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- W1526043405 abstract "An important class of structural econometric models (nonlinear rational expectations, option pricing, auction models, ...) characterize observable variables as highly nonlinear transforma- tions of some latent variables. These transformations are one-to-one, but they depend on the unknown distribution of the latent variables through the equilibrium of the game and/or the learning process. Therefore numerical complexity of the equilibrium definition generates sub- stantial obstacles for the direct implementation of maximum likelihood inference. This is a particular case of argmax estimators based on a untractable sample based criterion Q[exp.T][theta,lambda(theta)] contaminated by the occurences of [theta] in a ‘nuisance function’ [lambda(theta)] to which corresponds a simple criterion Q[exp.T][theta,lambda(theta.exp0)] with [theta.exp.0] the true, unknown value of the parameter. The natural idea is to replace the unknown value [lambda(theta.exp0)] by some ’good proxy’, say [lambda(theta.exp1)], to maximize Q[exp.T][theta,lambda(theta.exp1)] with respect to [theta] and to get a new, updated estimate which, in turn, can be used for approxi- mating [lambda(theta.exp0)],... Such steps can be considered for a fixed sample size (iterative M-estimators), or each time new data arrive (recursive estimators). In this paper we present and analyze several iterative and recursive (Robbins-Monro type) estimation procedures of this kind which, at the end, are applied to the class of structural econometric models which motivated this work." @default.
- W1526043405 created "2016-06-24" @default.
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- W1526043405 date "1997-10-01" @default.
- W1526043405 modified "2023-10-11" @default.
- W1526043405 title "Continuously updated extremum estimators" @default.
- W1526043405 hasPublicationYear "1997" @default.
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