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  • Articles and reports: 12-001-X200900211040
    Description:

    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

  • Articles and reports: 12-001-X20050029053
    Description:

    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

  • Articles and reports: 12-001-X19990014709
    Description:

    We develop an approach to estimating variances for X-11 seasonal adjustments that recognizes the effects of sampling error and errors from forecast extension. In our approach, seasonal adjustment error in the central values of a sufficiently long series results only from the effect of the X-11 filtering on the sampling errors. Towards either end of the series, we also recognize the contribution to seasonal adjustment error from forecast and backcast errors. We extend the approach to produce variances of errors in X-11 trend estimates, and to recognize error in estimation of regression coefficients used to model, e.g., calendar effects. In empirical results, the contribution of sampling error often dominated the seasonal adjustment variances. Trend estimate variances, however, showed large increases at the ends of series due to the effects of fore/backcast error. Nonstationarities in the sampling errors produced striking patterns in the seasonal adjustment and trend estimate variances.

    Release date: 1999-10-08
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  • Articles and reports: 12-001-X200900211040
    Description:

    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

  • Articles and reports: 12-001-X20050029053
    Description:

    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

  • Articles and reports: 12-001-X19990014709
    Description:

    We develop an approach to estimating variances for X-11 seasonal adjustments that recognizes the effects of sampling error and errors from forecast extension. In our approach, seasonal adjustment error in the central values of a sufficiently long series results only from the effect of the X-11 filtering on the sampling errors. Towards either end of the series, we also recognize the contribution to seasonal adjustment error from forecast and backcast errors. We extend the approach to produce variances of errors in X-11 trend estimates, and to recognize error in estimation of regression coefficients used to model, e.g., calendar effects. In empirical results, the contribution of sampling error often dominated the seasonal adjustment variances. Trend estimate variances, however, showed large increases at the ends of series due to the effects of fore/backcast error. Nonstationarities in the sampling errors produced striking patterns in the seasonal adjustment and trend estimate variances.

    Release date: 1999-10-08
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