In this paper we introduce two general non-parametric first-order stationary time-series models for which marginal (invariant) and transition distributions are expressed as infinite-dimensional mixtures. That feature makes them the first Bayesian stationary fully non-parametric models developed...

The COVID-19 pandemic not only generated real shocks affecting economic activity severely, but also a broad uncertainty that unleashed an extreme shock to financial markets. In this paper, we focus on the financial dimension of the pandemic from the viewpoint of an emerging market economy....

We analyze the issue of the impact of multiple breaks on monetary neutrality results, using a long annual international data set. We empirically verify whether neutrality propositions remain addressable (and if so, whether they hold or not), when unit root tests are carried out allowing for...

With the purpose of measuring and monitoring systemic risk, some topological properties of the interbank exposures and the payments system networks are studied. We propose non-topological measures which are useful to describe the individual behavior of banks in both networks. The evolution of...

We explore the dynamics of inflation, inflation expectations, and seigniorage-financed fiscal deficits in Mexico. To do so, we estimate the model in Sargent, Williams, and Zha (2009) using Mexican CPI inflation data. This model features dual expected inflation equilibriums and regime switching...

This paper introduces the random discounted expected utility (RDEU) model, which we have developed as a means to deal with heterogeneous risk and time preferences. The RDEU model provides an explicit linkage between preference and choice heterogeneity. We prove it has solid comparative statics,...

The COVID-19 pandemic caused severe losses in the world economy, particularly in emerging market economies (EMEs), which had to withstand the challenges of the pandemic and its domestic repercussions while also facing the deterioration of external demand. Using a panel data model, we estimate...

We are interested in forecasting bankruptcies in a probabilistic way. Specifically, we compare the classification performance of several statistical and machine-learning techniques, namely discriminant analysis (Altman's Z-score), logistic regression, least-squares support vector machines and...