A Monte Carlo EM Algorithm for the Estimation of a Logistic Auto-logistic Model with Missing Data

Marco Bee and Giuseppe Espa ()

No 801, Department of Economics Working Papers from Department of Economics, University of Trento, Italia

Abstract: This paper proposes an algorithm for the estimation of the parameters of a Logistic Auto-logistic Model when some values of the target variable are missing at random but the auxiliary information is known for the same areas. First, we derive a Monte Carlo EM algorithm in the setup of maximum pseudo-likelihood estimation; given the analytical intractability of the conditional expectation of the complete pseudo-likelihood function, we implement the E-step by means of Monte Carlo simulation. Second, we give an example using a simulated dataset. Finally, a comparison with the standard non-missing data case shows that the algorithm gives consistent results.

Keywords: Spatial Missing Data; Monte Carlo EM Algorithm; Logistic Auto-logistic Model; Pseudo-Likelihood. (search for similar items in EconPapers)
JEL-codes: C13 C15 C51 (search for similar items in EconPapers)
Date: 2008
New Economics Papers: this item is included in nep-ecm and nep-ore
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Citations: View citations in EconPapers (2)

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Journal Article: A Monte Carlo EM algorithm for the estimation of a logistic auto-logistic model with missing data (2008)
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