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A Comparative Study of Different Shrinkage Estimators for Panel Data Models

Author

Listed:
  • G. S. Maddala

    (Department of Economics, Ohio State University)

  • Hongyi Li

    (Department of Decision Sciences and Managerial Economics, Chinese University of Hong Kong)

  • V. K. Srivastava

    (Lucknow University)

Abstract

The present paper uses small-sigma asymptotics to show that in general the shrinkage estimators have superior properties among the individual least squares estimators, the simple average estimators, the weighted average estimators, estimators obtained by shrinking towards the simple average, and estimators obtained by shrinking towards the weighted average. The shrinkage estimators are used to derive short-run and long-run price and income elasticities for residential natural gas demand and electricity demand in the US based on panel data covering 49 states over 21 years (1970-90). They are also used for out of sample forecasting.

Suggested Citation

  • G. S. Maddala & Hongyi Li & V. K. Srivastava, 2001. "A Comparative Study of Different Shrinkage Estimators for Panel Data Models," Annals of Economics and Finance, Society for AEF, vol. 2(1), pages 1-30, May.
    • Handle: RePEc:cuf:journl:y:2001:v:2:i:1:p:1-30
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    Citations

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    Cited by:

    1. Mhlanga David & Denhere Varaidzo, 2020. "Determinants of Financial Inclusion in Southern Africa," Studia Universitatis Babeș-Bolyai Oeconomica, Sciendo, vol. 65(3), pages 39-52, December.
    2. Ali Mehrabani & Aman Ullah, 2020. "Improved Average Estimation in Seemingly Unrelated Regressions," Econometrics, MDPI, vol. 8(2), pages 1-22, April.
    3. Simona Bigerna & Maria Chiara D’Errico & Paolo Polinori, 2022. "Sustainable Power Generation in Europe: A Panel Data Analysis of the Effects of Market and Environmental Regulations," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 83(2), pages 445-479, October.
    4. Herbert Brücker & Boriss Siliverstovs, 2006. "On the estimation and forecasting of international migration: how relevant is heterogeneity across countries?," Empirical Economics, Springer, vol. 31(3), pages 735-754, September.
    5. Ali Mehrabani & Aman Ullah, 2022. "Weighted Average Estimation in Panel Data," Working Papers 202209, University of California at Riverside, Department of Economics, revised Apr 2022.
    6. Abdoulganiour Almame Tinta & Idrissa Mohamed Ouédraogo & Ramatu Mahama Al‐Hassan, 2022. "The micro determinants of financial inclusion and financial resilience in Africa," African Development Review, African Development Bank, vol. 34(2), pages 293-306, June.
    7. Bigerna, Simona & D'Errico, Maria Chiara & Polinori, Paolo, 2020. "Heterogeneous impacts of regulatory policy stringency on the EU electricity Industry:A Bayesian shrinkage dynamic analysis," Energy Policy, Elsevier, vol. 142(C).
    8. Shahnaz Parsaeian, 2023. "Structural Breaks in Seemingly Unrelated Regression Models," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202308, University of Kansas, Department of Economics.
    9. Slavutskaya, Anna, 2013. "Short-term hedge fund performance," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4404-4431.
    10. R. McAfee & Philip Reny, 2007. "The role of excess capacity in determining market power in natural gas transportation markets," Journal of Regulatory Economics, Springer, vol. 32(3), pages 209-223, December.

    More about this item

    Keywords

    Bayesian shrinkage estimator; Parameter heterogeneity; Stein-rule estimator;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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