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Weighted average least squares estimation with nonspherical disturbances and an application to the Hong Kong housing market

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  • Magnus, Jan R.
  • Wan, Alan T.K.
  • Zhang, Xinyu

Abstract

The recently proposed 'weighted average least squares' (WALS) estimator is a Bayesian combination of frequentist estimators. It has been shown that the WALS estimator possesses major advantages over standard Bayesian model averaging (BMA) estimators: the WALS estimator has bounded risk, allows a coherent treatment of ignorance and its computational effort is negligible. However, the sampling properties of the WALS estimator as compared to BMA estimators are heretofore unexamined. The WALS theory is further extended to allow for nonspherical disturbances, and the estimator is illustrated with data from the Hong Kong real estate market. Monte Carlo evidence shows that the WALS estimator performs significantly better than standard BMA and pretest alternatives.

Suggested Citation

  • Magnus, Jan R. & Wan, Alan T.K. & Zhang, Xinyu, 2011. "Weighted average least squares estimation with nonspherical disturbances and an application to the Hong Kong housing market," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1331-1341, March.
  • Handle: RePEc:eee:csdana:v:55:y:2011:i:3:p:1331-1341
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    Citations

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    Cited by:

    1. Magnus, J.R. & Wang, W. & Zhang, Xinyu, 2012. "WALS Prediction," Discussion Paper 2012-043, Tilburg University, Center for Economic Research.
    2. Schomaker, Michael & Heumann, Christian, 2014. "Model selection and model averaging after multiple imputation," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 758-770.
    3. Poghosyan, K., 2012. "Structural and reduced-form modeling and forecasting with application to Armenia," Other publications TiSEM ad1a24c3-15e6-4f04-b338-3, Tilburg University, School of Economics and Management.
    4. Aman Ullah & Alan T.K. Wan & Huansha Wang & Xinyu Zhang & Guohua Zou, 2014. "A Semiparametric Generalized Ridge Estimator and Link with Model Averaging," Working Papers 201412, University of California at Riverside, Department of Economics.
    5. Xinyu Zhang & Alan T. K. Wan & Sherry Z. Zhou, 2011. "Focused Information Criteria, Model Selection, and Model Averaging in a Tobit Model With a Nonzero Threshold," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 132-142, June.
    6. Karen Poghosyan & Jan R. Magnus, 2012. "WALS Estimation and Forecasting in Factor-based Dynamic Models with an Application to Armenia," International Econometric Review (IER), Econometric Research Association, vol. 4(1), pages 40-58, April.
    7. Valentino Dardanoni & Giuseppe De Luca & Salvatore Modica & Franco Peracchi, 2012. "A generalized missing-indicator approach to regression with imputed covariates," Stata Journal, StataCorp LP, vol. 12(4), pages 575-604, December.
    8. António Afonso & Florence Huart & João Tovar Jalles & Piotr Stanek, 2018. "Twin Deficits Revisited: a role for fiscal institutions?," Working Papers REM 2018/31, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    9. Zhao, Shangwei & Zhou, Jianhong & Li, Hongjun, 2016. "Model averaging with high-dimensional dependent data," Economics Letters, Elsevier, vol. 148(C), pages 68-71.
    10. Shangwei Zhao & Aman Ullah & Xinyu Zhang, 2018. "A Class of Model Averaging Estimators," Working Paper series 18-11, Rimini Centre for Economic Analysis.
    11. Sufrauj, Shamnaaz & Schiavo, Stefano & Riccaboni, Massimo, 2014. "The Structure and Growth of World Trade, and the Role of Europe in the Global Economy," MPRA Paper 54122, University Library of Munich, Germany.
    12. Giuseppe De Luca & Jan R. Magnus, 2011. "Bayesian model averaging and weighted-average least squares: Equivariance, stability, and numerical issues," Stata Journal, StataCorp LP, vol. 11(4), pages 518-544, December.
    13. repec:eee:ecolet:v:162:y:2018:i:c:p:101-106 is not listed on IDEAS
    14. Steel, Mark F. J., 2017. "Model Averaging and its Use in Economics," MPRA Paper 81568, University Library of Munich, Germany.
    15. Christopher F. Parmeter & Alan T. K. Wan & Xinyu Zhang, 2016. "Model Averaging Estimators for the Stochastic Frontier Model," Working Papers 2016-09, University of Miami, Department of Economics.
    16. Aedın Doris & Donal O’Neill & Olive Sweetman, 2011. "GMM estimation of the covariance structure of longitudinal data on earnings," Stata Journal, StataCorp LP, vol. 11(3), pages 439-459, September.
    17. 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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