Information Theoretic Alternatives To Traditional Simultaneous Equations Estimators In The Presence Of Heteroskedasticity
AbstractFinite sampling properties of information theoretic estimators of the simultaneous equations model, including maximum empirical likelihood, maximum empirical exponential likelihood, and maximum log Euclidean likelihood, are examined in the presence of selected forms of heteroskedasticity. Extensive Monte Carlo experiments are used to compare finite sample performance of Wald, Likelihood ratio, and Lagrangian multiplier tests constructed from information theoretic estimators to those from traditional generalized method of moments.
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Bibliographic InfoPaper provided by American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association) in its series 2002 Annual meeting, July 28-31, Long Beach, CA with number 19831.
Date of creation: 2002
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- Guido W. Imbens & Phillip Johnson & Richard H. Spady, 1995.
"Information Theoretic Approaches to Inference in Moment Condition Models,"
NBER Technical Working Papers
0186, National Bureau of Economic Research, Inc.
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- Guido W. Imbens & Phillip Johnson & Richard H. Spady, 1995. "Information Theoretic Approaches to Inference in Moment Condition Models," Harvard Institute of Economic Research Working Papers 1736, Harvard - Institute of Economic Research.
- Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers 1488, Iowa State University, Department of Economics.
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