By Joachim Inkmann

Generalized approach to moments (GMM) estimation of nonlinear platforms has vital merits over traditional greatest chance (ML) estimation: GMM estimation often calls for much less restrictive distributional assumptions and is still computationally appealing while ML estimation turns into burdensome or perhaps most unlikely. This ebook offers an in-depth therapy of the conditional second method of GMM estimation of versions often encountered in utilized microeconometrics. It covers either huge pattern and small pattern homes of conditional second estimators and gives an program to empirical business association. With its finished and up to date assurance of the topic together with issues like bootstrapping and empirical chance strategies, the publication addresses scientists, graduate scholars and pros in utilized econometrics.

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Extra resources for Conditional Moment Estimation of Nonlinear Equation Systems: With an Application to an Oligopoly Model of Cooperative R&D

Example text

In this sense, the GMM estimation approach is semiparametric. The functional form of the moment functions is parametrically specified but their distribution remains unspecified with the exception of certain (conditional) moment restrictions. Therefore maximum likelihood estimation is not possible without further assumptions and the usual parametric efficiency bounds do not apply. 5). 1) for the likelihood contributions f(z 19) hold. The former condition ensures that the Cramer-Rao bound A, of the submodel can be computed from J(9 0 ), the latter condition guarantees exchangeability of integration and differentiation operators used for the derivation of J(9J as will become clear in the subsequent sections.

For a completely specified parametric estimation problem where the probability density functions underlying the likelihood function are known, the maximum likelihood estimator attains the Cramer-Rao bound given the regularity conditions. 5) and does not require a complete specification of the density function. In this sense, the GMM estimation approach is semiparametric. The functional form of the moment functions is parametrically specified but their distribution remains unspecified with the exception of certain (conditional) moment restrictions.

To continue the example given above, one suitable sequence of cutoff values corresponding to ~ ~ ~ t would decrease by c. = K· n~ with increasing n for -1 ~ ~ ~ -t and some constant K. However, this leaves an infinite choice of possible sequences of cutoff values and is therefore of limited value for practical work. This problem is confirmed by the fact that the magnitude of the (always nonnegative) GMM objective function can be arbitrarily chosen by multiplying the moment functions with any scalar constant.

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