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Apr 9

GMM and Empirical Likelihood

Posted by abiao at 13:40 | Code » Matlab | Comments(0) | Reads(8027)
Generalized method of moments (GMM) estimation has got more and more popularity for linear and non-linear models with applications in economics and finance. GMM estimation was formalized by Hansen (1982), and since has become one of the most widely used methods of estimation for models in economics and finance. Unlike maximum likelihood estimation (MLE), GMM does not require complete knowledge of the distribution of the data. Only specified moments derived from an underlying model are needed for GMM estimator. In some cases in which the distribution of the data is known, MLE can be computationally very burdensome whereas GMM can be computationally very easy. The log-normal stochastic volatility model is one example. In models for which there are more moment conditions than model parameters, GMM estimation provides a straightforward way to test the specification of the proposed model. This is an important feature that is unique to GMM estimator.

Download Programs for GMM and Empirical Likelihood at http://www.ssc.wisc.edu/~bhansen/progs/progs_gmm.html

Finally, Happy easter day to all of you, while I will have to stay at home preparing interviewsweat

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