Applied nonparametric instrumental variables estimation.
Instrumental variables are widely used in applied econometrics to achieve identification and carry out estimation and inference in models that contain endogenous explanatory variables. In most applications, the function of interest (e.g., an Engel curve or demand function) is assumed to be known up...
| Publicado en: | Econometrica Vol. 79; no. 2; pp. 347 - 395 |
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| Formato: | Artículo |
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Wiley-Blackwell
March 2011
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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=511467104&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 511467104 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00129682 ECN jtl: Econometrica issn: 00129682 maglogo: N pubinfo: dt: March 2011 vid: 79 iid: 2 pid: 480 pub: Wiley-Blackwell artinfo: ui: 511467104 10.3982/ECTA8662 ppf: 347 ppct: 48 formats: tig: atl: Applied nonparametric instrumental variables estimation. aug: au: Horowitz, Joel L. su: Instrumental variables (Statistics) Nonparametric statistics Estimation theory sug: subj: Instrumental variables (Statistics) Nonparametric statistics Estimation theory ab: Instrumental variables are widely used in applied econometrics to achieve identification and carry out estimation and inference in models that contain endogenous explanatory variables. In most applications, the function of interest (e.g., an Engel curve or demand function) is assumed to be known up to finitely many parameters (e.g., a linear model), and instrumental variables are used to identify and estimate these parameters. However, linear and other finite-dimensional parametric models make strong assumptions about the population being modeled that are rarely if ever justified by economic theory or other a priori reasoning and can lead to seriously erroneous conclusions if they are incorrect. This paper explores what can be learned when the function of interest is identified through an instrumental variable but is not assumed to be known up to finitely many parameters. The paper explains the differences between parametric and nonparametric estimators that are important for applied research, describes an easily implemented nonparametric instrumental variables estimator, and presents empirical examples in which nonparametric methods lead to substantive conclusions that are quite different from those obtained using standard, parametric estimators. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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