Estimation of the monthly unemployment rate for six domains through structural time series modelling with cointegrated trends
National statistical institutes generally apply design-based techniques like the generalized regression estimator to compile official statistics. These techniques, however, have relatively large design variances in the case of small sample sizes. In such cases, model based small area estimation techniques can be considered to improve the precision of the estimates. A multivariate structural time series model is developed and applied to obtain more precise estimates of the Dutch monthly unemployment rate for six domains. The model takes advantage of sample information from preceding time periods through an appropriate time series model and from other domains by modelling the correlation between the trend components of the time series models for the different domains. The trends of the six domains are cointegrated, which allows the use of a more parsimonious common factor model that is based on three common trends. Although the use of common factor models is a well known approach in econometrics, its application in the context of small area estimation is novel. The standard errors of the direct estimates of the monthly unemployment rates are more than halved with this approach.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 56 (2012)
Issue (Month): 10 ()
|Contact details of provider:|| Web page: http://www.elsevier.com/locate/csda|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Pfeffermann, Danny & Feder, Moshe & Signorelli, David, 1998. "Estimation of Autocorrelations of Survey Errors with Application to Trend Estimation in Small Areas," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 339-348, July.
- Harm Jan Boonstra & Jan A. Van Den Brakel & Bart Buelens & Sabine Krieg & Marc Smeets, 2008. "Towards small area estimation at Statistics Netherlands," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 21-49.
- Durbin, James & Koopman, Siem Jan, 2012.
"Time Series Analysis by State Space Methods,"
Oxford University Press,
edition 2, number 9780199641178.
- Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, number 9780198523543.
- Tom Doan, "undated". "SEASONALDLM: RATS procedure to create the matrices for the seasonal component of a DLM," Statistical Software Components RTS00251, Boston College Department of Economics.
- Andrew Harvey & Chia-Hui Chung, 2000. "Estimating the underlying change in unemployment in the UK," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(3), pages 303-309.
- Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 107-160.
- Koopman, S.J.M. & Shephard, N. & Doornik, J.A., 1998. "Statistical Algorithms for Models in State Space Using SsfPack 2.2," Discussion Paper 1998-141, Tilburg University, Center for Economic Research.
- Moshe Feder, 2001. "Time Series Analysis of Repeated Surveys: The State-space Approach," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 55(2), pages 182-199.
- Pfeffermann, Danny, 1991. "Estimation and Seasonal Adjustment of Population Means Using Data from Repeated Surveys: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(2), pages 177-177, April.
- Jo Thori Lind, 2005. "Repeated surveys and the Kalman filter," Econometrics Journal, Royal Economic Society, vol. 8(3), pages 418-427, December.
- Lind, Jo Thori, 2004. "Repeated surveys and the Kalman filter," Memorandum 19/2004, Oslo University, Department of Economics.
- Pfeffermann, Danny & Tiller, Richard, 2006. "Small-Area Estimation With StateSpace Models Subject to Benchmark Constraints," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1387-1397, December.
- Pfeffermann, Danny, 1991. "Estimation and Seasonal Adjustment of Population Means Using Data from Repeated Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(2), pages 163-175, April.
- Rodríguez, Alejandro & Ruiz, Esther, 2012. "Bootstrap prediction mean squared errors of unobserved states based on the Kalman filter with estimated parameters," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 62-74, January.
- Ruiz, Esther & Rodríguez, Alejandro, 2010. "Bootstrap prediction mean squared errors of unobserved states based on the Kalman filter with estimated parameters," DES - Working Papers. Statistics and Econometrics. WS ws100301, Universidad Carlos III de Madrid. Departamento de Estadística.