Nonparametric Instrumental Regression

Darolles, Serge; Fan, Yanqin; Florens, Jean‐Pierre; Renault, Éric

doi:10.2139/ssrn.1338775

Cited by 219 publications

(332 citation statements)

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“…Here we propose an iterative estimation method. In fact, one can also use noniterative method to solve a Fredholm integral equation of the second kind; see, e.g., Linton and Mammen (2005), Darolles, Fan, Florens, and Renault (2011). This involves solving a linear system of equations.…”

Section: Run the Qth Order Local Polynomial Regression Ofmmentioning

confidence: 99%

Nonparametric dynamic panel data models: Kernel estimation and specification testing

2013

Journal of Econometrics

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Motivated by the first differencing method for linear panel data models, we propose a class of iterative local polynomial estimators for nonparametric dynamic panel data models with or without exogeous regressors. The estimators utilize the additive structure of the first-differenced model, the fact that the two additive components have the same functional form, and the unknown function of interest is implicitly defined as a solution of a Fredholm integral equation of the second kind. We establish the uniform consistency and asymptotic normality of the estimators. We also propose a consistent test for the correct specification of linearity in typical dynamic panel data models based on the L 2 distance of our nonparametric estimates and the parametric estimates under the linear restriction. We derive the asymptotic distributions of the test statistic under the null hypothesis and a sequence of Pitman local alternatives, and prove its consistency against global alternatives.Simulations suggest that the proposed estimators and tests perform well in finite samples. We apply our new methods to study the relation between economic growth, initial economic condition and capital accumulation and find the nonlinear relation between economic growth and initial economic condition.

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Section: Run the Qth Order Local Polynomial Regression Ofmmentioning

confidence: 99%

Nonparametric dynamic panel data models: Kernel estimation and specification testing

2013

Journal of Econometrics

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“…If W were observed this would be the instrumental variables nonparametric regression model of, e.g., Newey and Powell (2003), Hall and Horowitz (2005), Darolles, Fan, Florens, and Renault, (2011) and Chen and Reiss (2011). Assume, as those authors do, that g .X / is identified from the conditional moments in equation (8), and consider how we might construct sample moment analogues to this equation in the case where W is not observed.…”

Section: Latent Nonparametric Instrumental Variablesmentioning

confidence: 99%

An Overview of the Special Regressor Method

Lewbel

2014

The Oxford Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics

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“…Then for all u x in S Ux and z 2 S ; &(z; u x ) 1. If r is also separable, so that r(x; u y ) = r 1 (x)+ u y (see, e.g., Newey and Powell, 2003;Darolles, Florens, and Renault, 2003), then (z) = ss (z) for z 2 S ; where…”

Section: Separable Qmentioning

confidence: 99%

“…Blundell and Powell, 2003;Darolles, Florens, and Renault, 2003;and Hahn and Ridder, 2009). Correspondingly, Chalak and White (2009) (CW) emphasize the structural origins of instruments yielding (conditional) independence relationships that serve to identify e¤ects of interest.…”

Section: Introductionmentioning

confidence: 99%

“…Two extensions of IV to nonlinear systems have been studied in the literature. The …rst is based on what Darolles, Florens, and Renault (2003) call "instrumental regression" (IR), where Y is separably determined as, say, Y = r(X)+"; with E(" j Z) = 0: Blundell and Powell (2003), Darolles, Florens, and Renault (2003), Newey and Powell (2003), and Santos (2006), among others, show that IR methods can reliably identify speci…c e¤ect measures in separable structures. But they lose their structural interpretation in the nonseparable case unless X is separably determined (see e.g.…”

Section: Introductionmentioning

confidence: 99%

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Local indirect least squares and average marginal effects in nonseparable structural systems

Schennach

White

Chalak

2012

Journal of Econometrics

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We study the scope of local indirect least squares (LILS) methods for nonparametrically estimating average marginal e¤ects of an endogenous cause X on a response Y in triangular structural systems that need not exhibit linearity, separability, or monotonicity in scalar unobservables. One main …nding is negative: in the fully nonseparable case, LILS methods cannot recover the average marginal e¤ect. LILS methods can nevertheless test the hypothesis of no e¤ect in the general nonseparable case. We provide new nonparametric asymptotic theory, treating both the traditional case of observed exogenous instruments Z and the case where one observes only error-laden proxies for Z. Acknowledgement 0.1 We thank Stefan Hoderlein, Xun Lu, Andres Santos, and Suyong Song for helpful comments and suggestions. Any errors are the authors' responsibility. S. M. Schennach acknowledges support from the National Science Foundation via grants SES-0452089 and SES-0752699. This is a revised version of a paper titled "Estimating Average Marginal E¤ ects in Nonseparable Structural Systems." JEL Classi…cation Numbers: C13, C14, C31.

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Nonparametric Instrumental Regression

Abstract: and Toulouse. We also thank R. Lestringand who performed the numerical illustration given in Section 5. 2 Lyxor Asset Management and CREST.

Cited by 219 publications

References 54 publications

Nonparametric dynamic panel data models: Kernel estimation and specification testing

Nonparametric dynamic panel data models: Kernel estimation and specification testing

An Overview of the Special Regressor Method

Local indirect least squares and average marginal effects in nonseparable structural systems

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