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Empirical Bayes Regression With Many Regressors

Author

Listed:
  • Thomas Knox

    (University of Chicago)

  • James H. Stock

    (Harvard University)

  • Mark W. Watson

    (Princeton University)

Abstract

We consider frequentist and empirical Bayes estimation of linear regression coefficients with T observations and K orthonormal regressors. The frequentist formulation considers estimators that are equivariant under permutations of the regressors. The empirical Bayes formulation (both parametric and nonparametric) treats the coefficients as i.i.d. and estimates their prior. Asymptotically; when K =Ï Î¤Î´ for 0

Suggested Citation

  • Thomas Knox & James H. Stock & Mark W. Watson, 2004. "Empirical Bayes Regression With Many Regressors," Working Papers 2004-1, Princeton University. Economics Department..
    • Handle: RePEc:pri:econom:2004-1
    as

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    File URL: http://www.princeton.edu/~mwatson/papers/knox_stock_watson_empb_AS_1.pdf
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    References listed on IDEAS

    as
    1. Davidson, James, 1994. "Stochastic Limit Theory: An Introduction for Econometricians," OUP Catalogue, Oxford University Press, number 9780198774037.
    2. HÄRDLE, Wolfgang & HART, Jeffrey & MARRON, Steve & TSYBAKOV, Alexander, 1992. "Bandwith choice for average derivative estimation," LIDAM Reprints CORE 977, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. S. J. Koopman & G. Mesters, 2017. "Empirical Bayes Methods for Dynamic Factor Models," The Review of Economics and Statistics, MIT Press, vol. 99(3), pages 486-498, July.

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    More about this item

    Keywords

    Large model regression; equivariant estimation; minimax estimation; shrinkage estimation;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General

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