Danilo Leiva-Leon


Model Averaging in Markov-Switching Models: Predicting National Recessions with Regional Data

Staff Working Paper 2015-24 Pierre Guérin, Danilo Leiva-Leon
This paper introduces new weighting schemes for model averaging when one is interested in combining discrete forecasts from competing Markov-switching models. In particular, we extend two existing classes of combination schemes – Bayesian (static) model averaging and dynamic model averaging – so as to explicitly reflect the objective of forecasting a discrete outcome.

The Propagation of Industrial Business Cycles

Staff Working Paper 2014-48 Maximo Camacho, Danilo Leiva-Leon
This paper examines the business cycle linkages that propagate industry-specific business cycle shocks throughout the economy in a way that (sometimes) generates aggregated cycles. The transmission of sectoral business cycles is modelled through a multivariate Markov-switching model, which is estimated by Gibbs sampling.

Real-Time Nowcasting of Nominal GDP Under Structural Breaks

Staff Working Paper 2014-39 William A. Barnett, Marcelle Chauvet, Danilo Leiva-Leon
This paper provides a framework for the early assessment of current U.S. nominal GDP growth, which has been considered a potential new monetary policy target. The nowcasts are computed using the exact amount of information that policy-makers have available at the time predictions are made. However, real-time information arrives at different frequencies and asynchronously, which poses challenges of mixed frequencies, missing data and ragged edges.

A New Approach to Infer Changes in the Synchronization of Business Cycle Phases

Staff Working Paper 2014-38 Danilo Leiva-Leon
This paper proposes a Markov-switching framework to endogenously identify the following: (1) regimes where economies synchronously enter recessionary and expansionary phases; and (2) regimes where economies are unsynchronized, essentially following independent business cycles.

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