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Titlebook: Computational Economics and Econometrics; Hans M. Amman,David A. Belsley,Louis F. Pau Book 1992 Kluwer Academic Publishers 1992 Simulation

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Global Optimization of Statistical Functions: Preliminary Resultsthe algorithm converges, there is no assurance that it will have converged to a global, rather than a local optimum since conventional algorithms cannot distinguish between them. In sum, there is a poor match between the power of these methods and the algorithms used to implement them.
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https://doi.org/10.1007/978-3-8350-5521-6the algorithm converges, there is no assurance that it will have converged to a global, rather than a local optimum since conventional algorithms cannot distinguish between them. In sum, there is a poor match between the power of these methods and the algorithms used to implement them.
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https://doi.org/10.1007/978-3-8350-5521-6r of the square of the number of rows/columns; typically, in the hundreds of thousands to millions). In the class cal setting, the row and column totals are known and fixed, and the individual entries nonnegative. However, in certain applications, the row and column totals need not be known a priori
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Likelihood Evaluation for Dynamic Latent Variables Modelsactorizations of the sequential joint density of the observables and latent variables. The feasibility of the proposed technique is demonstrated by means of a pilot application to a one-parameter disequilibrium model. Extensions to models with weakly exogenous variables and the use of acceleration m
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