Central banks

We provide a versatile nowcasting toolbox that supports three model classes (dynamic factor models, large Bayesian VAR, bridge equations) and offers methods to manage data selection and adjust for Covid-19 observations. The toolbox aims at simplifying two key tasks: creating new nowcasting models and improving the policy analysis. For model creation, the toolbox automatizes testing input variables, assessing model accuracy, and checking robustness to the Covid period.

Negative economic shocks can cause waves of investor pessimism about the resilience of banks, which, in turn, generate additional adverse macroeconomic effects. This is commonly cited as an explanation for the economic havoc wrought by the global financial crisis of 2007-08. We introduce the notion of pessimism in a real business cycle model, which is a standard framework for business cycle analysis. The possibility of waves of pessimism generates countercyclical demand from banks for liquid assets (e.g., bank reserves).

This paper demonstrates that empirically grounding the discount factor significantly influences the determination of the carbon price. Using two complementary nonlinear statistical approaches, we assess which utility formulations and corresponding stochastic discount factors best align with U.S. data. We provide evidence that habit formation is essential for capturing the time variation in the stochastic discount factor necessary to match the data. This increased time variation raises the carbon price by 32% and makes it five times more procyclical compared to standard models.

Is the burden of distress in the banking sector shared equally among households, or is it distributed unevenly? Following the global financial crisis, the economic consequences of severe disruptions to the banking sector and the unequal impact of recessions have become a key concern of macroeconomic policy. This article examines how temporary banking sector losses affect households differently according to their income levels. The analysis reveals that low-income households bear most of the burden, while high-income households tend to be less adversely affected.