Smooth Minimum Distance Estimation and Testing in Conditional Moment Restrictions Models: Uniform in Bandwidth Theory
AbstractWe propose a new estimation method for models defined by conditional moment restrictions,that minimizes a distance criterion based on kernel smoothing. Whether the bandwidth parameter is fixed or decreases to zero with the sample size, our approach defines a whole class of estimators. We develop a theory that focuses on uniformity in bandwidth. We establish a pn-asymptotic representation of our estimator as a process depending on the bandwidth within a wide range including fixed bandwidths and that applies to misspecified models. We also study an efficient version of our estimator. We develop inference procedures based on a distance metric statistic for testing restrictions on parameters and we propose a new bootstrap technique. Our new methods apply to non-smooth problems, are simple to implement, and perform well in small samples.
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Bibliographic InfoPaper provided by Department of Economics, Simon Fraser University in its series Discussion Papers with number dp08-08.
Date of creation: Nov 2008
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Postal: Working Paper Coordinator, Department of Economics, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada
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- Manuel Dominguez & Ignacio Lobato, 2010. "Consistent Inference in Models Defined by COnditional Moment Restrictions: an Alternative to GMM," Working Papers 1005, Centro de Investigacion Economica, ITAM.
- Rachidi Kotchoni, 2013.
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- Crudu, Federico & Sándor, Zsolt, 2011. "On the finite-sample properties of conditional empirical likelihood estimators," MPRA Paper 34116, University Library of Munich, Germany.
- Marine Carrasco & Rachidi Kotchoni, 2013.
"Efficient Estimation Using the Characteristic Function,"
- Marine Carrasco & Rachidi Kotchoni, 2013. "Efficient estimation using the Characteristic Function," CIRANO Working Papers 2013s-22, CIRANO.
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