The New Palgrave Dictionary of Economics

2018 Edition
| Editors: Macmillan Publishers Ltd

Sieve Extremum Estimation

  • Xiaohong Chen
Reference work entry
DOI: https://doi.org/10.1057/978-1-349-95189-5_2695

Abstract

Semi-nonparametric models are more flexible and robust than parametric models, but are more complex due to the presence of infinite dimensional unknown parameters. This article describes the method of sieve extremum estimation of semi-nonparametric models, which is a general method of optimizing an empirical criterion function over a sequence of approximating parameter spaces (that is, sieves). Widely used sieve spaces and criterion functions are presented as examples, including the sieve M-estimation, series estimation, and sieve minimum distance estimation as special cases. Existing results are cited on asymptotic properties and applications of the method.

Keywords

ARCH models Artificial neural networks Chaos Engel curve Generalized method of moments Polynomials Maximum likelihood Measurement error models Nonparametric models Semi-nonparametric models Series estimation Sieve extremum estimation Sieve minimum distance Simultaneous equations Splines Spurious regressions Treatment effect Wavelets 

JEL Classifications

C14 
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© Macmillan Publishers Ltd. 2018

Authors and Affiliations

  • Xiaohong Chen
    • 1
  1. 1.