existing studies beyond 1999 by using SLID data from Panel 3 (1999 to 2004) and Panel 4 (2002 to 2007). We consider all three low-income thresholds established by federal departments: Statistics Canada's low-income cut-off (LICO) and low-income measure (LIM), and the market basket measure (MBM) of Human Resources and Skills Development Canada.

    Release date: 2011-10-21

  • Articles and reports: 82-003-X201100211426
    Geography: Canada
    Description:

    This study examines the association between neighbourhood income and the diagnosis of female breast cancer. Population data from the Canadian Cancer Registry were used to calculate national age-specific and age-standardized rates of breast cancer from 1992 through 2004 by neighbourhood income quintile and region.

    Release date: 2011-04-20

  • Articles and reports: 12-001-X200900211038
    Description:

    We examine overcoming the overestimation in using generalized weight share method (GWSM) caused by link nonresponse in indirect sampling. A few adjustment methods incorporating link nonresponse in using GWSM have been constructed for situations both with and without the availability of auxiliary variables. A simulation study on a longitudinal survey is presented using some of the adjustment methods we recommend. The simulation results show that these adjusted GWSMs perform well in reducing both estimation bias and variance. The advancement in bias reduction is significant.

    Release date: 2009-12-23
Stats in brief (1)

Stats in brief (1) ((1 result))

  • Stats in brief: 99-014-X201100311861
    Description:

    These two short articles provide complementary analysis to the 2011 National Household Survey (NHS) analytical document on the composition of income in Canada. They focus on specific topics of interest. The first NHS in Brief is entitled Education and occupations of high-income Canadians, and the second, Persons living in low-income neighbourhoods.

    Release date: 2013-09-11
Articles and reports (13)

Articles and reports (13) (0 to 10 of 13 results)

  • Articles and reports: 62F0014M2020017
    Description:

    This article presents the data sources and methodology for Lending Services Price Index (LSPI), to accompany the first release of this price index. The LSPI measures the monthly price change for existing lending services in Canada and its primary purpose is to provide supplemental information to help inform the deflation of the Banking industry's output.

    Release date: 2020-10-15

  • Articles and reports: 62F0014M2020010
    Description:

    Using various sources of expenditure data, Statistics Canada, in partnership with the Bank of Canada, has estimated Consumer Price Index (CPI) basket expenditures that reflect shifts in consumption patterns during the COVID-19 pandemic. The data cover the majority of CPI goods and services, and provide a snapshot estimate of expenditure weights for March, April and May, 2020. These estimates, updated to reflect recent expenditures during the pandemic and concurrent period of physical distancing, can provide insight into the impact of COVID-19 on the headline CPI.

    Release date: 2020-07-13

  • Articles and reports: 62F0014M2019005
    Description:

    This document describes the updated methodology for Investment Banking Services Price Index (IBSPI).

    Release date: 2019-07-08

  • Articles and reports: 62F0014M2017002
    Description:

    This document offers information on changes to the Mortgage Interest Cost Index (MICI), which is one of the Consumer Price Index (CPI) components. It describes the new approach for estimating MICI price movements.

    Release date: 2017-11-17

  • Articles and reports: 12-001-X201300211871
    Description:

    Regression models are routinely used in the analysis of survey data, where one common issue of interest is to identify influential factors that are associated with certain behavioral, social, or economic indices within a target population. When data are collected through complex surveys, the properties of classical variable selection approaches developed in i.i.d. non-survey settings need to be re-examined. In this paper, we derive a pseudo-likelihood-based BIC criterion for variable selection in the analysis of survey data and suggest a sample-based penalized likelihood approach for its implementation. The sampling weights are appropriately assigned to correct the biased selection result caused by the distortion between the sample and the target population. Under a joint randomization framework, we establish the consistency of the proposed selection procedure. The finite-sample performance of the approach is assessed through analysis and computer simulations based on data from the hypertension component of the 2009 Survey on Living with Chronic Diseases in Canada.

    Release date: 2014-01-15

  • Articles and reports: 12-001-X201200211753
    Description:

    Nonresponse in longitudinal studies often occurs in a nonmonotone pattern. In the Survey of Industrial Research and Development (SIRD), it is reasonable to assume that the nonresponse mechanism is past-value-dependent in the sense that the response propensity of a study variable at time point t depends on response status and observed or missing values of the same variable at time points prior to t. Since this nonresponse is nonignorable, the parametric likelihood approach is sensitive to the specification of parametric models on both the joint distribution of variables at different time points and the nonresponse mechanism. The nonmonotone nonresponse also limits the application of inverse propensity weighting methods. By discarding all observed data from a subject after its first missing value, one can create a dataset with a monotone ignorable nonresponse and then apply established methods for ignorable nonresponse. However, discarding observed data is not desirable and it may result in inefficient estimators when many observed data are discarded. We propose to impute nonrespondents through regression under imputation models carefully created under the past-value-dependent nonresponse mechanism. This method does not require any parametric model on the joint distribution of the variables across time points or the nonresponse mechanism. Performance of the estimated means based on the proposed imputation method is investigated through some simulation studies and empirical analysis of the SIRD data.

    Release date: 2012-12-19

  • Articles and reports: 75F0002M2011003
    Description:

    Existing studies on Canadian poverty (or low-income) dynamics are mainly based on 1990s data from the Longitudinal Administrative Database or the Survey of Labour and Income Dynamics (SLID). These studies typically rely on a single low-income threshold.

    Our work extends the existing studies beyond 1999 by using SLID data from Panel 3 (1999 to 2004) and Panel 4 (2002 to 2007). We consider all three low-income thresholds established by federal departments: Statistics Canada's low-income cut-off (LICO) and low-income measure (LIM), and the market basket measure (MBM) of Human Resources and Skills Development Canada.

    Release date: 2011-10-21

  • Articles and reports: 82-003-X201100211426
    Geography: Canada
    Description:

    This study examines the association between neighbourhood income and the diagnosis of female breast cancer. Population data from the Canadian Cancer Registry were used to calculate national age-specific and age-standardized rates of breast cancer from 1992 through 2004 by neighbourhood income quintile and region.

    Release date: 2011-04-20

  • Articles and reports: 12-001-X200900211038
    Description:

    We examine overcoming the overestimation in using generalized weight share method (GWSM) caused by link nonresponse in indirect sampling. A few adjustment methods incorporating link nonresponse in using GWSM have been constructed for situations both with and without the availability of auxiliary variables. A simulation study on a longitudinal survey is presented using some of the adjustment methods we recommend. The simulation results show that these adjusted GWSMs perform well in reducing both estimation bias and variance. The advancement in bias reduction is significant.

    Release date: 2009-12-23

  • Articles and reports: 11-522-X200800010959
    Description:

    The Unified Enterprise Survey (UES) at Statistics Canada is an annual business survey that unifies more than 60 surveys from different industries. Two types of collection follow-up score functions are currently used in the UES data collection. The objective of using a score function is to maximize the economically weighted response rates of the survey in terms of the primary variables of interest, under the constraint of a limited follow-up budget. Since the two types of score functions are based on different methodologies, they could have different impacts on the final estimates.

    This study generally compares the two types of score functions based on the collection data obtained from the two recent years. For comparison purposes, this study applies each score function method to the same data respectively and computes various estimates of the published financial and commodity variables, their deviation from the true pseudo value and their mean square deviation, based on each method. These estimates of deviation and mean square deviation based on each method are then used to measure the impact of each score function on the final estimates of the financial and commodity variables.

    Release date: 2009-12-03
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