Eduardo Zilberman

Macroeconomic Effects of Credit Deepening in Latin America

This paper augments a relatively standard dynamic general equilibrium model with financial frictions in order to quantify the macroeconomic effects of the credit deepening process observed in many Latin American (LA) countries in the last decade, most notably in Brazil. In the model, a stylized banking sector intermediates credit from patient households to impatient households and firms. The key novelty of the paper, motivated by the Brazilian experience, is to model the credit constraint faced by (impatient) households as a function of future labor income. In the calibrated model, credit deepening generates only modest abovetrend growth in consumption, investment, and GDP. Since Brazil has experienced one of the most intense credit deepening processes in Latin America, it is argued that the quantitative effects for other LA economies are unlikely to be sizeable.

Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods

Inflation forecasting is an important but difficult task. Here, we explore advances in machine learning (ML) methods and the availability of new datasets to forecast US inflation. Despite the skepticism in the previous literature, we show that ML models with a large number of covariates are systematically more accurate than the benchmarks. The ML method that deserves more attention is the random forest model, which dominates all other models. Its good performance is due not only to its specific method of variable selection but also the potential nonlinearities between past key macroeconomic variables and inflation.

Transitions in Central Bank Leadership

We assemble a novel dataset on transitions in central bank leadership in several countries, and study how monetary policy is conducted around those events. We find that policy is tighter both at the last meetings of departing governors and first meetings of incoming leaders. This finding cannot be fully explained by endogenous transitions, the effects of the zero lower bound, surges in inflation expectations, omitted variables such as fiscal policy and uncertainty nor electoral cycles. We conclude by offering two possible, perhaps complementary, explanations for these results. One based on a simple signalling story, another based on career and reputation concerns.

Gambling, Risk Appetite and Asset Pricing

A measure of the propensity to gamble in casinos constructed without any asset price data provides relevant information for asset pricing. This measure of risk appetite improves the fit of conditional asset pricing models such as the conditional CAPM, explains crosssectional differences in future returns for portfolios sorted on various characteristics, and helps forecast market and portfolio excess returns. The relationship between risk appetite and asset prices appears to be mainly explained by simultaneous changes in risk and risk premia.