Estimation of Tobit Type Censored Demand Systems: A Comparison of Estimators

Mikkel Barslund

No 07-16, Discussion Papers from University of Copenhagen. Department of Economics

Abstract: Recently a number of authors have suggested to estimate censored demand systems as a system of Tobit multivariate equations employing a Quasi Maximum Likelihood (QML) estimator based on bivariate Tobit models. In this paper I study the efficiency of this QML estimator relative to the asymptotically more efficient Simulated ML (SML) estimator in the context of a censored Almost Ideal demand system. Further, a simpler QML estimator based on the sum of univariate Tobit models is introduced. A Monte Carlo simulation comparing the three estimators is performed on three different sample sizes. The QML estimators perform well in the presence of moderate sized error correlation coefficients often found in empirical studies. With absolute larger correlation coefficients, the SML estimator is found to be superior. The paper lends support to the general use of the QML estimators and points towards the use of simple etimators for more general censored systems of equations.

Keywords: censored demand system; Monte Carlo; quasi maximum likelihood; simulated maximum likelihood (search for similar items in EconPapers)
JEL-codes: C15 C34 D12 (search for similar items in EconPapers)
Pages: 14 pages
Date: 2007-08
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (11)

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