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Simulation Estimation Methods for Limited Dependent Variable Models

This chapter discusses simulation estimation methods that overcome the computational intractability of classical estimation of limited dependent variable models with flexible correlation structures in the unobservable stochastic terms. These difficulties arise because of the need to evaluate accurately very high dimensional integrals. The methods based on simulation do not require the exact evaluation of these integrals and hence are feasible using computers of even moderate power. I first discuss a series of ideas that had been used in efforts to circumvent these computational problems by employing standard numerical analysis approximation methods. I then show how simulation techniques solve the computational problems without the need to resort to either generally unsatisfactory numerical approximations. All currently known simulation algorithms are then compared in terms of theoretical properties and practical performance.

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File URL: http://cowles.econ.yale.edu/P/cd/d10a/d1007.pdf
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Paper provided by Cowles Foundation for Research in Economics, Yale University in its series Cowles Foundation Discussion Papers with number 1007.

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Length: 33 pages
Date of creation: Dec 1991
Date of revision:
Publication status: Published in G.S. Maddala, C.R. Rao and H.D. Vinod, eds., Handbook of Statistics, Vol. 11, Elsevier, 1993, pp. 519-543
Handle: RePEc:cwl:cwldpp:1007
Note: CFP 862.
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  22. repec:att:wimass:9106 is not listed on IDEAS
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