We develop early warning models for financial crisis prediction using machine learning techniques on macrofinancial data for 17 countries over 1870–2016. Machine learning models mostly outperform logistic regression in out-of-sample predictions and forecasting. We identify economic drivers of...

We study the implications of multi-period mortgage loans for monetary policy, considering several realistic modifications — fixed interest rate contracts, lower bound constraint on newly granted loans, and possibility for the collateral constraint to become slack — to an otherwise standard...

We develop a dynamic stochastic general equilibrium framework that can account for important macroeconomic and financial moments, given Epstein-Zin preferences, heterogeneous banking and third-order approximation methods that yield a time-varying term premium that feeds back to the real economy....

We build a semi-structural New Keynesian model with financial frictions to study the drivers of macroeconomic tail risk (‘GDP-at-Risk’). We analyse the empirically observed fat left tail of the GDP distribution by modelling three key non-linearities emphasised in the literature: 1) an...