We study how the output gap affects potential output over time-i.e., the dynamic hysteresis effect. To do so, we introduce novel unobserved components (UC) models that consider hysteresis as a sequence of lagged effects, thus separating the long-run recession-induced adverse effects from other...

We demonstrate the use of a Naive Bayes model as a recession forecasting tool. The approach has a close connection to Markov-switching models and logistic regression but also important differences. In contrast to Markov-switching models, Naive Bayes treats National Bureau of Economic Research...

Macroeconomic forecasting in recessions is not easy due to the inherent asymmetry of business cycle phases and the increased uncertainty about the future path of the teetering economy. I propose a mixed-frequency threshold vector autoregressive model with common stochastic volatility in mean...

The recent observed decline of business cycle variability suggests that broad macroeconomic risk may have fallen as well. This may in turn have some impact on equity risk premia. We investigate the latent structures in the volatilities of the business cycle and stock market valuations by...

In this paper we investigate whether the dynamic properties of the U.S. business cycle have changed in the last fifty years. For this purpose we develop a flexible business cycle indicator that is constructed from a moderate set of macroeconomic time series. The coincident economic indicator is...

We leverage a data rich environment to construct and study a measure of macroeconomic uncertainty for the Korean economy. We provide several stylized facts about uncertainty in Korea from 1991M10-2016M5. We compare and contrast this measure of uncertainty with two other popular uncertainty...

This paper sheds light on the impact of global macroeconomic uncertainty on the euro area economy. We build on the methodology proposed by Jurado et al. (2015) and estimate global as well as country-specific measures of economic uncertainty for fifteen key euro area trade partners and the euro...

This paper shows how non-linear DSGE models with potential non-normal shocks can be estimated by Quasi-Maximum Likelihood based on the Central Difference Kalman Filter (CDKF). The advantage of this estimator is that evaluating the quasi log-likelihood function only takes a fraction of a second....

This paper improves the accuracy and speed of particle filtering for non-linear DSGE models with potentially non-normal shocks. This is done by introducing a new proposal distribution which i) incorporates information from new observables and ii) has a small optimization step that minimizes the...

We propose a novel method to estimate dynamic equilibrium models with stochastic volatility. First, we characterize the properties of the solution to this class of models. Second, we take advantage of the results about the structure of the solution to build a sequential Monte Carlo algorithm to...