My fields of research are econometrics, network econometrics, and machine learning.

**Network Dependence and Inference**

This paper considers the problem of making valid inferences with network data. In general, data is dependent when the underlying entities form a network; however, the exact nature of the relationship between network topology and data dependence is not clear. Accounting for dependence between network entity attributes is instrumental for valid inferences, but this has received little formal treatment and tends to be neglected in practice. The main contribution of this paper is to provide a formal framework for handling the dependence of network data, giving rise to new ways of making inferences. The proposed estimators can be easily implemented in practice and require a single network observation. Laws of large numbers, consistency of autocovariance function estimators, and a central limit theorem are proven. A number of theoretical and empirical applications of the provided framework are presented.

**A Pearson Statistic for Conditional Distribution Model Checking** (with Miguel A. Delgado)

We propose a cross-classification rule for the dependent and explanatory variables resulting in a contingency table such that the classical trinity of chi-square statistics can be used to check for conditional distribution specification. The resulting Pearson statistic is equal to the Lagrange multiplier statistic. We also provide a Chernoff-Lehmann result for the Pearson statistic using the raw data maximum likelihood estimator, which is applied to show that the corresponding limiting distribution of the Wald statistic does not depend on the number of parameters. The asymptotic distribution of the proposed statistics does not change when the grouping is data dependent. An algorithm allowing to control the number of observations per cell is developed. Monte Carlo experiments provide evidence of the excellent size accuracy of the proposed tests and their good power performance, compared to omnibus tests, in high dimensions.

**Nonparametric Estimation of Low-Rank Graphons** (with László Györfi and Gábor Lugosi)

**Network Dependence Counterfactuals and Their Estimation Using Machine Learning**

This paper introduces a new concept of network dependence counterfactuals. While counterfactual measures of outcome variables are well-understood, counterfactual dependence between outcome variables has not been considered. In this paper we exploit the network stationarity assumption allowing to estimate, given a single network observation, the counterfactual covariance between outcome variables of any two network entities under a given hypothetical network structure. We further propose a more flexible estimation procedure using machine learning algorithms. As an application, we suggest a series of vertex and edge influence and network robustness measures and illustrate them on microfinance data from Indian villages.

**University of Cambridge**

**Graduate**

**Universidad Carlos III de Madrid**

**Graduate**

Preliminary Statistics

Econometrics II

Game theory

**Undergraduate**

Econometrics

Applied Time Series Econometrics with R

Quantitative Macroeconomics