Araujo and Gaglianone (2023). Machine Learning Methods for Inflation Forecasting in Brazil: new contenders versus classical models. Latin American Journal of Central Banking, vol. 4(2), 100087.
Gaglianone, Giacomini, Issler and Skreta (2022). Incentive-driven Inattention. Journal of Econometrics, vol. 231(1), p. 188-212.
Vicente, Marins and Gaglianone (2022). Impacts of the Monetary Policy Committee Decisions on the Foreign Exchange Rate in Brazil. Brazilian Review of Finance, vol. 20(2), p. 1-19.
Costa, Ferreira, Gaglianone, Guillén, Issler and Lin (2021). Machine Learning and Oil Price Point and Density Forecasting. Energy Economics, vol. 102, 105494.
Duarte, Gaglianone, Guillén and Issler (2021). Commodity Prices and Global Economic Activity: A Derived-Demand Approach. Energy Economics, vol. 96, 105120.
Oliveira and Gaglianone (2020). Expectations Anchoring Indexes for Brazil using Kalman Filter: Exploring Signals of Inflation Anchoring in the Long Term. International Economics, vol. 163, p. 72-91.
Gaglianone, Guillén and Figueiredo (2018). Estimating Inflation Persistence by Quantile Autoregression with Quantile-Specific Unit Roots. Economic Modelling, vol. 73(C), p. 407-430.
Gaglianone, Issler and Matos (2017). Applying a Microfounded-Forecasting Approach to Predict Brazilian Inflation. Empirical Economics, vol. 53(1), p. 137-163.
Gaglianone and Marins (2017). Evaluation of Exchange Rate Point and Density Forecasts: An Application to Brazil. International Journal of Forecasting, vol. 33(3), p. 707-728.
Cordeiro, Gaglianone and Issler (2017). Inattention in Individual Expectations. EconomiA, vol. 18(1), p. 40-59.
Val, Klotzle, Pinto and Gaglianone (2017). Estimating the Credibility of Brazilian Monetary Policy using a Kalman Filter Approach. Research in International Business and Finance, vol. 41(C), p. 37-53.
Gaglianone and Lima (2014). Constructing Optimal Density Forecasts from Point Forecast Combinations. Journal of Applied Econometrics, vol. 29(5), p. 736-757.
Gaglianone and Lima (2012). Constructing Density Forecasts from Quantile Regressions. Journal of Money, Credit and Banking, vol. 44(8), p. 1589-1607.
Carrasco-Gutierrez and Gaglianone (2012). Evaluating Asset Pricing Models in a Simulated Multifactor Approach. Brazilian Review of Finance, vol. 10(4), p. 425-460.
Schechtman and Gaglianone (2012). Macro Stress Testing of Credit Risk Focused on the Tails. Journal of Financial Stability, vol. 8(3), p. 174-192.
Gaglianone, Lima, Linton and Smith (2011). Evaluating Value-at-Risk Models via Quantile Regression. Journal of Business & Economic Statistics, vol. 29(1), p. 150-160.
Lima, Gaglianone and Sampaio (2008). Debt Ceiling and Fiscal Sustainability in Brazil: a Quantile Autoregression Approach. Journal of Development Economics, vol. 86(2), p. 313-335.
Gaglianone and Pereira (2005). An Essay on the Foreign Exchange Rate Expectations in Brazil. Brazilian Review of Finance, vol. 3(1), p. 55-100.
Oliveira and Gaglianone (2020). Expectations Anchoring Indexes for Brazil Using Kalman Filter: Exploring Signals of Inflation Anchoring in the Long Term. In: Inflation Expectations, their Measurement and the Estimate of their Degree of Anchoring, Eds. Guarín, Melo and González, p.177-219, Joint Research Program - XXII Meeting of the Central Bank Researchers Network, Centro de Estudios Monetarios Latinoamericanos (CEMLA).
Pereira da Silva, Sales and Gaglianone (2013). Financial Stability in Brazil. In: Stability of The Financial System - Illusion or Feasible Concept?, Eds. Dombret, Andreas and Lucius, Otto, p.64-126, Edward Elgar Publishing.
Araújo and Gaglianone (2010). Survey-based Inflation Expectations in Brazil. In: Monetary Policy and the Measurement of Inflation: Prices, Wages and Expectations, Ed. Cecchetti, Stephen, vol. 49, p.107-114, Bank for International Settlements (BIS).
Blog Contributions (in Portuguese)
Risco de Inflação no Brasil (May 2024)
Métodos de Aprendizado de Máquina para Previsão de Inflação no Brasil (March 2024)