On the Problem of Variance Estimation for a Deseasonalized Series

Wolter, Kirk M.; Monsour, Nash J.

doi:10.1016/b978-0-12-426280-5.50028-9

Cited by 8 publications

(7 citation statements)

References 8 publications

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“…The purpose of this article is to develop measures of variability for model-based adjustment procedures and to apply these ideas to the autoregressive integrated moving average (ARIMA) model-based approach described in Hillmer and Tiao (1982). Wolter and Monsour (1981) presented two possible concepts of variability associated with the seasonal adjustment process. The first concept assumes that the unobserved N, values are fixed, with no probabilistic relationships between them.…”

Section: Introductionmentioning

confidence: 99%

Measures of Variability for Model-Based Seasonal Adjustment Procedures

Hillmer

1985

Journal of Business & Economic Statistics

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An algorithm is derived that develops measures of variability for the estimates of the nonseasonal component computed from a model-based seasonal adjustment procedure. The measures of variability are developed from signal extraction theory. Properties of components of the variance are developed, and the behavior of the variance is investigated for one popular time series model. The results are illustrated by using real data.

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Section: Introductionmentioning

confidence: 99%

Measures of Variability for Model-Based Seasonal Adjustment Procedures

Hillmer

1985

Journal of Business & Economic Statistics

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“…Wolter and Monsour (1981) were the first to use the linear approximation for variance estimation. They consider two scenarios.…”

Section: Comparison With Variances Computed In Other Studiesmentioning

confidence: 99%

“…The need to provide estimates for the variances of the SAEs has a long standing and several procedures have been proposed in the past to deal with this problem. Wolter and Monsour (1981) were the first to use the linear approximation for variance estimation. They consider two scenarios.…”

Section: Comparison With Variances Computed In Other Studiesmentioning

confidence: 99%

“…The other procedure proposed by Wolter and Monsour (1981) assumes that the trend and the seasonal components can be approximated by fixed polynomials in time which can be identified from the data. This assumption is often too restrictive and the commonly used time series models allow these components to evolve stochastically.…”

Section: Comparison With Variances Computed In Other Studiesmentioning

confidence: 99%

“…This assumption is often too restrictive and the commonly used time series models allow these components to evolve stochastically. See the discussion in Pierce (1980) and the concluding remarks in Wolter and Monsour (1981).…”

Section: Comparison With Variances Computed In Other Studiesmentioning

confidence: 99%

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A General Method for Estimating the Variances of X‐11 Seasonally Adjusted Estimators

Pfeffermann

1994

Journal Time Series Analysis

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The X-11 procedure with its various variants is the commonly used procedure for seasonal adjustment throughout the world. A well-known problem with the use of this procedure, however, is the estimation of the variances of its output such as, for example, the variances of the seasonally adjusted data or the month to month changes in these data. In this paper we propose a simple general procedure for estimating the variances of the X-11 estimators. The variances account for the sampling distribution of the survey estimators around the corresponding population values and for the distribution of the component series included in the decomposition of the population values. The procedure is applicable to general sampling designs, including partially overlapping surveys. Empirical results illustrating the performance of the procedure when applied to simulated and real series are presented.

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A nonparametric method for asymmetrically extending signal extraction filters

McElroy

2011

Journal of Forecasting

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Two important problems in the X-11 seasonal adjustment methodology are the construction of standard errors and the handling of the boundaries. We adapt the 'implied model approach' of Kaiser and Maravall to achieve both objectives in a nonparametric fashion. The frequency response function of an X-11 linear fi lter is used, together with the periodogram of the differenced data, to defi ne spectral density estimates for signal and noise. These spectra are then used to defi ne a matrix smoother, which in turn generates an estimate of the signal that is linear in the data. Estimates of the signal are provided at all time points in the sample, and the associated time-varying signal extraction mean squared errors are a by-product of the matrix smoother theory. After explaining our method, it is applied to popular nonparametric fi lters such as the HodrickPrescott (HP), the Henderson trend, and ideal low-pass and band-pass fi lters, as well as X-11 seasonal adjustment, trend, and irregular fi lters. Finally, we illustrate the method on several time series and provide comparisons with X-12-ARIMA seasonal adjustments.

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On the Problem of Variance Estimation for a Deseasonalized Series

Cited by 8 publications

References 8 publications

Measures of Variability for Model-Based Seasonal Adjustment Procedures

Measures of Variability for Model-Based Seasonal Adjustment Procedures

A General Method for Estimating the Variances of X‐11 Seasonally Adjusted Estimators

A nonparametric method for asymmetrically extending signal extraction filters

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