Non- and Semi-Parametric Panel Data Models: A Selective Review

Chen, Jia; Li, Degui; Gao, Jiti

doi:10.2139/ssrn.2313431

Cited by 5 publications

(5 citation statements)

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“…Note that Su and Ullah () and Chen et al . () focus on similar models, although in this case we include the most recent results and pay special attention to the so‐called incidental parameters problem as well as with endogenous explanatory variables.…”

Section: Resultsmentioning

confidence: 99%

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Nonparametric and Semiparametric Panel Data Models: Recent Developments

Rodríguez-Póo

Soberón

2016

Journal of Economic Surveys

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In this paper, we provide an intensive review of the recent developments for semiparametric and fully nonparametric panel data models that are linearly separable in the innovation and the individual-specific term. We analyze these developments under two alternative model specifications: fixed and random effects panel data models. More precisely, in the random effects setting, we focus our attention in the analysis of some efficiency issues that have to do with the so-called working independence condition. This assumption is introduced when estimating the asymptotic variance-covariance matrix of nonparametric estimators. In the fixed effects setting, to cope with the so-called incidental parameters problem, we consider two different estimation approaches: profiling techniques and differencing methods. Furthermore, we are also interested in the endogeneity problem and how instrumental variables are used in this context. In addition, for practitioners, we also show different ways of avoiding the so-called curse of dimensionality problem in pure nonparametric models. In this way, semiparametric and additive models appear as a solution when the number of explanatory variables is large.

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Section: Resultsmentioning

confidence: 99%

“…Meanwhile, in Chen et al . () this type of models are studied when deterministic trends and single‐index specifications are present.…”

Section: Introductionmentioning

confidence: 99%

Nonparametric and Semiparametric Panel Data Models: Recent Developments

Rodríguez-Póo

Soberón

2016

Journal of Economic Surveys

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“…The varying coefficient model considered here is therefore part of the already long tradition of research on semiparametric estimation of partially linear varying coefficient panel data models 6 which allow flexibility to characterize trending phenomenon in nonlinear panel data analysis. Some use semiparametric profile likelihood methods (Chen et al (2012), Li et al (2013), kernel or averaged local linear estimation (Li et et al (2011)). In the spirit of works on semiparametric partially linear model using series estimation methods (Huang et al (2002), Fan and Li (2004), Qu and Li (2006), Fan et al (2007), An et al (2016) for instance), we use a Bayesian linear-mixed Gaussian model-based penalized spline specification with random coefficients proposed by Lee and Wand (2016).…”

Section: Introductionmentioning

confidence: 99%

“…,Chen et al (2012),Zhang et al (2012)) or on nonparametric models with fixed effects(Li et al (2011),Lee and Robinson (2015)). The literature on the subject is important and is growing rapidly (see the surveys ofLi and Racine (2007),Ai and Li (2008),Su and Ullah (2011),Chen et al (2013),Sun et al (2015b),Rodriguez-Poo and Soberon (2017) and also the special issue of the Journal of Econometrics, edited by, to mention a few.…”

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confidence: 99%

Growth Empirics: a Bayesian Semiparametric Model With Random Coefficients for a Panel of OECD Countries

Baltagi¹,

Bresson²,

Etienne³

2020

Essays in Honor of Cheng Hsiao

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This paper proposes semiparametric estimation of the relationship between growth rate of GDP per capita, growth rates of physical and human capital, labor as well as other covariates and common trends for a panel of 23 OECD countries observed over the period 1971-2015. The observed differentiated behaviors by country reveal strong heterogeneity. This is the motivation behind using a mixed fixedand random-coefficients model to estimate this relationship. In particular, this paper uses a semiparametric specification with random intercepts and slopes coefficients. Motivated by Lee and Wand (2016), we estimate a mean field variational Bayes semiparametric model with random coefficients for this panel of countries. Results reveal nonparametric specifications for the common trends. The use of this flexible methodology may enrich the empirical growth literature underlining a large diversity of responses across variables and countries.

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“…For nonparametric and semiparametric panel data models with fixed effects, a growing strand of literature has emerged during the last years, including Baltagi and Li (2002), Su and Ullah (2006), Henderson et al (2008) and Mammen et al (2009). Extensive literature reviews are provided by Su and Ullah (2011) and Chen et al (2013). While having different concepts to handle the fixed effects and strictly parametric effects, all discussed methods have in common that they rely on some kind of kernel estimator to estimate the nonparametric model components.…”

Section: Introductionmentioning

confidence: 99%

A Penalized Spline Estimator for Fixed Effects Panel Data Models

Pütz

Kneib

2016

SSRN Journal

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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. Terms of use: Documents in SOEPpapers on Multidisciplinary Panel Data Research at DIW BerlinThis series presents research findings based either directly on data from the German SocioEconomic Panel study (SOEP) or using SOEP data as part of an internationally comparable data set (e.g. CNEF, ECHP, LIS, LWS, CHER/PACO). SOEP is a truly multidisciplinary household panel study covering a wide range of social and behavioral sciences: economics, sociology, psychology, survey methodology, econometrics and applied statistics, educational science, political science, public health, behavioral genetics, demography, geography, and sport science.The decision to publish a submission in SOEPpapers is made by a board of editors chosen by the DIW Berlin to represent the wide range of disciplines covered by SOEP. There is no external referee process and papers are either accepted or rejected without revision. Papers appear in this series as works in progress and may also appear elsewhere. They often represent preliminary studies and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be requested from the author directly.Any opinions expressed in this series are those of the author(s) and not those of DIW Berlin.Research disseminated by DIW Berlin may include views on public policy issues, but the institute itself takes no institutional policy positions. AbstractEstimating nonlinear effects of continuous covariates by penalized splines is well established for regressions with cross-sectional data as well as for panel data regressions with random effects. Penalized splines are particularly advantageous since they enable both the estimation of unknown nonlinear covariate effects and inferential statements about these effects. The latter are based, for example, on simultaneous confidence bands that provide a simultaneous uncertainty assessment for the whole estimated functions. In this paper, we consider fixed effects panel data models instead of random effects specifications and develop a first-difference approach for the inclusion of penalized splines in this case. We take the resulting dependence structure into account and adapt the construction of simultaneous confidence bands accordingly. In addition, the penalized spline estimates as well as the confidence bands are also made available for derivatives of the estimated effects which are of considerable interest in many application areas. A...

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Non- and Semi-Parametric Panel Data Models: A Selective Review

Cited by 5 publications

References 41 publications

Nonparametric and Semiparametric Panel Data Models: Recent Developments

Nonparametric and Semiparametric Panel Data Models: Recent Developments

Growth Empirics: a Bayesian Semiparametric Model With Random Coefficients for a Panel of OECD Countries

A Penalized Spline Estimator for Fixed Effects Panel Data Models

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