I am a Ph.D. student in Economics at NYU. I am interested in different subfields of econometrics but I am particularly focused on the quantification of uncertainty around economic forecasts and the use of machine learning to construct such predictions.
Email: matheus at nyu dot edu
Some recent projects
Analyzing video game sales (Kaggle notebook, Link)
Exploring Brazilian conflicts (Kaggle notebook, Link)
The best Formula One drivers and constructors from 1996-2022 (Working paper version, R&R)
I use lap-by-lap data to calculate the probability of different drivers and constructors to win a Formula One season. I propose a fixed-effects functional regression to exploit the functional nature of lap time data, controlling for driver, constructor, circuit, and other factors. I combine this approach with a resampling strategy to calculate different podium probabilities and find that, all else equal, Lewis Hamilton is the best driver and Mercedes is the best constructor. I also conduct the simulation of a hypothetical “all star" season where the winners of the 1996-2022 seasons race against each other and find that Lewis Hamilton, Max Verstappen, and Nico Rosberg are the most likely to finish in the first three positions when controlling for other factors.
Forecasting the 2022 Brazilian Elections (Click here, with Mauricio Roza, in Portuguese)
We combine data from traditional election polls with Google Trends data to infer the percent of the population that would vote for each presidential candidate in the Brazilian election in a daily frequency. We do so by treating the polling average and trends data as signals of the vote share each candidate will receive on the election day. Our results point toward a significantly more competitive electoral race than traditional polls indicate.
Estimation of Multi-Unit Double Auctions (Link)
Estimating market power is an essential, if not complicated, task for market designers. This is rendered even harder when there are few sellers and few buyers in a given market. This research aims to allow for the distinction of market power from both the demand and supply sides simultaneously. I propose a bootstrap-based procedure to estimate multi-unit, two-sided auctions in which all agents can submit elastic price schedules. I use this methodology to estimate the private values of all agents in the Italian electricity market. The results suggest that buyers and sellers both have market power. An earlier version of this paper was awarded a student research prize and this paper is being expanded with Quang Vuong.
Solving and Estimating Finite-Time, Dynamic Discrete Choice Models with Deep Learning (Link)
Empirical evidence shows that deep learning performs well in situations where the curse of dimensionality is computationally prohibitive. This situation is common when solving finite-time, dynamic discrete choice models because they feature infinite dimension state-space, and also because of the nonstationary nature of these problems. I show how to approximate policy functions of those models with deep neural networks. These methods reduce the complexity of the problem, simplifying the computation process when closed-form solutions are not available.