I am an economist with research interests
in econometrics and industrial organization.

I am currently a PhD candidate in economics
at the University of Southern California.

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A uniform bound on the operator norm of sub-Gaussian random matrices and its applications [2021, arxiv]

With Hyungsik Roger Moon

Forthcoming, Econometric Theory

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.

Conditional quantile estimators: a small sample theory [Apr 2021, arxiv CESifo]

With Bulat Gafarov and Kaspar Wüthrich

Reject & resubmit, Journal of Econometrics

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]


Dyadic quantile regression

With Hyungsik Roger Moon

Estimation and inference in panel models with attrition and refreshment

With Jinyong Hahn, Pierre Hoonhout, Arie Kapteyn, and Geert Ridder



@USC (2017-2022):

  • Big data econometrics

  • 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

  • Game theory

  • Empirics of financial markets

  • Probability theory


  • Principles of microeconomics (USC 2017)



Mailing address:

Grigory Franguridi

Department of Economics, USC 
3620 South Vermont Ave. Kaprielian (KAP) Hall, 300
Los Angeles, CA 90089

Email: franguri [at] usc [dot] edu