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Maximum Likelihood with Estimating Equations

  • Grendar, Marian


    (Department of mathematics, FPV UMB, Tajovského 40, 974 01 Banská Bystrica, Slovakia. Institute of Mathematics and CS, Slovak Academy of Sciences (SAS) and UMB, Banská Bystrica. Institute of Measurement Sciences SAS, Bratislava, Slovakia.)

  • Judge, George G.


    (University of California, Berkeley. Dept of agricultural and resource economics)

Methods, like Maximum Empirical Likelihood (MEL), that operate within the Empirical Estimating Equations (E3) approach to estimation and inference are challenged by the Empty Set Problem (ESP). We propose to return from E3 back to the Estimating Equations, and to use the Maximum Likelihood method. In the discrete case the Maximum Likelihood with Estimating Equations (MLEE) method avoids ESP. In the continuous case, how to make ML-EE operational is an open question. Instead of it, we propose a Patched Empirical Likelihood, and demonstrate that it avoids ESP. The methods enjoy, in general, the same asymptotic properties as MEL.

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Paper provided by University of California at Berkeley, Department of Agricultural and Resource Economics and Policy in its series CUDARE Working Paper Series with number 1094.

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Length: 9 pages
Date of creation: Jan 2010
Handle: RePEc:are:cudare:1094
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