This paper analyzes the predictability of emerging market currency crises by comparing the often used probit model to a new method, namely a multi-layer perceptron artificial neural network (ANN) model. According to the results, both models were able to signal currency crises reasonably well...

Vector autoregressions (VARs) are flexible time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, their dense parameterization leads to unstable inference and inaccurate out-of sample forecasts, particularly for models with many variables....

This paper proposes a new methodology based on textual analysis to forecast U.S. recessions. Specifically, the paper develops an index in the spirit of Baker et al. (2016) and Caldara and Iacoviello (2018) which tracks developments in U.S. real activity. When used in a standard recession...

This paper proposes a new and robust methodology to obtain conditional density forecasts, based on information not contained in an initial econometric model. The methodology allows to condition on expected marginal densities for a selection of variables in the model, rather than just on future...

Despite being critical parameters in many economic fields, the received wisdom, in theoretical and empirical literatures, states that joint identification of the elasticity of capital-labor substitution and technical bias is infeasible. This paper challenges that pessimistic interpretation....

This paper describes a methodology to estimate the coefficients, to test specification hypotheses and to conduct policy exercises in multi-country VAR models with cross unit interdependencies, unit specific dynamics and time variations in the coefficients. The framework of analysis is Bayesian:...

Following the 2000 stockmarket crash, have US interest rates been held too low in relation to their natural level? Most likely, yes. Using a structural neo-Keynesian model, this paper attempts a real-time evaluation of the US monetary policy stance while ensuring consistency between the...

This paper applies linear and neural network-based “thick” models for forecasting inflation based on Phillips–curve formulations in the USA, Japan and the euro area. Thick models represent “trimmed mean” forecasts from several neural network models. They outperform the best performing...

This paper presents a set of probability density functions for Euribor outturns in three months' time, estimated from the prices of options on Euribor futures. It is the first official and freely available dataset to span the complete history of Euribor futures options, thus comprising over ten...