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Monika Avila Márquez,
Ph.D. in Econometrics

University of Geneva

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Welcome to my website! I am an econometrician working in econometric analysis for panel data or longitudinal data and in the use of
machine learning methods in econometrics.

Research Interests

My research interests lie in the intersection of panel data econometrics and the use of machine
learning techniques in econometric analysis. In particular, I am interested in clustering for panel data. Additionally, I work on identification without
exclusion restrictions using machine learning techniques. 

Job Market Paper

Abstract: This paper investigates the identification and estimation of dynamic heterogeneous linear models for unbalanced panel data with known clustering structure and short time dimension (greater than or equal to 3). For this purpose, we use a linear multidimensional panel data model with additive cluster fixed effects and a mixed coefficient structure composed of cluster specific fixed effects and random cluster-individual-time specific effects. For estimation of the mean coefficients, we propose a Mean Cluster-FGLS estimator and a Mean Cluster-OLS estimator. In order to make feasible the GLS estimation of the cluster specific parameters, we introduce a ridge estimator of the variance-covariance matrix of the model. The Mean Cluster estimators are consistent: i) under stratified sampling when the number of clusters is fixed, the proportion of observed clusters is equal to 1 and the number of individuals per cluster grows to infinity or ii) under cluster sampling when the square root of the number of clusters grows at a slower rate than the growth rate of the number of individuals per cluster. In addition, we present two extensions of the baseline model. In the first one, we allow for cluster-individual specific fixed effects instead of cluster additive fixed effects. In this setting, we propose a Hierarchical Bayes estimator that takes into account the problem of unknown initial conditions. In the second extension, we allow for cross sectional dependence by including common factors. For estimation of this model, we propose the Mean Cluster estimator using the time demeaned variables. As an empirical application, we present the estimation of a value-added model of learning.

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