Estimating semiparametric panel data models by marginal integration

Qian, Junhui; Wang, Le

doi:10.1016/j.jeconom.2011.09.030

“…4 Asymptotic properties of this estimator are derived in Qian and Wang (2012). In practice, we use a plug-in bandwidth that minimizes estimated asymptotic mean integrated squared error (AMISE).…”

Section: Methodsmentioning

confidence: 99%

“…samples I i,t−1 from the distribution f (·) and then we construct the estimator, m(I i,t ) = 1 n n i=1 g(I i,t , I i,t−1 ) using numerical integration. This approach is non-iterative (unlike that in Henderson et al, 2008), easy to implement in practice and has been shown to perform well in finite samples (Qian and Wang, 2012). …”

Section: Methodsmentioning

confidence: 99%

The inequality–growth plateau

Henderson

¹

,

Qian

²

,

Wang

³

2015

Self Cite

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. www.econstor.eu The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit organization supported by Deutsche Post Foundation. The center is associated with the University of Bonn and offers a stimulating research environment through its international network, workshops and conferences, data service, project support, research visits and doctoral program. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. Terms of use: Documents in D I S C U S S I O N P A P E R S E R I E SIZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author.IZA Discussion Paper No. 8771 January 2015 ABSTRACTThe Inequality-Growth Plateau *We examine the (potentially nonlinear) relationship between inequality and growth using a method which does not require an a priori assumption on the underlying functional form. This approach reveals a plateau completely missed by commonly used (nonlinear) parametric approaches -the economy first expands rapidly with a large decline in inequality, plateaus when inequality remains relatively stable, and then decreases rapidly with a large increase in inequality. This novel finding helps reconcile the conflicting results in the literature (using different parametric assumptions and datasets) and has important policy implications.JEL Classification: C5, C14, O4

show abstract

“…Not only can the model in (1.1) be conveniently applied to reduce the "curse-of-dimensionality" problem, but it also nests purely nonparametric fixed-effects panel data models as well as partially linear fixed-effects panel data models studied by Henderson, Carroll & Li (2008), Qian & Wang (2012) and Li & Liang (2015), who all however focus on a rather restrictive case of d g = 0.…”

Section: Introductionmentioning

confidence: 99%

Estimation and Inference in Functional-Coefficient Spatial Autoregressive Panel Data Models with Fixed Effects

Sun

¹

,

Malikov

²

2017

View full text Add to dashboard Cite

This paper develops an innovative way of estimating a functional-coefficient spatial autoregressive panel data model with unobserved individual effects which can accommodate (multiple) time-invariant regressors in the model with a large number of cross-sectional units and a fixed number of time periods. The methodology we propose removes unobserved fixed effects from the model by transforming the latter into a semiparametric additive model, the estimation of which however does not require the use of backfitting or marginal integration techniques. We derive the consistency and asymptotic normality results for the proposed kernel and sieve estimators. We also construct a consistent nonparametric test to test for spatial endogeneity in the data. A small Monte Carlo study shows that our proposed estimators and the test statistic exhibit good finite-sample performance.

show abstract

“…Most of the literature on non-and semiparametric panel models is based on the assumption that the regression function is the same across individuals; see Henderson et al (2008), Mammen et al (2009) and Qian and Wang (2012) among many others. This assumption, however, is very unrealistic in many applications.…”

Section: Introductionmentioning

confidence: 99%

Classification of nonparametric regression functions in heterogeneous panels

Vogt

¹

,

Linton

²

2015

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. We investigate a nonparametric panel model with heterogeneous regression functions. In a variety of applications, it is natural to impose a group structure on the regression curves. Specifically, we may suppose that the observed individuals can be grouped into a number of classes whose members all share the same regression function. We develop a statistical procedure to estimate the unknown group structure from the observed data. Moreover, we derive the asymptotic properties of the procedure and investigate its finite sample performance by means of a simulation study and a real-data example. Terms of use: Documents in EconStor may

show abstract

Estimating semiparametric panel data models by marginal integration

Cited by 44 publications

References 39 publications

The inequality–growth plateau

The inequality–growth plateau

Estimation and Inference in Functional-Coefficient Spatial Autoregressive Panel Data Models with Fixed Effects

Classification of nonparametric regression functions in heterogeneous panels

Contact Info

Product

Resources

About