Abstract
AbstractIn 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.
Highlights
In empirical research, the complexity of econometric models has been greatly enriched by the availability of panel data sets
We provide an intensive review of the recent developments for semi-parametric and fully nonparametric panel data models that are linearly separable in the innovation and the individual specific term
We focus on the resulting estimators for different specifications of these nonparametric models, i.e., allowing for additive structures of the unknown smooth function or the presence of time lagged endogenous explanatory variables
Summary
The complexity of econometric models has been greatly enriched by the availability of panel data sets. Through the use of panel data econometric models, under some standard assumptions on the data generating process, it is possible to draw inference on the parameters of interest that otherwise would be impossible to obtain As it is often the case in applied econometrics, we are interested in partial effects of the observable explanatory variables in the population regression (quantile) function but, following the approach in Chamberlain (1984), when there exists time-invariant or/and individual invariant omitted latent variables. We provide an intensive review of the recent developments for semi-parametric 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 settings, the so-called fixed and random effects panel data models.
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