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ELENA MANRESA

Assistant Professor NYU

 

ABOUT ME

Assistant Professor

I am an econometrician at the NYU department of Economics. My research interests are in microeconometrics of panel data and applied microeconomics. I also have an interest in financial econometrics. 

I obtained my PhD from CEMFI under the supervision of Stephane Bonhomme and Enrique Sentana.

 

PUBLICATIONS

May 2019

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.

Supplementary Appendix

Files for replication

R package for the estimator

May 2015

Joint with Stephane Bonhomme
Econometrica

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.

Supplementary Appendix

Files for replication

R package for the estimator

August 2015

Joint with Yacine Ait-Sahalia and Dante Amengual
Journal of Econometrics

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.

 

WORKING PAPERS

August 2019
Joint with Stephane Bonhomme and Thibaut Lamadon.

Resubmitted to Econometrica

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-stepgrouped-fixedeffects 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

Supplementary Appendix

November 2016

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

Supplementary Appendix

June 2017

With  Francisco Penaranda and Enrique Sentana

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

November 2019

Joint with Tetsuya Kaji and Guillaume Pouliot

> ARTIFICIAL INTELLIGENCE FOR STRUCTURAL ESTIMATION

We propose a new estimation method for structural models using tools from Artificial Intelligence. The approach uses the availability of modern pattern recognition methods, discriminators, that can accurately distinguish between real data from data generated using a fully specified model. The estimator is defined as the value of the structural parameters for which the discriminator is unable to distinguish the true data from the corresponding generated data. Different models of discrimination define different estimators and we show that when using a logistic regression the estimator is asymptotically equivalent to the optimally weighted simulated method of moments. Sophisticated discriminators based on Neural Networks with multiple hidden layers provide more efficient estimators up to the extent that they nonparametrically estimate an oracle discriminator related to the Jensen Shannon distance. Using recent results on rates of convergence of multilayer networks we justify the superiority of using NN versus other types of basis functions in particular classes of functions of interest. The method can be combined with recent development in robust inference. We showcase the good properties of the proposed class of estimators using simulated data from a two-period Roy Model where individuals can choose to work between two locations in exchange of wages, individuals are heterogeneous in terms of their location-specific comparative advantage, and there are returns to experience. We also show the usefulness of our estimation approach in the context of understanding the saving patterns among the eldest in the U.S., based on DeNardi, French, Jones (2010).

>>> SLIDES

June 2019
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).

 

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