Estimation with Numerical Integration on Sparse Grids
AbstractFor 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.
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Bibliographic InfoPaper provided by University of Munich, Department of Economics in its series Discussion Papers in Economics with number 916.
Date of creation: Apr 2006
Date of revision:
Estimation; Quadrature; Simulation; Mixed Logit;
Find related papers by JEL classification:
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-04-22 (All new papers)
- NEP-DCM-2006-04-22 (Discrete Choice Models)
- NEP-ECM-2006-04-22 (Econometrics)
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