Classical Estimation Methods for LDV Models Using Simulation

Vassilis Hajivassiliou and Paul Ruud

Econometrics from University Library of Munich, Germany

Abstract: This paper discusses estimation methods for limited dependent variable (LDV) models that employ Monte Carlo simulation techniques to overcome computational problems in such models. These difficulties take the form of high dimensional integrals that need to be calculated repeatedly but cannot be easily approximated by series expansions. In the past, investigators were forced to restrict attention to special classes of LDV models that are computationally manageable. The simulation estimation methods we discuss here make it possible to estimate LDV models that are computationally intractable using classical estimation methods. We first review the ways in which LDV models arise, describing the differences and similarities in censored and truncated data generating processes. Censoring and truncation give rise to the troublesome multivariate integrals. Following the LDV models, we described variables simulation methods for evaluating such integrals. Naturally, censoring and truncation play roles in simulation as well. Finally, estimation methods that rely on simulation are described. We review three general approaches that combine estimation of LDV models and simulation: simulation of the log-likelihood function (MSL), simulation of moment functions (MSM), and simulation of the score (MSS).

JEL-codes: C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Pages: 47 pages
Date: 1993-11-23
Note: 47 pages
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Related works:
Working Paper: Classical Estimation Methods for LDV Models Using Simulation (1993)
Working Paper: Classical Estimation Methods for LDV Models Using Simulation (1993)
Chapter: Classical estimation methods for LDV models using simulation (1986)
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Persistent link: https://EconPapers.repec.org/RePEc:wpa:wuwpem:9311002

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