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Network and Panel Quantile Effects Via Distribution Regression

Victor Chernozhukov, Iván Fernández-Val, Martin Weidner

arXiv 21 Mar 2018 · Econometrics · publishedJournal of Econometrics (2020) · 12 citations (OpenAlex)

arXiv:1803.08154 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper provides a method to construct simultaneous confidence bands for quantile functions and quantile effects in nonlinear network and panel models with unobserved two-way effects, strictly exogenous covariates, and possibly discrete outcome variables. The method is based upon projection of simultaneous confidence bands for distribution functions constructed from fixed effects distribution regression estimators. These fixed effects estimators are debiased to deal with the incidental parameter problem. Under asymptotic sequences where both dimensions of the data set grow at the same rate, the confidence bands for the quantile functions and effects have correct joint coverage in large samples. An empirical application to gravity models of trade illustrates the applicability of the methods to network data.

Citation extraction

48
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90
in-text mentions
48
distinct cited
6
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Fernandez-Val, I. and M. Weidner (2016) Individual and time effects in nonlinear panel models with large N, T1.000165100%
2Chernozhukov, V., I. Fernández-Val, B. Melly, and K. Wüthrich (2016,… (2016) Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes self1.00096100%
3van der Vaart, A. W. and J. A. Wellner (1996) Weak convergence0.87452100%
4Cruz-Gonzalez, M., I. Fernandez-Val, and M. Weidner (2016, October) (2016) probitfe and logitfe: Bias corrections for probit and logit models with two-way fixed effects0.84333100%
5Chernozhukov, V., D. Chetverikov, and K. Kato (2016) Empirical and multiplier bootstraps for suprema of empirical processes of increasing complexity, and related Gaussian couplings self0.84333100%
6Charbonneau, K. B (2017) Multiple fixed effects in binary response panel data models0.73732100%
7Jochmans, K (2018) Semiparametric analysis of network formation0.73732100%
8Chernozhukov, V., I. Fernández-Val, and B. Melly (2013) Inference on counterfactual distributions self0.64422100%
9Graham, B. S (2017) An econometric model of link formation with degree heterogeneity0.64422100%
10Neyman, J. and E. Scott (1948) Consistent estimates based on partially consistent observations0.64422100%

Showing the top 10 of 48 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Identification of Time-Varying Transformation Models with Fixed Effects, with an Application to Unobserved Heterogeneity in Resource Shares0.92843
2Dynamic Network Quantile Regression Model0.64422
3Debiased Fixed Effects Estimation of Binary Logit Models with Three-Dimensional Panel Data0.58533
4Dynamic Heterogeneous Distribution Regression Panel Models, with an Application to Labor Income Processes$^*$0.51121
5Fixed Effects Binary Choice Models: Estimation and Inference with Long Panels0.40511
6Distribution Regression in Duration Analysis: an Application to Unemployment Spells0.40511
7PARAMETRIC MODELING OF QUANTILE REGRESSION COEFFICIENT FUNCTIONS WITH LONGITUDINAL DATA0.40511
8Distributional Vector Autoregression: Eliciting Macro and Financial Dependence0.40511
9Regression Adjustment for Estimating Distributional Treatment Effects in Randomized Controlled Trials0.40511
10Partitioned Wild Bootstrap for Panel Data Quantile Regression0.40511