7 citations · 13 across the 8 of their papers we have counts for
9 papers
Balancing Flexibility and Interpretability: A Conditional Linear Model Estimation via Random Forest
Ricardo Masini, Marcelo Medeiros
Traditional parametric econometric models often rely on rigid functional forms, while nonparametric techniques, despite their flexibility, frequently lack interpretability. This pa…
Higher-order Refinements of Small Bandwidth Asymptotics for Density-Weighted Average Derivative Estimators
Matias D. Cattaneo, Max H. Farrell, Michael Jansson +1
The density weighted average derivative (DWAD) of a regression function is a canonical parameter of interest in economics. Classical first-order large sample distribution theory fo…
Yurinskii's Coupling for Martingales
Matias D. Cattaneo, Ricardo P. Masini, William G. Underwood
Yurinskii's coupling is a popular theoretical tool for non-asymptotic distributional analysis in mathematical statistics and applied probability, offering a Gaussian strong approxi…
Distributional Counterfactual Analysis in High-Dimensional Setup
Ricardo Masini
In the context of treatment effect estimation, this paper proposes a new methodology to recover the counterfactual distribution when there is a single (or a few) treated unit and p…
Bridging factor and sparse models
Jianqing Fan, Ricardo Masini, Marcelo C. Medeiros
Factor and sparse models are two widely used methods to impose a low-dimensional structure in high-dimensions. However, they are seemingly mutually exclusive. We propose a lifting…
Machine Learning Advances for Time Series Forecasting
Ricardo P. Masini, Marcelo C. Medeiros, Eduardo F. Mendes
In this paper we survey the most recent advances in supervised machine learning and high-dimensional models for time series forecasting. We consider both linear and nonlinear alter…
Do We Exploit all Information for Counterfactual Analysis? Benefits of Factor Models and Idiosyncratic Correction
Jianqing Fan, Ricardo P. Masini, Marcelo C. Medeiros
Optimal pricing, i.e., determining the price level that maximizes profit or revenue of a given product, is a vital task for the retail industry. To select such a quantity, one need…
Regularized Estimation of High-Dimensional Vector AutoRegressions with Weakly Dependent Innovations
Ricardo P. Masini, Marcelo C. Medeiros, Eduardo F. Mendes
There has been considerable advance in understanding the properties of sparse regularization procedures in high-dimensional models. In time series context, it is mostly restricted…
Constrained Polynomial Likelihood
Caio Almeida, Ricardo Masini, Paul Schneider
We develop a non-negative polynomial minimum-norm likelihood ratio (PLR) of two distributions of which only moments are known. The sample PLR converges to the unknown population PL…