arXiv subjects

Federico Zincenko

Publications and source records attributed to Federico Zincenko.

4 recordsLinked to original sources

arXiv2026

Sensitivity Analysis for the Average Treatment Effect under Discrete Unobserved Confounders

We model unobserved confounding through an unknown finite number of latent types. This assumption induces finite-mixture representations of the treated and control outcome distributions. Using the identified mixture components, we characterize the sharp identified set for the number of latent types and derive the sharp identified set for the average treatment effect (ATE) corresponding to each admissible value, thereby providing a natural framework for sensitivity analysis. We further obtain a cutoff beyond which the identified set for the ATE coincides with a version of the Manski bounds, whereas below the cutoff it is strictly smaller. This cutoff grows only linearly with the numbers of mixture components in the treated and control groups, although the maximum admissible number of latent types grows quadratically. We also provide estimation and inference procedures with asymptotic guarantees and illustrate our methodology using LaLonde's data.

econ.EM↗

arXiv2023

Nonparametric estimation of conditional densities by generalized random forests

Considering a continuous random variable Y together with a continuous random vector X, I propose a nonparametric estimator f^(.|x) for the conditional density of Y given X=x. This estimator takes the form of an exponential series whose coefficients Tx = (Tx1,...,TxJ) are the solution of a system of nonlinear equations that depends on an estimator of the conditional expectation E[p(Y)|X=x], where p is a J-dimensional vector of basis functions. The distinguishing feature of the proposed estimator is that E[p(Y)|X=x] is estimated by generalized random forest (Athey, Tibshirani, and Wager, Annals of Statistics, 2019), targeting the heterogeneity of Tx across x. I show that f^(.|x) is uniformly consistent and asymptotically normal, allowing J to grow to infinity. I also provide a standard error formula to construct asymptotically valid confidence intervals. Results from Monte Carlo experiments are provided.

econ.EM↗

arXiv2021

Empirical Framework for Cournot Oligopoly with Private Information

We propose an empirical framework for asymmetric Cournot oligopoly with private information about variable costs. First, considering a linear demand for a homogenous product with a random intercept, we characterize the Bayesian Cournot-Nash equilibrium. Then we establish the identification of the joint distribution of demand and firm-specific cost distributions. Following the identification steps, we propose a likelihood-based estimation method and apply it to the global market for crude-oil and quantify the welfare effect of private information. We also consider extensions of the model to include either product differentiation, conduct parameters, nonlinear demand, or selective entry.

econ.GN↗

arXiv2014

Identification and Estimation of Multidimensional Screening

We study the identification and estimation of a multidimensional screening model, where a monopolist sells a multi-attribute product to consumers with private information about their multidimensional preferences. Under optimal screening, the seller designs product and payment rules that exclude "low-type" consumers, bunches the "medium types" at "medium-quality" products, and perfectly screens the "high types." Under the assumption that the cost function is quadratic and additively separable in products, we determine sufficient conditions to identify the joint distribution of preferences and the marginal costs from data on optimal individual choices and payments. Then, we propose estimators for these objects, establish their asymptotic properties, and assess their small-sample performance using Monte Carlo experiments.

econ.GN↗