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.
PUBLICATIONS
A uniform bound on the operator norm of subGaussian 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 subGaussian entries xit(β) that may depend on a possibly infinitedimensional 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 multiscale 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 valueatrisk [2014, in Russian, pdf]
Quantile No.12, pp. 69–82
WORKING PAPERS
Bias correction and uniform inference for the quantile density function [July 2022, arxiv]
Submitted
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 biascorrected 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 biascorrected estimator and known anticoncentration properties of the Gaussian approximation for its supremum.
Nonparametric inference on counterfactuals in firstprice auctions [major revision, June 2022, arxiv]
With Pasha Andreyanov
Submitted
In a classical model of the firstprice sealedbid auction with independent private values, we develop nonparametric estimation and inference procedures for a class of policyrelevant 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 spacingsbased estimator of the latter and derive its BahadurKiefer 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]
WORK IN PROGRESS
Dyadic quantile regression
With Hyungsik Roger Moon
TEACHING
Graduate:
@USC (20172022):

Big data econometrics

Econometrics

Probability and statistics

Time series analysis

Economics of financial markets
@New Economic School (20122014):

Econometrics I, II, III

Mathematics for economists I, II

Game theory

Empirics of financial markets

Probability theory
Undergraduate:

Principles of microeconomics (USC 2017)
CONTACT
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