In recognition of the severe consequences of the recent international financial crisis, the topic of macroprudential policy has elicited considerable research effort. The present study constructs, for 46 economies around the globe, an index of the capacity to deploy macroprudential policies....

How damaging are uncertainty shocks during extreme events such as the great recession and the Covid-19 outbreak? Can monetary policy limit output losses in such situations? We use a nonlinear VAR framework to document the large response of real activity to a financial uncertainty shock during...

How does the yield curve respond to a jump in financial uncertainty? We address this question by conducting a local projections analysis with US monthly data, period: 1962- 2018. The state-of-the-art financial uncertainty measure proposed by Ludvigson, Ma, and Ng (2019) is found to predict...

This survey features three parts. The first one covers the recent literature on domestic (i.e., country-specific) uncertainty and offers ten main takeaways. The second part reviews contributions on the fast-growing strand of the literature focusing on the macroeconomic effects of uncertainty...

Seasonality in macroeconomic time series can obscure movements of other components in a series that are operationally more important for economic and econometric analyses. Indeed, in practice one often prefers to work with seasonally adjusted data to assess the current state of the economy and...

Multivariate analysis can help to focus on economic phenomena, including trend and cyclical movements. To allow for potential correlation with seasonality, the present paper studies a three component multivariate unobserved component model, focusing on the case of quarterly data and showing that...

Economists typically use seasonally adjusted data in which the assumption is imposed that seasonality is uncorrelated with trend and cycle. The importance of this assumption has been highlighted by the Great Recession. The paper examines an unobserved components model that permits non-zero...

State space models play an important role in macroeconometric analysis and the Bayesian approach has been shown to have many advantages. This paper outlines recent developments in state space modelling applied to macroeconomics using Bayesian methods. We outline the directions of recent...

An updated version of our Markov-switching model of U.S. real GDP clearly suggests the COVID-19 recession was more U shaped than L shaped. As with linear time series models, it is important to account for extreme outliers during the pandemic, but a simple decay function for volatility from...

We show how random subspace methods can be adapted to estimating local projections with many controls. Random subspace methods have their roots in the machine learning literature and are implemented by averaging over regressions estimated over different combinations of subsets of these controls....