Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/104186
Authors: 
Heiss, Florian
Winschel, Viktor
Year of Publication: 
2006
Series/Report no.: 
Munich Discussion Paper 2006-15
Abstract: 
For the estimation of many econometric models, integrals without analytical solutions have to be evaluated. Examples include limited dependent variables and nonlinear panel data models. In the case of one-dimensional integrals, Gaussian quadrature is known to work efficiently for a large class of problems. In higher dimensions, similar approaches discussed in the literature are either very specific and hard to implement or suffer from exponentially rising computational costs in the number of dimensions - a problem known as the "curse of dimensionality" of numerical integration. We propose a strategy that shares the advantages of Gaussian quadrature methods, is very general and easily implemented, and does not suffer from the curse of dimensionality. Monte Carlo experiments for the random parameters logit model indicate the superior performance of the proposed method over simulation techniques.
Subjects: 
Estimation
Quadrature
Simulation
Mixed Logit
JEL: 
C15
C25
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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