Grigory Franguridi
Research scientistCenter for Economic and Social Research
University of Southern California
Publications
"Medical costs and caregiver burden of delivering disease-modifying Alzheimer's treatments with different duration and route of administration"with Tabasa Ozawa and Soeren Mattke
Journal of Prevention of Alzheimer's Disease, 2024
Abstract
BackgroundMultiple disease modifying treatment for Alzheimer’s disease are currently in clinical development or have been recently approved for use. They have vastly different treatment properties but so far, little work has been done to quantify the impact of treatment properties on the treatment’s value in terms of medical and social care costs and caregiver burden.
Objectives
This study aims to analyze how the mode of treatment administration, treatment frequency and duration, and monitoring requirements affect the value of disease modifying treatments. In order to isolate these effects, we compare five hypothetical disease modifying treatments with equal efficacy and safety: (1) chronic bi-weekly intravenous infusion, (2) chronic four-weekly intravenous infusion, (3) 52 weeks fixed duration four-weekly intravenous infusion, (4) chronic subcutaneous injections, and (5) chronic oral prescription on their direct medical costs, caregiver burden, and preservation of treatment value.
Design
Survey of Alzheimer’s disease treatment clinics and retrospective data analysis.
Setting
United States.
Measurements
Direct medical cost and caregiver burden of treatment administration and monitoring compared to gross treatment benefit.
Results
Chronic bi-weekly infusion treatment had the highest direct medical cost ($45,208) and caregiver burden ($6,095), reducing the treatment value by 44%, while oral treatment with the lowest direct medical cost ($1,983) and caregiver burden ($457) reduced the treatment value by only 2%.
Substantial caregiver burden was reported from the survey, with a reported average of 2.3 hours for an office visit and infusion, 44 minutes of round-trip travel time, and 78% of patients being accompanied by a caregiver for treatment.
Conclusion
Burden of chronic intravenous treatments exceed the gross medical and social care cost savings and value of caregiver benefit. The results suggest the need for less complex treatments that require fewer clinic visits to preserve the economic value of disease modifying treatments.
with Hyungsik Roger Moon
Econometric Theory, 38, 2022, 1073–1091
Abstract
For an $N \times T$ random matrix $X(\beta)$ with weakly dependent uniformly sub-Gaussian entries $x_{it}(\beta)$ that may depend on a possibly infinite-dimensional parameter $\beta\in\mathbf{B}$, we obtain a uniform bound on its operator norm of the form $\mathbb{E} \sup_{\beta \in \mathbf{B}} \|X(\beta)\| \le CK\left( \sqrt{\max(N,T)} + \gamma_2(\mathbf{B}, d_{\mathbf{B}}) \right)$, where $C$ is an absolute constant, $K$ controls the tail behavior of (the increments of) $x_{it}(\cdot)$, and $\gamma_2(\mathbf{B}, d_{\mathbf{B}})$ is Talagrand’s functional, a measure of multi-scale complexity of the metric space $(\mathbf{B}, d_{\mathbf{B}})$. 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.Working papers
"Closed-form estimation and inference for panels with attrition and refreshment samples" (Oct 2024)with Lidia Kosenkova
Abstract
It has long been established that, if a panel dataset suffers from attrition, auxiliary (refreshment) sampling restores full identification under additional assumptions that still allow for nontrivial attrition mechanisms. Such identification results rely on implausible assumptions about the attrition process or lead to theoretically and computationally challenging estimation procedures. We propose an alternative identifying assumption that, despite its nonparametric nature, suggests a simple estimation algorithm based on a transformation of the empirical cumulative distribution function of the data. This estimation procedure requires neither tuning parameters nor optimization in the first step, i.e. has a closed form. We prove that our estimator is consistent and asymptotically normal and demonstrate its good performance in simulations. We provide an empirical illustration with income data from the Understanding America Study.with Bulat Gafarov and Kaspar Wüthrich
revise & resubmit, Journal of Econometrics
Previous drafts: Dec 2022, Apr 2021
Abstract
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.with Pasha Andreyanov
Previous drafts: June 2022, June 2021
Abstract
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.Abstract
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.Abstract
I suggest an enhancement of the procedure of Chiong, Hsieh, and Shum (2017) for calculating bounds on counterfactual demand in semiparametric discrete choice models. Their algorithm relies on a system of inequalities indexed by cycles of a large number $M$ of observed markets, and hence seems to require computationally infeasible enumeration of all such cycles. I show that such enumeration is unnecessary because solving the “fully efficient” inequality system exploiting cycles of all possible lengths $K=1,\dots,M$ can be reduced to finding the length of the shortest path between every pair of vertices in a complete bidirected weighted graph on $M$ vertices. The latter problem can be solved using the Floyd–Warshall algorithm with computational complexity $O(M^3)$, which takes only seconds to run even for thousands of markets. Monte Carlo simulations illustrate the efficiency gain from using cycles of all lengths, which turns out to be positive, but small.Work in progress
"Raking for estimation and inference in panel models with attrition and refreshment"
with
Jinyong Hahn,
Pierre Hoonhout,
Arie Kapteyn,
and
Geert Ridder
"Robust estimation and inference in panel models with attrition and refreshment"
with
Jinyong Hahn
and
Geert Ridder
"Debiasing functions of private statistics in postprocessing"
with
Flavio Calmon,
Elbert Du,
Cynthia Dwork,
and
Brian Finley
"The association between risk-adjusted wound healing rates and long-term outcomes in a network of U.S. wound care clinics"
with
Andrew Becker,
Soeren Mattke,
Mary Sheridan,
and
William Ennis
"Nonparametric welfare analysis with additively separable heterogeneity"
"Dyadic quantile regression"
with
Hyungsik Roger Moon
Graduate courses
@UCLA (Winter 2024):
Machine learning for economists
ML pipeline. Linear models. Regularization: lasso, ridge. Logistic regression. Decision trees and random forests. Imbalanced data: SMOTE. Neural networks.
Clustering and principal component analysis. Bagging, boosting, and ensemble methods. Large language models. Reinforcement learning.
@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
Undergraduate courses
Principles of microeconomics (USC 2017)
Contact
USC Dornsife Center for Economic and Social Research
635 Downey Way, VPD, Room 501K
Los Angeles, CA 90089
Email: franguri [at] usc [dot] edu