Continuous updating gmm estimator

The GMM estimators are known to be consistent, asymptotically normal, and efficient in the class of all estimators that do not use any extra information aside from that contained in the moment conditions.GMM was developed by Lars Peter Hansen in 1982 as a generalization of the method of moments, (norm of m, denoted as

In econometrics and statistics, the generalized method of moments (GMM) is a generic method for estimating parameters in statistical models.The interpretation gives some insight into why there is less bias associated with this estimator.k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining.The subjects include time series data in macroeconomics and clustered data in applied microeconomics.Besides the GMM context, I also apply the idea of new asymptotics to other popular econometric models such as triangular cointegration regression and long-horizon predictive regression.These moment conditions are functions of the model parameters and the data, such that their expectation is zero at the true values of the parameters.


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