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Large Deviations Theory and Empirical Estimator Choice

  • Grendar, Marian
  • Judge, George G.
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    Criterion choice is such a hard problem in information recovery and in estimation and inference. In the case of inverse problems with noise, can probabilistic laws provide a basis for empirical estimator choice? That is the problem we investigate in this paper. Large Deviations Theory is used to evaluate the choice of estimator in the case of two fundamental situations-problems in modelling data. The probabilistic laws developed demonstrate that each problem has a unique solution-empirical estimator. Whether other members of the empirical estimator family can be associated a particular problem and conditional limit theorem, is an open question.

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    Paper provided by Department of Agricultural & Resource Economics, UC Berkeley in its series Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series with number qt20n3j23r.

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    Date of creation: 01 Jan 2006
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    Handle: RePEc:cdl:agrebk:qt20n3j23r
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    1. Whitney K. Newey & Richard J. Smith, 2004. "Higher Order Properties of Gmm and Generalized Empirical Likelihood Estimators," Econometrica, Econometric Society, vol. 72(1), pages 219-255, 01.
    2. Guido W Imbens, Phillip Johnson & Richard H Spady, . "Information theoretic approaches to inference in moment condition model," Economics Papers W12., Economics Group, Nuffield College, University of Oxford.
    3. Yuichi Kitamura, 2001. "Asymptotic Optimality of Empirical Likelihood for Testing Moment Restrictions," Econometrica, Econometric Society, vol. 69(6), pages 1661-1672, November.
    4. Judge, George G. & Mittelhammer, Ron C, 2003. "A Semi-Parametric Basis for Combining Estimation Problems Under Quadratic Loss," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt8z25j0w3, Department of Agricultural & Resource Economics, UC Berkeley.
    5. Mittelhammer, Ronald C. & Judge, George G. & Schoenberg, Ron, 2003. "Empirical evidence concerning the finite sample performance of El-type structural equation estimation and inference methods," CUDARE Working Paper Series 945, University of California at Berkeley, Department of Agricultural and Resource Economics and Policy.
    6. repec:cup:cbooks:9780521623940 is not listed on IDEAS
    7. Marian Grendar Jr & Marian Grendar, 2003. "Maximum Probability/Entropy translating of contiguous categorical observations into frequencies," Econometrics 0309003, EconWPA.
    8. Francesco Bravo, . "Bartlett-type Adjustments for Empirical Discrepancy Test Statistics," Discussion Papers 04/14, Department of Economics, University of York.
    9. Yuichi Kitamura & Michael Stutzer, 1997. "An Information-Theoretic Alternative to Generalized Method of Moments Estimation," Econometrica, Econometric Society, vol. 65(4), pages 861-874, July.
    10. Imbens, G.W. & Johnson, P. & Spady, R.H., 1995. "Information Theoretic Approaches to Inference in Movement Condition Models," Economics Papers 99, Economics Group, Nuffield College, University of Oxford.
    11. Kitamura, Yuichi & Stutzer, Michael, 2002. "Connections between entropic and linear projections in asset pricing estimation," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 159-174, March.
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