I am a Ph.D. student in Economics at NYU. Recently, I have been working with machine learning and economic forecasting, but I am interested in Econometrics as a broad field.
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Email: matheus at nyu dot edu
Fields: Econometrics, Applied Microeconomics
Job market paper: Predictive Inference in a Wide Class of Temporal Data
Forecasters usually report point predictions; however, understanding the randomness around such values is of practical importance. An example: a central bank predicts 2% inflation next quarter but is also interested in an interval (0%-4%, for example) that will contain the future realization of this series with a set probability. I show how to construct intervals as such, and I prove their asymptotic validity. I propose a model free method that encompasses, but not limited to, any off-the-shelf machine-learning method including high-dimensional ones. The method is based on a subsampling estimation strategy, consisting of analysing smaller cuts of the original time series. I prove the prediction intervals constructed with the subsampling method remain valid even when the data exhibits nonstationarities of many kinds –- such as time-varying parameters, structural breaks, unit roots, and transitions between steady-states. In addition to this theoretical work, I provide simulation studies to show the numerical performance of this method. I also apply the method to a demand dataset and to the forecast of inflation in a high-dimensional setup. The subsampling procedure extends to allow for comparisons of predictive accuracy between different models.
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.