About

Marcelo Cunha Medeiros is a distinguished professor at the University of Illinois, holding the Jorge Paulo Lemann Distinguished Chair in Economics. He serves as the Assistant Head for Faculty Development in the Economics department and is also a professor in the fields of Economics and Finance. Additionally, he is affiliated with the Center for Latin American and Caribbean Studies. Medeiros's research expertise centers on advanced econometric and statistical methods, particularly in time series modeling, autoregressive models, and nonlinear models. His work extensively covers topics such as realized volatility, factor models, and generalized autoregressive conditional heteroscedasticity (GARCH). He has contributed significantly to the development of shrinkage estimators and model selection techniques, with a focus on applications in economics and finance. Medeiros's scholarly output includes numerous peer-reviewed articles that explore the intersection of econometrics, forecasting, and applied economics, demonstrating a strong emphasis on methodological innovation and empirical analysis.

Research topics

• Computer Science
• Artificial Intelligence
• Statistics
• Mathematics
• Econometrics
• Economics
• Psychology
• Social psychology
• Algorithm

Selected publications

• A sorted penalty estimator: Inference for a correlation-robust shrinkage method

  Journal of Econometrics · 2026-03-19

  article1st author
  PublisherDOI

• A Sorted Penalty Estimator: Inference for a Correlation-Robust Shrinkage Method

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• A Sorted Penalty Estimator: Inference for a Correlation-Robust Shrinkage Method

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Time Series Embedding and Combination of Forecasts: A Reinforcement Learning Approach

  ArXiv.org · 2025-08-28

  preprintOpen access1st authorCorresponding

  The forecasting combination puzzle is a well-known phenomenon in forecasting literature, stressing the challenge of outperforming the simple average when aggregating forecasts from diverse methods. This study proposes a Reinforcement Learning - based framework as a dynamic model selection approach to address this puzzle. Our framework is evaluated through extensive forecasting exercises using simulated and real data. Specifically, we analyze the M4 Competition dataset and the Survey of Professional Forecasters (SPF). This research introduces an adaptable methodology for selecting and combining forecasts under uncertainty, offering a promising advancement in resolving the forecasting combination puzzle.

  PublisherOA PDFDOI

• Counterfactual Imputation: Comments on “Imputation of Counterfactual Outcomes when the Errors are Predictable” by Silvia Gonçalves and Serena Ng

  Journal of Business and Economic Statistics · 2024-09-20

  article1st authorCorresponding

  The measurement of treatment (intervention) effects on a single (or just a few) treated unit(s) based on counterfactuals constructed from artificial controls has become a popular practice in applied statistics and economics since the proposal of the synthetic control method. However, most of the literature has ignored the time-series properties of the data. The work of Gonçalves and Ng fills this gap by proposing a simple correction for existing estimators to take into account serial and cross-correlation in the data. This note provides some thoughts on Gonçalves and Ng’s method.

  PublisherDOI

• Forecasting inflation using disaggregates and machine learning

  arXiv (Cornell University) · 2023-08-22 · 3 citations

  preprintOpen accessSenior author

  This paper examines the effectiveness of several forecasting methods for predicting inflation, focusing on aggregating disaggregated forecasts - also known in the literature as the bottom-up approach. Taking the Brazilian case as an application, we consider different disaggregation levels for inflation and employ a range of traditional time series techniques as well as linear and nonlinear machine learning (ML) models to deal with a larger number of predictors. For many forecast horizons, the aggregation of disaggregated forecasts performs just as well survey-based expectations and models that generate forecasts using the aggregate directly. Overall, ML methods outperform traditional time series models in predictive accuracy, with outstanding performance in forecasting disaggregates. Our results reinforce the benefits of using models in a data-rich environment for inflation forecasting, including aggregating disaggregated forecasts from ML techniques, mainly during volatile periods. Starting from the COVID-19 pandemic, the random forest model based on both aggregate and disaggregated inflation achieves remarkable predictive performance at intermediate and longer horizons.

  PublisherOA PDFDOI

• Forecasting Large Realized Covariance Matrices: The Benefits of Factor Models and Shrinkage

  arXiv (Cornell University) · 2023-03-22 · 4 citations

  preprintOpen access

  We propose a model to forecast large realized covariance matrices of returns, applying it to the constituents of the S\&P 500 daily. To address the curse of dimensionality, we decompose the return covariance matrix using standard firm-level factors (e.g., size, value, and profitability) and use sectoral restrictions in the residual covariance matrix. This restricted model is then estimated using vector heterogeneous autoregressive (VHAR) models with the least absolute shrinkage and selection operator (LASSO). Our methodology improves forecasting precision relative to standard benchmarks and leads to better estimates of minimum variance portfolios.

  PublisherOA PDFDOI

• Bridging factor and sparse models

  The Annals of Statistics · 2023-08-01 · 44 citations

  articleSenior author

  Factor and sparse models are widely used to impose a low-dimensional structure in high-dimensions. However, they are seemingly mutually exclusive. We propose a lifting method that combines the merits of these two models in a supervised learning methodology that allows for efficiently exploring all the information in high-dimensional datasets. The method is based on a flexible model for high-dimensional panel data with observable and/or latent common factors and idiosyncratic components. The model is called the factor-augmented regression model. It includes principal components and sparse regression as specific models, significantly weakens the cross-sectional dependence, and facilitates model selection and interpretability. The method consists of several steps and a novel test for (partial) covariance structure in high dimensions to infer the remaining cross-section dependence at each step. We develop the theory for the model and demonstrate the validity of the multiplier bootstrap for testing a high-dimensional (partial) covariance structure. A simulation study and applications support the theory.

  PublisherDOI

• Modeling the evolution of deaths from infectious diseases with functional data models: The case of COVID‐19 in Brazil

  Statistics in Medicine · 2023-01-11 · 6 citations

  articleOpen accessSenior author

  In this paper, we apply statistical methods for functional data to explore the heterogeneity in the registered number of deaths of COVID-19, over time. The cumulative daily number of deaths in regions across Brazil is treated as continuous curves (functional data). The first stage of the analysis applies clustering methods for functional data to identify and describe potential heterogeneity in the curves and their functional derivatives. The estimated clusters are labeled with different "levels of alert" to identify cities in a possible critical situation. In the second stage of the analysis, we apply a functional quantile regression model for the death curves to explore the associations with functional rates of vaccination and stringency and also with several scalar geographical, socioeconomic and demographic covariates. The proposed model gave a better curve fit at different levels of the cumulative number of deaths when compared to a functional regression model based on ordinary least squares. Our results add to the understanding of the development of COVID-19 death counts.

  PublisherOA PDFDOI

• Forecasting Large Realized Covariance Matrices: The Benefits of Factor Models and Shrinkage

  Journal of Financial Econometrics · 2023-05-11 · 9 citations

  article

  Abstract We propose a model to forecast large realized covariance matrices of returns, applying it to the constituents of the S&P 500 daily. To address the curse of dimensionality, we decompose the return covariance matrix using standard firm-level factors (e.g., size, value, and profitability) and use sectoral restrictions in the residual covariance matrix. This restricted model is then estimated using vector heterogeneous autoregressive models with the least absolute shrinkage and selection operator. Our methodology improves forecasting precision relative to standard benchmarks and leads to better estimates of minimum variance portfolios.

  PublisherDOI

Frequent coauthors

• Michael McAleer

  Tinbergen Institute

  49 shared

• Ricardo Masini

  University of California, Davis

  42 shared

• Álvaro Veiga

  Pontifical Catholic University of Rio de Janeiro

  26 shared

• Eduardo Mendes

  Institut polytechnique de Grenoble

  22 shared

• Márcio Garcia

  Pontifical Catholic University of Rio de Janeiro

  17 shared

• Gabriel Vasconcelos

  Brazilian Development Bank

  17 shared

• Eric Hillebrand

  Lancaster University

  13 shared

• Carlos E. Pedreira

  Universidade Federal do Rio de Janeiro

  10 shared

Education

• PhD, Electrical Engineering

  Pontifícia Universidade Católica do Rio de Janeiro

  2000

• Master of Science, Electrical Engineering

  Pontifícia Universidade Católica do Rio de Janeiro

  1998

• Bachelor, Electrical Engineering

  Pontifícia Universidade Católica do Rio de Janeiro

  1996

Awards & honors

• Elected Fellow of the Society for Financial Econometrics (So…
• Associate Editor for the Journal of Financial Econometrics
• Associate Editor for the Quarterly Review of Economics and F…
• Associate Editor for the Journal of the American Statistical…
• Member of the editorial board of the Annals Financial Econom…