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That variant is the method of simulated moments. Now, if (1) is correct the sample moment, T gT({3) =L g(Xt, {3) / T t=1 should be close to zero when evaluated at {3 estimate {3 by choosing to minimise /3 = /3. It is therefore reasonable to On the Accuracy and Efficiency of GMM Estimators 25 Jr«(3) = gT«(3)' W T gT (;3) , where WT is a positive definitive weighting matrix. Setting WT = lor n- I , where Euu' = n, gives the OLS or GLS regression based GMM estimators, and setting W T = N(N' N)-I N ' , gives the instrumental variable version with instruments N.

Successful computation permits a rigorous interpretation of the economic time series data. 3. A BOOTSTRAP ESTIMATE The parameters of the model described in the previous section are estimated using a bootstrapping procedure and Bayes' Theorem. The prior density is an indicator function that is diffuse when the boundary conditions hold and 0 otherwise. The Bayesian bootstrap procedure permits valid inference on all of the parameters and response coefficients. The most convenient way to explain how the bootstrap procedure is applied here is to examine the four fundamental components of the model.

But we could also pick {J to make the fitted Ut distribution yield the same characteristics as we actually observe in the data on Yt, given Xt. e. we choose by solving /3. /3 (3) where the value of r defines the norm and W the weighting function. ) represents the fitted moments given Xt and the choice of {J. In many cases we do not have an analytic maintained hypothesis, so the fitted moments j(-) have to be constructed by numerical simulation with pseudo-data replicated many times through the model to generate numerical evaluations of those moments.

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A Study of Transmit and Receive Antenna Diversity Techniques for Communication Systems [thesis] by Q. Yan

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