Marcel Ribeiro

I am a Lecturer at Sao Paulo School of Economics - FGV and a joint Associated Editor of the Brazilian Review of Econometrics .

My primary research interests are in macroeconomics and monetary economics.

Contact information:

Sao Paulo School of Economics - FGV

Rua Itapeva, 474

Sao Paulo, Brazil, 01332-000

Email: marcel.ribeiro@fgv.br

CV (pdf); CV Lattes

IDEAS Profile; Google Scholar Profile

Working papers

Endogenous information and expectations in macroeconomics: Implications of strategic uncertainty (new version!)

Awarded Best Applied Macro Paper, SBE Prize 2018

Inflation target expectations, transparency and monetary policy

Publications

A model of the confidence channel of fiscal policy (with Caio Machado and Bernardo Guimaraes), Journal of Money, Credit and Banking, 2016


Publications in Brazilian Journals

New Tools for the CGE Analysis of PTAs in the era of Non-Tariff Barriers and Global Value Chains: The case of Mercosur and China (with Lucas Ferraz), Revista Brasileira de Economia, 2018.

On the effects of non-tariff measures on Brazilian exports (with Lucas Ferraz and Pedro Monasterio), Revista Brasileira de Economia, 2017.

Book Chapters

Comparative Advantage and the Uneven Effects of Non-Tariff Measures (with Lucas Ferraz and Marcos Ritel), in Non-Tariff Measures: Economic Assessment and Policy Options for Development, UNCTAD, 2018.

Work in progress

Solution for linear rational expectations models with imperfect common knowledge

This paper develops a novel solution method for a general class of DSGE models with imperfect common knowledge. The main contribution is that the method allows for the inclusion of endogenous state variables into the system of linear rational expectations conditions under imperfect common knowledge. The method also allows for a rich set of exogenous noisy public and private signals and current and lagged endogenous variables into the agents' information sets. One key implication is that the endogenous persistence of state variables is the same under full information and imperfect common knowledge. This method offers a tractable laboratory that pushes the quantitative frontier for imperfect and dispersed information models by allowing to introduce such informational structure into medium-scale DSGE models.