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Comments on: “Single and two-stage cross-sectional and time series benchmarking procedures for small area estimation”

Author

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  • Rebecca Steorts
  • M. Ugarte

Abstract

We congratulate the authors for a stimulating and valuable manuscript, providing a careful review of the state-of-the-art in cross-sectional and time-series benchmarking procedures for small area estimation. They develop a novel two-stage benchmarking method for hierarchical time series models, where they evaluate their procedure by estimating monthly total unemployment using data from the US Census Bureau. We discuss three topics: linearity and model misspecification, computational complexity and model comparisons, and, some aspects on small area estimation in practice. More specifically, we pose the following questions to the authors, that they may wish to answer: How robust is their model to misspecification? Is it time to perhaps move away from linear models of the type considered by Fay and Herriot (J Am Stat Assoc 74:269–277, 1979 ), Battese et al. (J Am Stat Assoc 83:28–36, 1988 )? What is the asymptotic computational complexity and what comparisons can be made to other models? Should the benchmarking constraints be inherently fixed or should they be random?. Copyright Sociedad de Estadística e Investigación Operativa 2014

Suggested Citation

  • Rebecca Steorts & M. Ugarte, 2014. "Comments on: “Single and two-stage cross-sectional and time series benchmarking procedures for small area estimation”," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(4), pages 680-685, December.
    • Handle: RePEc:spr:testjl:v:23:y:2014:i:4:p:680-685
    DOI: 10.1007/s11749-014-0386-2
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    References listed on IDEAS

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    1. G. Datta & M. Ghosh & R. Steorts & J. Maples, 2011. "Bayesian benchmarking with applications to small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(3), pages 574-588, November.
    2. Militino, A.F. & Goicoa, T. & Ugarte, M.D., 2012. "Estimating the percentage of food expenditure in small areas using bias-corrected P-spline based estimators," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2934-2948.
    3. W. R. Bell & G. S. Datta & M. Ghosh, 2013. "Benchmarking small area estimators," Biometrika, Biometrika Trust, vol. 100(1), pages 189-202.
    4. Danny Pfeffermann & Anna Sikov & Richard Tiller, 2014. "Single- and two-stage cross-sectional and time series benchmarking procedures for small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(4), pages 631-666, December.
    5. Ugarte, M.D. & Goicoa, T. & Militino, A.F. & Durbán, M., 2009. "Spline smoothing in small area trend estimation and forecasting," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3616-3629, August.
    6. Danny Pfeffermann & Anna Sikov & Richard Tiller, 2014. "Rejoinder on: Single- and two-stage cross-sectional and time series benchmarking procedures for small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(4), pages 686-690, December.
    7. Malay Ghosh & Rebecca Steorts, 2013. "Two-stage benchmarking as applied to small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(4), pages 670-687, November.
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