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Smooth Minimum Distance Estimation and Testing in Conditional Moment Restrictions Models: Uniform in Bandwidth Theory

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Author Info
Pascal Lavergne () (Simon Fraser University)
Valentin Patilea (IRMAR-INSA)

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Abstract

We 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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File URL: http://www.econ.sfu.ca/research/RePEc/sfu/sfudps/dp08-06.pdf
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Publisher Info
Paper provided by Department of Economics, Simon Fraser University in its series Discussion Papers with number dp08-08.

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Length: 46
Date of creation: Nov 2008
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Handle: RePEc:sfu:sfudps:dp08-08

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Postal: Department of Economics, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada
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Related research
Keywords: Conditional Moments; Smoothing Methods;

Find related papers by JEL classification:
C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods

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