Nonparametric Estimation of Triangular Simultaneous Equations Models

Newey, Whitney K.; Powell, James Lawrence; Vella, F.

doi:10.1111/1468-0262.00037

Cited by 384 publications

(412 citation statements)

References 24 publications

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“…In order to address this issue, we consider the triangular nonparametric simultaneous specification of Newey et al (1999),…”

Section: Structural Form Estimationmentioning

confidence: 99%

“…Thus, functions π and G are the nonparametric generalization of the reduced forms for y and x. Newey et al (1999) discussed the identification of the system (9)-(10). 9 Starting from a preliminary estimation of the reduced formsπ andĜ:…”

Section: Structural Form Estimationmentioning

confidence: 99%

“…Observe that the method of Newey et al (1999) consists of estimating equation (15) by including an additional control variable which is the first difference residualsû it −û i,t−1 computed from equation (16). We perform estimation in two steps: (i) construct GAM semi-parametric first differences residualsû it −û i,t−1 of equation (16), (ii) estimate GAM semi-parametric model in equation (15) using the residualsû it −û i,t−1 from (i) as additional regressor.…”

Section: Structural Form Estimationmentioning

confidence: 99%

“…The latter relies on the fact that proximity to productivity frontier is endogenous and is then specified as part of a semi-parametric triangular system (Newey et al, 1999). To our knowledge, until now, the empirical literature on R&D and productivity was almost exclusively dominated by the parametric estimation of reduced forms.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinearities in productivity growth: A semi-parametric panel analysis

Azomahou

Diène

Diene

2013

Structural Change and Economic Dynamics

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We use country panel data spanning over 1998-2008 for both developed and developing countries to study the productivity growth when countries are close to the technology frontier. Relying on a semi-parametric generalized additive model, we estimate both reduced and structural forms for total factor productivity growth. We consider three measurements of frontier: the economy with the highest level of productivity growth, the world productivity growth and the productivity growth of the USA. We obtain a U-shape relation between productivity growth and the proximity to the world productivity growth. The relation between productivity growth and human capital displays an inverted U-shape form (res. U-shape) when the proximity to the highest productivity growth is used (res. the proximity to productivity growth of USA). Total staff in R&D has an inverted W-shape effect on productivity growth. The share of R&D expenditure funded by government and from abroad impact positively the growth of productivity. However, the increase in government spending on R&D has a greater impact on productivity growth when the former is weak, and a smaller impact when R&D spending is already high. International trade has a positive effect on productivity growth. Specification tests show that our semi-parametric models provide a better approximation of the data compared to the parametric analogue, revealing a high degree of nonlinearity governing productivity growth.JEL Classification: C14, I23, O3, O4

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“…In order to address this issue, we consider the triangular nonparametric simultaneous specification of Newey et al (1999),…”

Section: Structural Form Estimationmentioning

confidence: 99%

Section: Structural Form Estimationmentioning

confidence: 99%

Section: Structural Form Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Nonlinearities in productivity growth: A semi-parametric panel analysis

Azomahou

Diène

Diene

2013

Structural Change and Economic Dynamics

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“…The order of the polynomial is guided by the results of Newey et al (1998) which provides a semi-parametric estimate of the wage/hours profile. Using this result as a starting point we were unable to identify a dominating alternative specification.…”

Section: Empirical Examplementioning

confidence: 99%

Two-step estimation of panel data models with censored endogenous variables and selection bias

Vella

Verbeek

1999

Journal of Econometrics

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163

111

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General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.-Users may download and print one copy of any publication from the public portal for the purpose of private study or research -You may not further distribute the material or use it for any profit-making activity or commercial gain -You may freely distribute the URL identifying the publication in the public portal Take down policy If you believe that this document breaches copyright, please contact us providing details, and we will remove access to the work immediately and investigate your claim. Abstract This paper presents some two-step estimators for a wide range of parametric panel data models with censored endogenous variables and sample selection bias. Our approach is to derive estimates of the unobserved heterogeneity responsible for the endogeneity/selection bias to include as additional explanatory variables in the primary equation. These are obtained through a decomposition of the reduced form residuals. The panel nature of the data allows adjustment, and testing, for two forms of endogeneity and/or sample selection bias. Furthermore, it incorporates roles for dynamics and state dependence in the reduced form. Finally, we provide an empirical illustration which features our procedure and highlights the ability to test several of the underlying assumptions.1999 Elsevier Science S.A. All rights reserved.JEL classification: C23; C33; C34

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Department of Surgery, The University of Hong Kong

Fan¹

2002

Arch Surg

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Nonlinearity and endogeneity are common in causal effect studies with observational data. In this paper, we propose new estimation and inference procedures for nonparametric treatment effect functions with endogeneity and potentially highdimensional covariates. The main innovation of this paper is the double bias correction procedure for the nonparametric instrumental variable (NPIV) model under high dimensions. We provide a useful uniform confidence band of the marginal effect function, defined as the derivative of the nonparametric treatment function. The asymptotic honesty of the confidence band is verified in theory. Simulations and an empirical study of air pollution and migration demonstrate the validity of our procedures.

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Nonparametric Estimation of Triangular Simultaneous Equations Models

Abstract: This paper presents a simple two-step nonparametric estimator for a triangular simultaneous equation model.

Cited by 384 publications

References 24 publications

Nonlinearities in productivity growth: A semi-parametric panel analysis

Nonlinearities in productivity growth: A semi-parametric panel analysis

Two-step estimation of panel data models with censored endogenous variables and selection bias

Department of Surgery, The University of Hong Kong

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