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- Articles and reports: 12-001-X202600100001Description: Wayne A. Fuller is a leading figure in statistics whose career at Iowa State University (ISU) began in 1959; he is now Distinguished Professor Emeritus in Statistics and Economics. This article briefly recounts his early life and training in agricultural economics at ISU and highlights influential contributions spanning time series analysis, measurement error models, and survey sampling. It documents his impact through seminal textbooks, methodological advances such as the Dickey-Fuller test and regression estimation, sustained work on major operational surveys (e.g., the National Resources Inventory), and mentorship of many graduate students. The article includes an interview conducted on May 20th, 2025, at Professor Fuller’s home.Release date: 2026-06-29
- Surveys and statistical programs – Documentation: 19-20-00012026002Description: This reference document provides answers on selected topics related to the use, interpretation, and calculation of trend-cycle estimates for seasonally adjusted data. It is designed to complement more technical discussions of seasonal adjustment and trend-cycle estimation found in Statistics Canada publications and reference manuals.Release date: 2026-06-08
- Journals and periodicals: 11-633-XDescription: Papers in this series provide background discussions of the methods used to develop data for economic, health, and social analytical studies at Statistics Canada. They are intended to provide readers with information on the statistical methods, standards and definitions used to develop databases for research purposes. All papers in this series have undergone peer and institutional review to ensure that they conform to Statistics Canada's mandate and adhere to generally accepted standards of good professional practice.Release date: 2026-05-27
- Surveys and statistical programs – Documentation: 19-20-00012026001Description: This reference document provides nontechnical answers on selected topics related to the use and interpretation of seasonally adjusted data. It is designed to complement more technical discussions of seasonal adjustment found in Statistics Canada publications and reference manuals.Release date: 2026-05-11
- Articles and reports: 75-006-X202400100007Description: This study uses data from multiple waves of the Canadian Social Survey (CSS) to examine trends in three key Quality of Life indicators, namely life satisfaction, experiences of financial hardship, and future outlook. Monitoring these well-being indicators following periods of considerable social and economic change is particularly important. Beginning in the summer of 2021, the CSS, a new quarterly survey, captured the latter part of the COVID-19 pandemic as well as the rising cost of living in Canada, allowing for an understanding of how Canadians are coping with these challenges.Release date: 2024-09-13
- Stats in brief: 11-001-X202425738424Description: Release published in The Daily – Statistics Canada’s official release bulletinRelease date: 2024-09-13
- Articles and reports: 12-001-X202300100010Description: Precise and unbiased estimates of response propensities (RPs) play a decisive role in the monitoring, analysis, and adaptation of data collection. In a fixed survey climate, those parameters are stable and their estimates ultimately converge when sufficient historic data is collected. In survey practice, however, response rates gradually vary in time. Understanding time-dependent variation in predicting response rates is key when adapting survey design. This paper illuminates time-dependent variation in response rates through multi-level time-series models. Reliable predictions can be generated by learning from historic time series and updating with new data in a Bayesian framework. As an illustrative case study, we focus on Web response rates in the Dutch Health Survey from 2014 to 2019.Release date: 2023-06-30
- Articles and reports: 11-522-X202100100020Description: Seasonal adjustment of time series at Statistics Canada is performed using the X-12-ARIMA method. For most statistical programs performing seasonal adjustment, subject matter experts (SMEs) are responsible for managing the program and for verification, analysis and dissemination of the data, while methodologists from the Time Series Research and Analysis Center (TSRAC) are responsible for developing and maintaining the seasonal adjustment process and for providing support on seasonal adjustment to SMEs. A visual summary report called the seasonal adjustment dashboard has been developed in R Shiny by the TSRAC to build capacity to interpret seasonally adjusted data and to reduce the resources needed to support seasonal adjustment. It is currently being made available internally to assist SMEs to interpret and explain seasonally adjusted results. The summary report includes graphs of the series across time, as well as summaries of individual seasonal and calendar effects and patterns. Additionally, key seasonal adjustment diagnostics are presented and the net effect of seasonal adjustment is decomposed into its various components. This paper gives a visual representation of the seasonal adjustment process, while demonstrating the dashboard and its interactive functionality.
Key Words: Time Series; X-12-ARIMA; Summary Report; R Shiny.
Release date: 2021-10-15 - 9. Growth Rates Preservation (GRP) temporal benchmarking: Drawbacks and alternative solutions ArchivedArticles and reports: 12-001-X201800154927Description:
Benchmarking monthly or quarterly series to annual data is a common practice in many National Statistical Institutes. The benchmarking problem arises when time series data for the same target variable are measured at different frequencies and there is a need to remove discrepancies between the sums of the sub-annual values and their annual benchmarks. Several benchmarking methods are available in the literature. The Growth Rates Preservation (GRP) benchmarking procedure is often considered the best method. It is often claimed that this procedure is grounded on an ideal movement preservation principle. However, we show that there are important drawbacks to GRP, relevant for practical applications, that are unknown in the literature. Alternative benchmarking models will be considered that do not suffer from some of GRP’s side effects.
Release date: 2018-06-21 - Articles and reports: 82-003-X201800254908Description:
This study examined nine national surveys of the household population which collected information about drug use during the period from 1985 through 2015. These surveys are examined for comparability. The data are used to estimate past-year (current) cannabis use (total, and by sex and age). Based on the most comparable data, trends in use from 2004 through 2015 are estimated.
Release date: 2018-02-21
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- Articles and reports: 13-604-M2015077Description:
This new dataset increases the information available for comparing the performance of provinces and territories across a range of measures. It combines often fragmented provincial time series data that, as such, are of limited utility for examining the evolution of provincial economies over extended periods. More advanced statistical methods, and models with greater breadth and depth, are difficult to apply to existing fragmented Canadian data. The longitudinal nature of the new provincial dataset remedies this shortcoming. This report explains the construction of the latest vintage of the dataset. The dataset contains the most up-to-date information available.
Release date: 2015-02-12 - Articles and reports: 12-001-X201400214110Description:
In developing the sample design for a survey we attempt to produce a good design for the funds available. Information on costs can be used to develop sample designs that minimise the sampling variance of an estimator of total for fixed cost. Improvements in survey management systems mean that it is now sometimes possible to estimate the cost of including each unit in the sample. This paper develops relatively simple approaches to determine whether the potential gains arising from using this unit level cost information are likely to be of practical use. It is shown that the key factor is the coefficient of variation of the costs relative to the coefficient of variation of the relative error on the estimated cost coefficients.
Release date: 2014-12-19 - 13. Seasonal adjustment and identifying economic trends ArchivedArticles and reports: 11-010-X201000311141Geography: CanadaDescription:
A review of what seasonal adjustment does, and how it helps analysts focus on recent movements in the underlying trend of economic data.
Release date: 2010-03-18 - 14. Estimation of the monthly unemployment rate through structural time series modelling in a rotating panel design ArchivedArticles and reports: 12-001-X200900211040Description:
In this paper a multivariate structural time series model is described that accounts for the panel design of the Dutch Labour Force Survey and is applied to estimate monthly unemployment rates. Compared to the generalized regression estimator, this approach results in a substantial increase of the accuracy due to a reduction of the standard error and the explicit modelling of the bias between the subsequent waves.
Release date: 2009-12-23 - 15. A nonparametric test for residual seasonality ArchivedArticles and reports: 12-001-X200900110885Description:
Peaks in the spectrum of a stationary process are indicative of the presence of stochastic periodic phenomena, such as a stochastic seasonal effect. This work proposes to measure and test for the presence of such spectral peaks via assessing their aggregate slope and convexity. Our method is developed nonparametrically, and thus may be useful during a preliminary analysis of a series. The technique is also useful for detecting the presence of residual seasonality in seasonally adjusted data. The diagnostic is investigated through simulation and an extensive case study using data from the U.S. Census Bureau and the Organization for Economic Co-operation and Development (OECD).
Release date: 2009-06-22 - Articles and reports: 11-522-X200600110398Description:
The study of longitudinal data is vital in terms of accurately observing changes in responses of interest for individuals, communities, and larger populations over time. Linear mixed effects models (for continuous responses observed over time) and generalized linear mixed effects models and generalized estimating equations (for more general responses such as binary or count data observed over time) are the most popular techniques used for analyzing longitudinal data from health studies, though, as with all modeling techniques, these approaches have limitations, partly due to their underlying assumptions. In this review paper, we will discuss some advances, including curve-based techniques, which make modeling longitudinal data more flexible. Three examples will be presented from the health literature utilizing these more flexible procedures, with the goal of demonstrating that some otherwise difficult questions can be reasonably answered when analyzing complex longitudinal data in population health studies.
Release date: 2008-03-17 - 17. Estimating TFP in the Presence of Outliers and Leverage Points: An Examination of the KLEMS Dataset ArchivedArticles and reports: 11F0027M2007047Geography: CanadaDescription: This paper examines the effect of aberrant observations in the Capital, Labour, Energy, Materials and Services (KLEMS) database and a method for dealing with them. The level of disaggregation, data construction and economic shocks all potentially lead to aberrant observations that can influence estimates and inference if care is not exercised. Commonly applied pre-tests, such as the augmented Dickey-Fuller and the Kwaitkowski, Phillips, Schmidt and Shin tests, need to be used with caution in this environment because they are sensitive to unusual data points. Moreover, widely known methods for generating statistical estimates, such as Ordinary Least Squares, may not work well when confronted with aberrant observations. To address this, a robust method for estimating statistical relationships is illustrated.Release date: 2007-12-05
- Articles and reports: 11-522-X20050019467Description:
This paper reviews techniques for dealing with missing data from complex surveys when conducting longitudinal analysis. In addition to incurring the same types of missingness as cross sectional data, longitudinal observations also suffer from drop out missingness. For the purpose of analyzing longitudinal data, random effects models are most often used to account for the longitudinal nature of the data. However, there are difficulties in incorporating the complex design with typical multi-level models that are used in this type of longitudinal analysis, especially in the presence of drop-out missingness.
Release date: 2007-03-02 - 19. Taking stock: the future of longitudinal surveys ArchivedArticles and reports: 11-522-X20050019469Description:
The 1990s was the decade of longitudinal surveys in Canada. The focus was squarely on the benefits that could be derived from the increased analytical power of longitudinal surveys. This presentation explores issues of insights gained, timeliness, data access, survey design, complexity, research capacity, survey governance and knowledge mobilisation. This presentation outlines some of the issues that are likely to be raised in any debate regarding longitudinal surveys.
Release date: 2007-03-02 - 20. Spatio-temporal models in small area estimation ArchivedArticles and reports: 12-001-X20050029053Description:
A spatial regression model in a general mixed effects model framework has been proposed for the small area estimation problem. A common autocorrelation parameter across the small areas has resulted in the improvement of the small area estimates. It has been found to be very useful in the cases where there is little improvement in the small area estimates due to the exogenous variables. A second order approximation to the mean squared error (MSE) of the empirical best linear unbiased predictor (EBLUP) has also been worked out. Using the Kalman filtering approach, a spatial temporal model has been proposed. In this case also, a second order approximation to the MSE of the EBLUP has been obtained. As a case study, the time series monthly per capita consumption expenditure (MPCE) data from the National Sample Survey Organisation (NSSO) of the Ministry of Statistics and Programme Implementation, Government of India, have been used for the validation of the models.
Release date: 2006-02-17
Reference (8)
Reference (8) ((8 results))
- Surveys and statistical programs – Documentation: 19-20-00012026002Description: This reference document provides answers on selected topics related to the use, interpretation, and calculation of trend-cycle estimates for seasonally adjusted data. It is designed to complement more technical discussions of seasonal adjustment and trend-cycle estimation found in Statistics Canada publications and reference manuals.Release date: 2026-06-08
- Surveys and statistical programs – Documentation: 19-20-00012026001Description: This reference document provides nontechnical answers on selected topics related to the use and interpretation of seasonally adjusted data. It is designed to complement more technical discussions of seasonal adjustment found in Statistics Canada publications and reference manuals.Release date: 2026-05-11
- Surveys and statistical programs – Documentation: 11-522-X19990015648Description:
We estimate the parameters of a stochastic model for labour force careers involving distributions of correlated durations employed, unemployed (with and without job search) and not in the labour force. If the model is to account for sub-annual labour force patterns as well as advancement towards retirement, then no single data source is adequate to inform it. However, it is possible to build up an approximation from a number of different sources.
Release date: 2000-03-02 - 4. Particulate matter and daily mortality: Combining time series information from eight U.S. cities ArchivedSurveys and statistical programs – Documentation: 11-522-X19990015656Description:
Time series studies have shown associations between air pollution concentrations and morbidity and mortality. These studies have largely been conducted within single cities, and with varying methods. Critics of these studies have questioned the validity of the data sets used and the statistical techniques applied to them; the critics have noted inconsistencies in findings among studies and even in independent re-analyses of data from the same city. In this paper we review some of the statistical methods used to analyze a subset of a national data base of air pollution, mortality and weather assembled during the National Morbidity and Mortality Air Pollution Study (NMMAPS).
Release date: 2000-03-02 - Surveys and statistical programs – Documentation: 11-522-X19990015688Description:
The geographical and temporal relationship between outdoor air pollution and asthma was examined by linking together data from multiple sources. These included the administrative records of 59 general practices widely dispersed across England and Wales for half a million patients and all their consultations for asthma, supplemented by a socio-economic interview survey. Postcode enabled linkage with: (i) computed local road density; (ii) emission estimates of sulphur dioxide and nitrogen dioxides, (iii) measured/interpolated concentration of black smoke, sulphur dioxide, nitrogen dioxide and other pollutants at practice level. Parallel Poisson time series analysis took into account between-practice variations to examine daily correlations in practices close to air quality monitoring stations. Preliminary analyses show small and generally non-significant geographical associations between consultation rates and pollution markers. The methodological issues relevant to combining such data, and the interpretation of these results will be discussed.
Release date: 2000-03-02 - Surveys and statistical programs – Documentation: 11-522-X19980015031Description:
The U.S. Third National Health and Nutrition Examination Survey (NHANES III) was carried out from 1988 to 1994. This survey was intended primarily to provide estimates of cross-sectional parameters believed to be approximately constant over the six-year data collection period. However, for some variable (e.g., serum lead, body mass index and smoking behavior), substantive considerations suggest the possible presence of nontrivial changes in level between 1988 and 1994. For these variables, NHANES III is potentially a valuable source of time-change information, compared to other studies involving more restricted populations and samples. Exploration of possible change over time is complicated by two issues. First, there was of practical concern because some variables displayed substantial regional differences in level. This was of practical concern because some variables displayed substantial regional differences in level. Second, nontrivial changes in level over time can lead to nontrivial biases in some customary NHANES III variance estimators. This paper considers these two problems and discusses some related implications for statistical policy.
Release date: 1999-10-22 - 7. Probability of victimization over time: Results from the U.S. National Crime Victimization Survey ArchivedSurveys and statistical programs – Documentation: 11-522-X19980015033Description:
Victimizations are not randomly scattered through the population, but tend to be concentrated in relatively few victims. Data from the U.S. National Crime Victimization Survey (NCVS), a multistage rotating panel survey, are employed to estimate the conditional probabilities of being a crime victim at time t given the victimization status in earlier interviews. Models are presented and fit to allow use of partial information from households that move in or out of the housing unit during the study period. The estimated probability of being a crime victim at interview t given the status at interview (t-l) is found to decrease with t. Possible implications for estimating cross-sectional victimization rates are discusssed.
Release date: 1999-10-22 - Notices and consultations: 62-010-X19970023422Description:
The current official time base of the Consumer Price Index (CPI) is 1986=100. This time base was first used when the CPI for June 1990 was released. Statistics Canada is about to convert all price index series to the time base 1992=100. As a result, all constant dollar series will be converted to 1992 dollars. The CPI will shift to the new time base when the CPI for January 1998 is released on February 27th, 1998.
Release date: 1997-11-17