Estimation with Numerical Integration on Sparse Grids

Heiss, Florian and Winschel, Viktor (April 2006): Estimation with Numerical Integration on Sparse Grids. Discussion Papers in Economics 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.

• Item Type: Paper
• Keywords: Estimation, Quadrature, Simulation, Mixed Logit
• Faculties: Economics; Economics > Munich Discussion Papers in Economics; Economics > Munich Discussion Papers in Economics > Statistical Methods
• Subjects: 300 Social sciences > 300 Social sciences, sociology and anthropology; 300 Social sciences > 330 Economics
• JEL Classification: C15, C25
• Language: English