Estimation of Nonparametric Models With Simultaneity.
We introduce methods for estimating nonparametric, nonadditive models with simultaneity. The methods are developed by directly connecting the elements of the structural system to be estimated with features of the density of the observable variables, such as ratios of derivatives or averages of produ...
| Publicado en: | Econometrica Vol. 83; no. 1; pp. 1 - 67 |
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| Formato: | Artículo |
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Wiley-Blackwell
Jan2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=101048790&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 101048790 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00129682 ECN jtl: Econometrica issn: 00129682 maglogo: Y pubinfo: dt: Jan2015 vid: 83 iid: 1 pid: 480 pub: Wiley-Blackwell artinfo: ui: 101048790 10.3982/ECTA9348 ppf: 1 ppct: 66 formats: tig: atl: Estimation of Nonparametric Models With Simultaneity. aug: au: Matzkin, Rosa L. affil: Dept. of Economics, University of California-Los Angeles su: Estimation theory Density functionals Regression analysis Matrix inversion Least squares sug: subj: Estimation theory Density functionals Regression analysis Matrix inversion Least squares keyword: constructive identification endogeneity instrumental variables kernel estimators nonseparable models Simultaneous equations structural models constructive identification endogeneity instrumental variables kernel estimators nonseparable models Simultaneous equations structural models ab: We introduce methods for estimating nonparametric, nonadditive models with simultaneity. The methods are developed by directly connecting the elements of the structural system to be estimated with features of the density of the observable variables, such as ratios of derivatives or averages of products of derivatives of this density. The estimators are therefore easily computed functionals of a nonparametric estimator of the density of the observable variables. We consider in detail a model where to each structural equation there corresponds an exclusive regressor and a model with one equation of interest and one instrument that is included in a second equation. For both models, we provide new characterizations of observational equivalence on a set, in terms of the density of the observable variables and derivatives of the structural functions. Based on those characterizations, we develop two estimation methods. In the first method, the estimators of the structural derivatives are calculated by a simple matrix inversion and matrix multiplication, analogous to a standard least squares estimator, but with the elements of the matrices being averages of products of derivatives of nonparametric density estimators. In the second method, the estimators of the structural derivatives are calculated in two steps. In a first step, values of the instrument are found at which the density of the observable variables satisfies some properties. In the second step, the estimators are calculated directly from the values of derivatives of the density of the observable variables evaluated at the found values of the instrument. We show that both pointwise estimators are consistent and asymptotically normal. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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