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  • Articles and reports: 12-001-X200900110885
    Description:

    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-X20030017694
    Description:

    This paper evaluates the performance of diagnostics for seasonal adjustment as they relate to the performance of X-12-ARIMA and SEATS on a large sample of real and simulated economic time series.

    Release date: 2005-01-26

  • Articles and reports: 11-522-X20030017695
    Description:

    This paper proposes methods to correct a seasonally adjusted series so that its annual totals match those of the raw series. The methods are illustrated with a seasonally adjusted series obtained with either X-11-ARIMA or X-12-ARIMA.

    Release date: 2005-01-26

  • Notices and consultations: 62-010-X19970023422
    Description:

    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

  • Articles and reports: 12-001-X199000214535
    Description:

    Papers by Scott and Smith (1974) and Scott, Smith, and Jones (1977) suggested the use of signal extraction results from time series analysis to improve estimates in repeated surveys, what we call the time series approach to estimation in repeated surveys. We review the underlying philosophy of this approach, pointing out that it stems from recognition of two sources of variation - time series variation and sampling variation - and that the approach can provide a unifying framework for other problems where the two sources of variation are present. We obtain some theoretical results for the time series approach regarding design consistency of the time series estimators, and uncorrelatedness of the signal and sampling error series. We observe that, from a design-based perspective, the time series approach trades some bias for a reduction in variance and a reduction in average mean squared error relative to classical survey estimators. We briefly discuss modeling to implement the time series approach, and then illustrate the approach by applying it to time series of retail sales of eating places and of drinking places from the U.S. Census Bureau’s Retail Trade Survey.

    Release date: 1990-12-14

  • Articles and reports: 12-001-X198600214448
    Description:

    The seasonal adjustment of a time series is not a straightforward procedure particularly when the level of a series nearly doubles in just one year. The 1981-82 recession had a very sudden great impact not only on the structure of the series but on the estimation of the trend- cycle and seasonal components at the end of the series. Serious seasonal adjustment problems can occur. For instance: the selection of the wrong decomposition model may produce underadjustment in the seasonally high months and overadjustment in the seasonally low months. The wrong decomposition model may also signal a false turning point. This article analyses these two aspects of the interplay between a severe recession and seasonal adjustment.

    Release date: 1986-12-15

  • Articles and reports: 12-001-X198500214376
    Description:

    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

  • Articles and reports: 12-001-X198500114366
    Description:

    This study is mainly concerned with an evaluation of the forecasting performance of a set of the most often applied ARIMA models. These models were fitted to a sample of two hundred seasonal time series chosen from eleven sectors of the Canadian economy. The performance of the models was judged according to eight variable criteria, namely: average forecast error for the last three years, the chi-square statistic for the randomness of the residuals, the presence of small parameters, overdifferencing, underdifferencing, correlation between the parameters, stationarity and invertibility. Overall and conditional rankings of the models are obtained and graphs are presented.

    Release date: 1985-06-14
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Analysis (7)

Analysis (7) ((7 results))

  • Articles and reports: 12-001-X200900110885
    Description:

    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-X20030017694
    Description:

    This paper evaluates the performance of diagnostics for seasonal adjustment as they relate to the performance of X-12-ARIMA and SEATS on a large sample of real and simulated economic time series.

    Release date: 2005-01-26

  • Articles and reports: 11-522-X20030017695
    Description:

    This paper proposes methods to correct a seasonally adjusted series so that its annual totals match those of the raw series. The methods are illustrated with a seasonally adjusted series obtained with either X-11-ARIMA or X-12-ARIMA.

    Release date: 2005-01-26

  • Articles and reports: 12-001-X199000214535
    Description:

    Papers by Scott and Smith (1974) and Scott, Smith, and Jones (1977) suggested the use of signal extraction results from time series analysis to improve estimates in repeated surveys, what we call the time series approach to estimation in repeated surveys. We review the underlying philosophy of this approach, pointing out that it stems from recognition of two sources of variation - time series variation and sampling variation - and that the approach can provide a unifying framework for other problems where the two sources of variation are present. We obtain some theoretical results for the time series approach regarding design consistency of the time series estimators, and uncorrelatedness of the signal and sampling error series. We observe that, from a design-based perspective, the time series approach trades some bias for a reduction in variance and a reduction in average mean squared error relative to classical survey estimators. We briefly discuss modeling to implement the time series approach, and then illustrate the approach by applying it to time series of retail sales of eating places and of drinking places from the U.S. Census Bureau’s Retail Trade Survey.

    Release date: 1990-12-14

  • Articles and reports: 12-001-X198600214448
    Description:

    The seasonal adjustment of a time series is not a straightforward procedure particularly when the level of a series nearly doubles in just one year. The 1981-82 recession had a very sudden great impact not only on the structure of the series but on the estimation of the trend- cycle and seasonal components at the end of the series. Serious seasonal adjustment problems can occur. For instance: the selection of the wrong decomposition model may produce underadjustment in the seasonally high months and overadjustment in the seasonally low months. The wrong decomposition model may also signal a false turning point. This article analyses these two aspects of the interplay between a severe recession and seasonal adjustment.

    Release date: 1986-12-15

  • Articles and reports: 12-001-X198500214376
    Description:

    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

  • Articles and reports: 12-001-X198500114366
    Description:

    This study is mainly concerned with an evaluation of the forecasting performance of a set of the most often applied ARIMA models. These models were fitted to a sample of two hundred seasonal time series chosen from eleven sectors of the Canadian economy. The performance of the models was judged according to eight variable criteria, namely: average forecast error for the last three years, the chi-square statistic for the randomness of the residuals, the presence of small parameters, overdifferencing, underdifferencing, correlation between the parameters, stationarity and invertibility. Overall and conditional rankings of the models are obtained and graphs are presented.

    Release date: 1985-06-14
Reference (1)

Reference (1) ((1 result))

  • Notices and consultations: 62-010-X19970023422
    Description:

    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
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