A uniform bound on the operator norm of sub-Gaussian random matrices and its applications [pdf]
With Hyungsik Roger Moon
Econometric Theory, 38, 2022, 1073–1091.
For an N×T random matrix X(β) with weakly dependent uniformly sub-Gaussian entries xit(β) that may depend on a possibly infinite-dimensional parameter β ∈ B, we obtain a uniform bound on its operator norm of the form E supβ∈B ||X(β)|| ≤ CK(√max(N, T) + γ2(B, dB)), where C is an absolute constant, K controls the tail behavior of (the increments of) xit(·), and γ2(B, dB) is Talagrand’s functional, a measure of multi-scale complexity of the metric space (B, dB). We illustrate how this result may be used for estimation that seeks to minimize the operator norm of moment conditions as well as for estimation of the maximal number of factors with functional data.
Higher order conditional moment dynamics and forecasting value-at-risk [2014, in Russian, pdf]
Quantile No.12, pp. 69–82
Bias correction and uniform inference for the quantile density function [July 2022, arxiv]
For the kernel estimator of the quantile density function (the derivative of the quantile function), I show how to perform the boundary bias correction, establish the rate of strong uniform consistency of the bias-corrected estimator, and construct the confidence bands that are asymptotically exact uniformly over the entire domain [0,1]. The proposed procedures rely on the pivotality of the studentized bias-corrected estimator and known anti-concentration properties of the Gaussian approximation for its supremum.
Nonparametric inference on counterfactuals in first-price auctions [major revision, June 2022, arxiv]
With Pasha Andreyanov
In a classical model of the first-price sealed-bid auction with independent private values, we develop nonparametric estimation and inference procedures for a class of policy-relevant metrics, such as total expected surplus and expected revenue under counterfactual reserve prices. Motivated by the linearity of these metrics in the quantile function of bidders’ values, we propose a bid spacings-based estimator of the latter and derive its Bahadur-Kiefer expansion. This makes it possible to construct exact uniform confidence bands and assess the optimality of a given auction rule. Using the data on U.S. Forest Service timber auctions, we test whether setting zero reserve prices in these auctions was revenue maximizing.
Bias correction for quantile regression estimators [major revision, Dec 2022, arxiv]
With Bulat Gafarov and Kaspar Wüthrich
Resubmitted, Journal of Econometrics
Supercedes "Conditional quantile estimators: a small sample theory"
We study the bias of classical quantile regression and instrumental variable quantile regression estimators. While being asymptotically first-order unbiased, these estimators can have non-negligible second-order biases. We derive a higher-order stochastic expansion of these estimators using empirical process theory. Based on this expansion, we derive an explicit formula for the second-order bias and propose a feasible bias correction procedure that uses finite-difference estimators of the bias components. The proposed bias correction method performs well in simulations. We provide an empirical illustration using Engel's classical data on household expenditure.
Efficient counterfactual estimation in semiparametric discrete choice models: a note on Chiong, Hsieh, and Shum (2017) [Dec 2021, arxiv]
Nonparametric welfare analysis with additively separable heterogeneity [pdf coming soon]
Big data econometrics
Probability and statistics
Time series analysis
Economics of financial markets
@New Economic School (2012-2014):
Econometrics I, II, III
Mathematics for economists I, II
Empirics of financial markets
Principles of microeconomics (USC 2017)
USC Dornsife Center for Economic and Social Research
635 Downey Way, VPD, Room 501K
Los Angeles, CA 90089
Email: franguri [at] usc [dot] edu