 Estimation with Numerical Integration on Sparse GridsFlorian Heiss and Viktor Winschel Discussion Papers in Economics from University of Munich, Department of Economics 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. Keywords: Estimation; Quadrature; Simulation; Mixed Logit (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:lmu:muenec:916 Access Statistics for this paper More papers in Discussion Papers in Economics from University of Munich, Department of Economics Ludwigstr. 28, 80539 Munich, Germany. Contact information at EDIRC. |
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