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Weighted-average least squares estimation of generalized linear models

Author

Listed:
  • Giuseppe De Luca

    (University of Palermo)

  • Jan R. Magnus

    (Vrije Universiteit Amsterdam and Tinbergen Institute)

  • Franco Peracchi

    (Georgetown University and EIEF)

Abstract

The weighted-average least squares (WALS) approach, introduced by Magnus et al. (2010) in the context of Gaussian linear models, has been shown to enjoy important advantages over other strictly Bayesian and strictly frequentist model-averaging estimators when accounting for problems of uncertainty in the choice of the regressors. In this paper we extend the WALS approach to deal with uncertainty about the specification of the linear predictor in the wider class of generalized linear models (GLMs). We study the large-sample properties of the WALS estimator for GLMs under a local misspecification framework, and the finite-sample properties of this estimator by a Monte Carlo experiment the design of which is based on a real empirical analysis of attrition in the first two waves of the Survey of Health, Ageing and Retirement in Europe (SHARE).

Suggested Citation

  • Giuseppe De Luca & Jan R. Magnus & Franco Peracchi, 2017. "Weighted-average least squares estimation of generalized linear models," EIEF Working Papers Series 1711, Einaudi Institute for Economics and Finance (EIEF), revised Aug 2017.
    • Handle: RePEc:eie:wpaper:1711
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    References listed on IDEAS

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    Cited by:

    1. Berger, Michael & Pock, Markus & Reiss, Miriam & Röhrling, Gerald & Czypionka, Thomas, 2023. "Exploring the effectiveness of demand-side retail pharmaceutical expenditure reforms: cross-country evidence from weighted-average least squares estimation," LSE Research Online Documents on Economics 116928, London School of Economics and Political Science, LSE Library.
    2. De Luca, Giuseppe & Magnus, Jan R. & Peracchi, Franco, 2022. "Sampling properties of the Bayesian posterior mean with an application to WALS estimation," Journal of Econometrics, Elsevier, vol. 230(2), pages 299-317.
    3. Giuseppe De Luca & Jan R. Magnus & Franco Peracchi, 2022. "Asymptotic properties of the weighted-average least squares (WALS) estimator," EIEF Working Papers Series 2203, Einaudi Institute for Economics and Finance (EIEF), revised Mar 2022.
    4. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    5. Giuseppe Luca & Jan R. Magnus & Franco Peracchi, 2023. "Weighted-Average Least Squares (WALS): Confidence and Prediction Intervals," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1637-1664, April.
    6. Mariarosaria Comunale, 2020. "The persistently high rate of suicide in Lithuania: an updated view," Bank of Lithuania Discussion Paper Series 21, Bank of Lithuania.
    7. Gupta, Abhimanyu, 2023. "Efficient closed-form estimation of large spatial autoregressions," Journal of Econometrics, Elsevier, vol. 232(1), pages 148-167.
    8. Michael Berger & Markus Pock & Miriam Reiss & Gerald Röhrling & Thomas Czypionka, 2023. "Exploring the effectiveness of demand-side retail pharmaceutical expenditure reforms," International Journal of Health Economics and Management, Springer, vol. 23(1), pages 149-172, March.
    9. Romain Duval & Davide Furceri & Jakob Miethe, 2021. "Robust political economy correlates of major product and labor market reforms in advanced economies: Evidence from BAMLE for logit models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(1), pages 98-124, January.
    10. Wei Qian & Craig A. Rolling & Gang Cheng & Yuhong Yang, 2019. "On the Forecast Combination Puzzle," Econometrics, MDPI, vol. 7(3), pages 1-26, September.
    11. Qian, Wei & Rolling, Craig A. & Cheng, Gang & Yang, Yuhong, 2022. "Combining forecasts for universally optimal performance," International Journal of Forecasting, Elsevier, vol. 38(1), pages 193-208.
    12. Quang T. T. Nguyen & Son T. B. Nguyen & Quang V. Nguyen, 2019. "Can Higher Capital Discipline Bank Risk: Evidence from a Meta-Analysis," JRFM, MDPI, vol. 12(3), pages 1-21, August.
    13. Judith Anne Clarke, 2017. "Model Averaging OLS and 2SLS: An Application of the WALS Procedure," Econometrics Working Papers 1701, Department of Economics, University of Victoria.

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    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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