I am an econometrician at the NYU department of Economics. My research interests are in microeconometrics of panel data and applied microeconomics. I use tools from Machine Learning and AI to develop methods suitable for economic data. I also have an interest in financial econometrics and health.
Joint with Stephane Bonhomme and Thibaut Lamadon
Econometrica, 87(3), 699–739
We propose a framework to identify and estimate earnings distributions and worker composition on matched panel data, allowing for two-sided worker-firm unobserved heterogeneity. We introduce two models: a static model that allows for nonlinear interactions between workers and firms, and a dynamic model that allows in addition for Markovian earnings dynamics and endogenous mobility.
R package for the estimator
Joint with Stephane Bonhomme
This paper introduces time-varying grouped patterns of heterogeneity in linear panel data models. A distinctive feature of our approach is that group membership is left unrestricted. We estimate the parameters of the model using a “grouped fixed-effects” estimator that minimizes a least-squares criterion with respect to all possible groupings of the cross-sectional units.
R package for the estimator
OF STOCHASTIC VOLATILITY MODELS
We propose a method for estimating stochastic volatility models by adapting the HJM approach to the case of volatility derivatives. We characterize restrictions that observed variance swap dynamics have to satisfy to prevent arbitrage opportunities. When the drift of variance swap rates are affine under the pricing measure, we obtain closed form expressions for those restrictions and formulas for forward variance curves.
Joint with Tetsuya Kaji and Guillaume Pouliot
Revise and Resubmit at Econometrica
We propose a new simulation-based estimation method, adversarial estimation, for structural models. The estimator is formulated as the solution to a minimax problem between a generator (which generates synthetic observations using the structural model) and a discriminator (which classifies if an observation is synthetic). The discriminator maximizes the accuracy of its classification while the generator minimizes it. We show that, with a sufficiently rich discriminator, the adversarial estimator attains parametric efficiency. We advocate the use of a neural network as a discriminator that can exploit adaptivity properties and attain fast rates of convergence. We apply our method to the elderly's saving decision model and show that including gender and health profiles in the discriminator uncovers the bequest motive as an important source of saving across the wealth distribution.
We study panel data estimators based on a discretization of unobserved heterogeneity when individual heterogeneity is not necessarily discrete in the population. We focus on two-step grouped-fixed effects estimators, where individuals are classified into groups in a first step using k-means clustering, and the model is estimated in a second step allowing for group-specific heterogeneity
I consider the problem of quantifying externalities in settings in which an outcome depends on own characteristics and on the characteristics of other individuals. In contrast to existing approaches, which require a priori knowledge of who interacts with whom, I propose a method that estimates both the structure of interactions and spillover effects using panel data
Revise and Resubmit at Journal of Financial Economics
Unlike other studies focusing on the properties of standard estimators and tests, we estimate the sets of SDFs and risk prices compatible with the asset pricing restrictions of a given model. We also propose tests to detect problematic situations with economically meaningless SDFs uncorrelated to the test assets.
WORK IN PROGRESS
Joint with Milena Almagro
> DATA-DRIVEN NESTS IN DISCRETE CHOICE MODELS
Nested logit models represent consumers as agents that choose sequentially over product groups, hence allowing for flexible substitution patterns across products. Assuming knowledge of these nest has proven problematic in many applications. We make use of the panel structure of consumer choice data, where there are many consumers and relatively few products, to estimate both the nested structure as well as the structural parameters. We propose a two-step estimation strategy where in the first step we use clustering methods to classify products, and in the second step we estimate the model conditional on the estimated nest structure, as in Bonhomme, Lamadon, Manresa (2019).