Average and Quantile Effects in Nonseparable Panel Models

By Victor Chernozhukov, Iv'an Fern'andez-Val, Jinyong Hahn, Whitney K. Newey. 2009

256 citations recorded by the source corpus.

Nonseparable panel models are important in a variety of economic settings, including discrete choice. This paper gives identification and estimation results for nonseparable models under time-homogeneity conditions that are like �time is randomly assigned� or �time is an instrument.� Partial-identification results for average and quantile effects are given for discrete regressors, under static or dynamic conditions, in fully nonparametric and in semiparametric models, with time effects. It is shown that the usual, linear, fixed-effects estimator is not a consistent estimator of the identified average effect, and a consistent estimator is given. A simple estimator of identified quantile treatment effects is given, providing a solution to the important problem of estimating quantile treatment effects from panel data. Bounds for overall effects in static and dynamic models are given. The dynamic bounds provide a partial-identification solution to the important problem of estimating the effect of state dependence in the presence of unobserved heterogeneity. The impact of T, the number of time periods, is shown by deriving shrinkage rates for the identified set as T grows. We also consider semiparametric, discrete-choice models and find that semiparametric panel bounds can be much tighter than nonparametric bounds. Computationally convenient methods for semiparametric models are presented. We propose a novel inference method that applies in panel data and other settings and show that it produces uniformly valid confidence regions in large samples. We give empirical illustrations.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/39d4331c8fea1cb3254d60771d2cb5c9a0bf4731

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Citations in the corpus

Quantile regression with nonadditive fixed effects (2022, background): “With a similar motivation as the one discussed in this paper, Chernozhukov et al. (2013) consider identification and estimation of quantile effects in nonseparable panel models with exogenous, discrete variables.”
When Should We Use Unit Fixed Effects Regression Models for Causal Inference with Longitudinal Data? (2019, background): “Proposition 1 (Inconsistency of the Linear Fixed Effects Regression Estimator [Chernozhukov et al. 2013, Theorem 1]).”
Common correlated effects estimation of heterogeneous dynamic panel quantile regression models (2020, unknown): “Pesaran (2006), Bai (2009), Pesaran and Chudik (2014), Moon and Weidner (2015, 2017), Chudik and Pesaran (2015)). The Common Correlated Effects (CCE) approach of Pesaran (2006) is robust to cross-sectional dependence and slope heterogeneity, and it has been further developed to allow for possible un”
Individual and time effects in nonlinear panel models with large N , T (2013, methodology): “The Neyman and Scott incidental parameter problem has been extensively discussed in the econometric literature; see, for example, Heckman (1981), Lancaster (2000), and Greene (2004).”
Nonlinear Panel Data Estimation via Quantile Regressions (2015, methodology): “Recent related approaches to quantile panel data models include Chernozhukov et al. (2013, 2015) and Graham et al. (2015).”
A quantile correlated random coefficients panel data model (2016, methodology): “Our estimate of β p (τ) is given by the solution to the empirical counterpart of (20) above”
Estimating and Testing a Quantile Regression Model with Interactive Effects (2012, methodology): “To estimate this model, we can accommodate the instrumental variable approach proposed in Chernozhukov and Hansen (2005) to panel data, integrating out the quantile τ ′ as in Ma and Koenker (2006). If we augment the designmatrix with cross-sectional averages, we have that the approximation for the f”
Difference-in-Differences with multiple time periods (2018, background): “, de Chaisemartin and D’Haultfœuille (2020), Goodman-Bacon (2018), Imai et al. (2018), Borusyak and Jaravel (2017), Athey and Imbens (2018) and Sun and Abraham (2020).”
Minimizing Sensitivity to Model Misspecification (2018, background): “From (24), we see that sπ(y) = ∇π log f(y) can be represented by the function g(y | a)/f(y).”
A Simple Approach to Quantile Regression for Panel Data (2011, background): “These type of models are flexible enough to provide quantile treatment effects (see, e.g. Chernozhukov et al., 2010, and Graham and Powell, 2010).”