IDEAS home Printed from https://ideas.repec.org/p/upf/upfgen/1427.html
   My bibliography  Save this paper

Policy-related small-area estimation

Author

Listed:
  • Nicholas Longford

Abstract

A method of small-area estimation with a utility function is developed. The utility characterises a policy planned to be implemented in each area, based on the area's estimate of a key quantity. It is shown by simulations that the commonly applied composite and empirical Bayes estimators are inefficient for a wide range of asymmetric utility functions. Adaptations for limited budget to implement the policy are explored. An argument is presented for a closer integration of estimation and (regional) policy making because no single small- area estimator is suitable for a wide range of purposes.

Suggested Citation

  • Nicholas Longford, 2014. "Policy-related small-area estimation," Economics Working Papers 1427, Department of Economics and Business, Universitat Pompeu Fabra.
    • Handle: RePEc:upf:upfgen:1427
    as

    Download full text from publisher

    File URL: https://econ-papers.upf.edu/papers/1427.pdf
    File Function: Whole Paper
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. N. T. Longford, 1999. "Multivariate shrinkage estimation of small area means and proportions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(2), pages 227-245.
    2. Peter Hall & Tapabrata Maiti, 2006. "On parametric bootstrap methods for small area prediction," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(2), pages 221-238, April.
    3. Garthwaite, Paul H. & Kadane, Joseph B. & O'Hagan, Anthony, 2005. "Statistical Methods for Eliciting Probability Distributions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 680-701, June.
    4. Longford, Nicholas T., 2010. "Bayesian Decision Making About Small Binomial Rates With Uncertainty About the Prior," The American Statistician, American Statistical Association, vol. 64(2), pages 164-169.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. LONGFORD Nicholas Tibor, 2011. "Policy-related small.area estimation," LISER Working Paper Series 2011-44, Luxembourg Institute of Socio-Economic Research (LISER).
    2. Nicholas Longford, 2014. "Incompatibility of estimation and policy objectives. An example from small-area estimation," Economics Working Papers 1447, Department of Economics and Business, Universitat Pompeu Fabra.
    3. Nicholas T. Longford, 2015. "Policy-Oriented Inference And The Analyst-Client Cooperation. An Example From Small-Area Statistics," Statistics in Transition New Series, Polish Statistical Association, vol. 16(1), pages 65-82, March.
    4. Nicholas T. Longford, 2015. "Policy-oriented inference and the analyst-client cooperation. An example from small-area statistics," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(1), pages 65-82, May.
    5. Longford Nicholas T., 2015. "Policy-Oriented Inference and the Analyst-Client Cooperation. An Example from Small-Area Statistics," Statistics in Transition New Series, Polish Statistical Association, vol. 16(1), pages 65-82, March.
    6. Claire Copeland & Britta Turner & Gareth Powells & Kevin Wilson, 2022. "In Search of Complementarity: Insights from an Exercise in Quantifying Qualitative Energy Futures," Energies, MDPI, vol. 15(15), pages 1-21, July.
    7. Patrick Krennmair & Timo Schmid, 2022. "Flexible domain prediction using mixed effects random forests," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1865-1894, November.
    8. repec:csb:stintr:v:17:y:2016:i:1:p:9-24 is not listed on IDEAS
    9. Erciulescu Andreea L. & Fuller Wayne A., 2016. "Small Area Prediction Under Alternative Model Specifications," Statistics in Transition New Series, Polish Statistical Association, vol. 17(1), pages 9-24, March.
    10. Miller, Joshua Benjamin & Sanjurjo, Adam, 2018. "How Experience Confirms the Gambler's Fallacy when Sample Size is Neglected," OSF Preprints m5xsk, Center for Open Science.
    11. Dai, Min & Jia, Yanwei & Kou, Steven, 2021. "The wisdom of the crowd and prediction markets," Journal of Econometrics, Elsevier, vol. 222(1), pages 561-578.
    12. A Zuashkiani & D Banjevic & A K S Jardine, 2009. "Estimating parameters of proportional hazards model based on expert knowledge and statistical data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1621-1636, December.
    13. Ibsen Chivatá Cárdenas & Saad S.H. Al‐Jibouri & Johannes I.M. Halman & Frits A. van Tol, 2014. "Modeling Risk‐Related Knowledge in Tunneling Projects," Risk Analysis, John Wiley & Sons, vol. 34(2), pages 323-339, February.
    14. Wilson, Kevin J., 2017. "An investigation of dependence in expert judgement studies with multiple experts," International Journal of Forecasting, Elsevier, vol. 33(1), pages 325-336.
    15. Meng, Xiaochun & Taylor, James W., 2022. "Comparing probabilistic forecasts of the daily minimum and maximum temperature," International Journal of Forecasting, Elsevier, vol. 38(1), pages 267-281.
    16. Constantinou Anthony Costa & Fenton Norman Elliott, 2012. "Solving the Problem of Inadequate Scoring Rules for Assessing Probabilistic Football Forecast Models," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(1), pages 1-14, March.
    17. Durbach, Ian N. & Stewart, Theodor J., 2012. "A comparison of simplified value function approaches for treating uncertainty in multi-criteria decision analysis," Omega, Elsevier, vol. 40(4), pages 456-464.
    18. Johan René van Dorp & Salvador Cruz Rambaud & José García Pérez & Rafael Herrerías Pleguezuelo, 2007. "An Elicitation Procedure for the Generalized Trapezoidal Distribution with a Uniform Central Stage," Decision Analysis, INFORMS, vol. 4(3), pages 156-166, September.
    19. Peter Hall & Tapabrata Maiti, 2008. "Non‐parametric inference for clustered binary and count data when only summary information is available," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 725-738, September.
    20. Anna Chrysafi & Vili Virkki & Mika Jalava & Vilma Sandström & Johannes Piipponen & Miina Porkka & Steven J. Lade & Kelsey Mere & Lan Wang-Erlandsson & Laura Scherer & Lauren S. Andersen & Elena Bennet, 2022. "Quantifying Earth system interactions for sustainable food production via expert elicitation," Nature Sustainability, Nature, vol. 5(10), pages 830-842, October.
    21. Helton, Jon C. & Sallaberry, Cedric J., 2009. "Conceptual basis for the definition and calculation of expected dose in performance assessments for the proposed high-level radioactive waste repository at Yucca Mountain, Nevada," Reliability Engineering and System Safety, Elsevier, vol. 94(3), pages 677-698.

    More about this item

    Keywords

    Composition; empirical Bayes; expected loss; borrowing strength; exploiting similarity; small-area estimation; utility function.;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:upf:upfgen:1427. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: http://www.econ.upf.edu/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.