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All (10) ((10 results))
- 1. 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 - 2. Social media as a data source for official statistics; the Dutch Consumer Confidence Index ArchivedArticles and reports: 12-001-X201700254871Description:
In this paper the question is addressed how alternative data sources, such as administrative and social media data, can be used in the production of official statistics. Since most surveys at national statistical institutes are conducted repeatedly over time, a multivariate structural time series modelling approach is proposed to model the series observed by a repeated surveys with related series obtained from such alternative data sources. Generally, this improves the precision of the direct survey estimates by using sample information observed in preceding periods and information from related auxiliary series. This model also makes it possible to utilize the higher frequency of the social media to produce more precise estimates for the sample survey in real time at the moment that statistics for the social media become available but the sample data are not yet available. The concept of cointegration is applied to address the question to which extent the alternative series represent the same phenomena as the series observed with the repeated survey. The methodology is applied to the Dutch Consumer Confidence Survey and a sentiment index derived from social media.
Release date: 2017-12-21 - 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 - 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 - Articles and reports: 12-001-X19980024351Description:
To calculate price indexes, data on "the same item" (actually a collection of items narrowly defined) must be collected across time periods. The question arises whether such "quasi-longitudinal" data can be modeled in such a way as to shed light on what a price index is. Leading thinkers on price indexes have questioned the feasibility of using statistical modeling at all for characterizing price indexes. This paper suggests a simple state space model of price data, yielding a consumer price index that is given in terms of the parameters of the model.
Release date: 1999-01-14 - Articles and reports: 12-001-X199000214533Description:
A commonly used model for the analysis of time series models is the seasonal ARIMA model. However, the survey errors of the input data are usually ignored in the analysis. We show, through the use of state-space models with partially improper initial conditions, how to estimate the unknown parameters of this model using maximum likelihood methods. As well, the survey estimates can be smoothed using an empirical Bayes framework and model validation can be performed. We apply these techniques to an unemployment series from the Labour Force Survey.
Release date: 1990-12-14 - 7. Modelling and estimation for repeated surveys ArchivedArticles and reports: 12-001-X198900114579Description:
Estimation of the means of a characteristic for a population at different points in time, based on a series of repeated surveys, is briefly reviewed. By imposing a stochastic parametric model on these means, it is possible to estimate the parameters of the model and to obtain alternative estimators of the means themselves. We describe the case where the population means follow an autoregressive-moving average (ARMA) process and the survey errors can also be formulated as an ARMA process. An example using data from the Canadian Travel Survey is presented.
Release date: 1989-06-15 - 8. Current issues on seasonal adjustment ArchivedArticles and reports: 12-001-X198700114509Description:
This paper discusses three problems that have been a major preoccupation among researchers and practitioners of seasonal adjustment in statistical bureaus for the last ten years. These problems are: (l) the use of concurrent seasonal factors versus seasonal factor forecasts for current seasonal adjustment; (2) finding an optimal pattern of revisions for series seasonally adjusted with concurrent factors; and (3) smoothing highly irregular seasonally adjusted data.
Release date: 1987-06-15 - Articles and reports: 12-001-X198500214375Description:
This paper analyzes the revisions of eight seasonally adjusted labour force series during recession and non-recession periods. The four seasonal adjustment methods applied are X-11 and X-11-ARIMA using either concurrent or forecast seasonal factors. The series are seasonally adjusted with these four methodologies according to both a multiplicative and an additive decomposition model. The results indicate that the X-11-ARIMA concurrent adjustment yields the smallest revisions both during recession and non-recession periods regardless of the decomposition model used.
Release date: 1985-12-16 - 10. Relational patterns between total unemployment and unemployment insurance beneficiaries in Canada ArchivedArticles and reports: 12-001-X198500214376Description:
This study purports to assess whether there are temporal relationships between Unemployment Insurance Beneficiaries, Total Unemployment, Job Losers and Job Leavers in Canada using univariate and multivariate time series methods. The results indicate that during 1975-82 the Unemployment Insurance Beneficiaries series leads: (1) Total Unemployment by one month and (2) Job Leavers by two months. On the other hand, there are evidence of a feedback relationship between Unemployment Insurance Beneficiaries and Job Losers.
Release date: 1985-12-16
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Analysis (9)
Analysis (9) ((9 results))
- 1. 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 - 2. Social media as a data source for official statistics; the Dutch Consumer Confidence Index ArchivedArticles and reports: 12-001-X201700254871Description:
In this paper the question is addressed how alternative data sources, such as administrative and social media data, can be used in the production of official statistics. Since most surveys at national statistical institutes are conducted repeatedly over time, a multivariate structural time series modelling approach is proposed to model the series observed by a repeated surveys with related series obtained from such alternative data sources. Generally, this improves the precision of the direct survey estimates by using sample information observed in preceding periods and information from related auxiliary series. This model also makes it possible to utilize the higher frequency of the social media to produce more precise estimates for the sample survey in real time at the moment that statistics for the social media become available but the sample data are not yet available. The concept of cointegration is applied to address the question to which extent the alternative series represent the same phenomena as the series observed with the repeated survey. The methodology is applied to the Dutch Consumer Confidence Survey and a sentiment index derived from social media.
Release date: 2017-12-21 - 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 - Articles and reports: 12-001-X19980024351Description:
To calculate price indexes, data on "the same item" (actually a collection of items narrowly defined) must be collected across time periods. The question arises whether such "quasi-longitudinal" data can be modeled in such a way as to shed light on what a price index is. Leading thinkers on price indexes have questioned the feasibility of using statistical modeling at all for characterizing price indexes. This paper suggests a simple state space model of price data, yielding a consumer price index that is given in terms of the parameters of the model.
Release date: 1999-01-14 - Articles and reports: 12-001-X199000214533Description:
A commonly used model for the analysis of time series models is the seasonal ARIMA model. However, the survey errors of the input data are usually ignored in the analysis. We show, through the use of state-space models with partially improper initial conditions, how to estimate the unknown parameters of this model using maximum likelihood methods. As well, the survey estimates can be smoothed using an empirical Bayes framework and model validation can be performed. We apply these techniques to an unemployment series from the Labour Force Survey.
Release date: 1990-12-14 - 6. Modelling and estimation for repeated surveys ArchivedArticles and reports: 12-001-X198900114579Description:
Estimation of the means of a characteristic for a population at different points in time, based on a series of repeated surveys, is briefly reviewed. By imposing a stochastic parametric model on these means, it is possible to estimate the parameters of the model and to obtain alternative estimators of the means themselves. We describe the case where the population means follow an autoregressive-moving average (ARMA) process and the survey errors can also be formulated as an ARMA process. An example using data from the Canadian Travel Survey is presented.
Release date: 1989-06-15 - 7. Current issues on seasonal adjustment ArchivedArticles and reports: 12-001-X198700114509Description:
This paper discusses three problems that have been a major preoccupation among researchers and practitioners of seasonal adjustment in statistical bureaus for the last ten years. These problems are: (l) the use of concurrent seasonal factors versus seasonal factor forecasts for current seasonal adjustment; (2) finding an optimal pattern of revisions for series seasonally adjusted with concurrent factors; and (3) smoothing highly irregular seasonally adjusted data.
Release date: 1987-06-15 - Articles and reports: 12-001-X198500214375Description:
This paper analyzes the revisions of eight seasonally adjusted labour force series during recession and non-recession periods. The four seasonal adjustment methods applied are X-11 and X-11-ARIMA using either concurrent or forecast seasonal factors. The series are seasonally adjusted with these four methodologies according to both a multiplicative and an additive decomposition model. The results indicate that the X-11-ARIMA concurrent adjustment yields the smallest revisions both during recession and non-recession periods regardless of the decomposition model used.
Release date: 1985-12-16 - 9. Relational patterns between total unemployment and unemployment insurance beneficiaries in Canada ArchivedArticles and reports: 12-001-X198500214376Description:
This study purports to assess whether there are temporal relationships between Unemployment Insurance Beneficiaries, Total Unemployment, Job Losers and Job Leavers in Canada using univariate and multivariate time series methods. The results indicate that during 1975-82 the Unemployment Insurance Beneficiaries series leads: (1) Total Unemployment by one month and (2) Job Leavers by two months. On the other hand, there are evidence of a feedback relationship between Unemployment Insurance Beneficiaries and Job Losers.
Release date: 1985-12-16
Reference (1)
Reference (1) ((1 result))
- 1. 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
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