Triple-goal estimation of unemployment rates for U.S. states using the U.S. Current Population Survey data

Sign In

  1. Home
  2. Publications
  3. Statistics in Transition New Series
  4. Publication Articles
  5. Article

Publications

Share / Export Citation / Email / Print / Text size:

Statistics in Transition New Series

Polish Statistical Association

Central Statistical Office of Poland

Subject: Economics, Statistics & Probability

GET ALERTS

ISSN: 1234-7655
eISSN: 2450-0291

DESCRIPTION

Article Metrics
21
Reader(s)
36
Visit(s)
0
Comment(s)
0
Share(s)

SEARCH WITHIN CONTENT

FIND ARTICLE

Volume / Issue / page

Archive
Volume 22 (2021)
Volume 21 (2020)
Volume 20 (2019)
Volume 19 (2018)
Volume 18 (2017)
Volume 17 (2016)
Volume 16 (2015)
Related articles

VOLUME 16 , ISSUE 4 (December 2015) > List of articles

Triple-goal estimation of unemployment rates for U.S. states using the U.S. Current Population Survey data

Daniel Bonnéry * / Yang Cheng * / Neung Soo Ha * / Partha Lahiri *

Keywords : complex survey data, empirical distribution function, Monte Carlo Markov Chain, rank, risk, small area estimation,

Citation Information : Statistics in Transition New Series. Volume 16, Issue 4, Pages 511-522,

License : (CC BY 4.0)

Published Online: 01-November-2017

  • ARTICLE
  • FIGURES & TABLES
  • REFERENCES
  • EXTRA FILES
  • COMMENTS

ARTICLE

ABSTRACT

In this paper, we first develop a triple-goal small area estimation methodology for simultaneous estimation of unemployment rates for U.S. states using the Current Population Survey (CPS) data and a two-level random sampling variance normal model. The main goal of this paper is to illustrate the utility of the triple-goal methodology in generating a single series of unemployment rate estimates for three separate purposes: developing estimates for individual small area means, producing empirical distribution function (EDF) of true small area means, and the ranking of the small areas by true small area means. We achieve our goal using a Monte Carlo simulation experiment and a real data analysis.

PDF Share

Content not available PDF Share

FIGURES & TABLES

REFERENCES

  1. CONLON, E. M. and LOUIS, T. A. (1999). Addressing Multiple Goals in Evaluating Region-specific Risk Using Bayesian Methods. In Disease Mapping and Risk Assessment for Public Health, Chichester: Wiley, 31–47.
  2. DEVINE, O. J. and LOUIS, T. A. (1994). A Constrained Empirical Bayes Estimator for Incidence Rates in Areas with Small Populations. Statistics in Medicine, 13, 1119–1133.
  3. EFRON, B. and MORRIS, C. (1975). Data Analysis Using Stein’s Estimator and Its Generalizations. Journal of the American Statistical Association, 70, 311– 319.
  4. GELMAN, A. and PRICE, P. N. (1999). All Maps of Parameter Estimates are Misleading. Statistics in Medicine, 18, 3221–3234.
  5. GHOSH, M. (1992). Constrained Bayes Estimation with Applications. Journal of the American Statistical Association, 87, 533–540.
  6. GOLDSTEIN, H. and SPIEGELHALTER, D. J. (1996). League Tables and Their Limitations: Statistical Issues in Comparisons of Institutional Performance. Journal of Royal Statistical Society, Series A., 159, 385–409.
  7. GURNEY, M. and DALY, J. F. (1965). A Multivariate Approach to the Estimation in Periodic Sample Surveys. In Proceedings of the Social Statistics Section, ASA., 242–257.
  8. HA, N. S., LAHIRI, P., and PARSONS, P. (2014). Methods and Results for Small Area Estimation Using Smoking Data from The 2008 National Health Interview Survey. Statistics in Medicine, 33, 3932–3945.
  9. JIANG, J. and LAHIRI, P. (2006). Mixed Model Prediction and Small Area Estimation. Test, 15, 111–999.
  10. LAHIRI, P. (1990). Adjusted Bayes and Empirical Bayes Estimation in Population Sampling. Sankhya, 52, 50–66.
  11. LAIRD, N. M. and LOUIS, T. A. (1989). Empirical Bayes Ranking Methods. Journal of Educational Statistics, 14, 29–46.
  12. LANDRUM, M. B., BRONSKILL, S. E., and NORMAND, S. (2000). Analytic Methods for Constructing Cross-Sectional Profiles of Health Care Providers. Health Services Outcomes Research Methodology, 1, 23–47.
  13. LENT, J., MILLER, S., CANTWELL, P., and DUFF, M. (1999). Effects of composite weights Current Population Survey. Journal of Official Statistics, 15, 431–448.
  14. LIU, B., LAHIRI, P., and KALTON, G. (2014). Hierarchical Bayes Modeling of Survey-Weighted Small Area Proportions. Survey Methodology, 40, 1–13.
  15. LOUIS, T. (1984). Estimating a Population of Parameter Values Using Bayes and Empirical Bayes Methods. Journal of the American Statistical Association,
    79, 393–398.
  16. PFEFFERMANN, D. (2013). New Important Developments in Small Area Estimation. Statistical Science, 28, 1, 40–68
  17. PFEFFERMANN, D. and TILLER, R. B. (2006). Small Area Estimation With State-Space Models Subject to Benchmark Constraints. Journal of the American
    Statistical Association, 101, 1387–1397.
  18. RAO, J. N. K. (2003). Small Area Estimation. Wiley Series in Survey Methodology, Hoboken: NJ.
  19. SHEN,W. and LOUIS, T. (1998). Triple-Goal Estimates in Two-Stage Hierarchical Models. Journal of Royal Statistical Society, Series B., 60, 455–471.

EXTRA FILES

COMMENTS