We provide novel evidence that technological news and uncertainty shocks, identified one at a time using VAR models as in the literature, are correlated; that is, they are not truly structural. We then proceed by proposing an identification scheme to disentangle the effects of news and financial...

What are the effects of beliefs, sentiment, and uncertainty, over the business cycle? To answer this question, we develop a behavioral New Keynesian macroeconomic model, in which we relax the assumption of rational expectations. Agents are, instead, boundedly rational: they have a...

We introduce a structural quantile vector autoregressive (VAR) model. Unlike standard VAR which models only the average interaction of the endogenous variables, quantile VAR models their interaction at any quantile. We show how to estimate and forecast multivariate quantiles within a recursive...

This paper constructs internationally consistent measures of macroeconomic uncertainty. Our econometric framework extracts uncertainty from revisions in data obtained from standardized national accounts. Applying our model to quarterly post-WWII real-time data, we estimate macroeconomic...

This paper presents a high-frequency structural VAR framework for identifying oil price shocks and examining their uncertainty transmission in the U.S. macroeconomy and financial markets. Leveraging the stylized features of financial data - specifically, volatility clustering effectively...

The concept of cointegration (see e.g., Engle and Granger, 1987; Johansen, 1988) has extensively been used to model equilibrium relationships (see e.g., Johansen and Juselius, 1990; Ericsson, 1998). The links between economic and econometric concepts are now well understood and they have become part of...

This paper compares the forecasting performance of linear and nonlinear models under the presence of structural breaks for the Brazilian real GDP growth. The Markov-switching models proposed by Hamilton (1989) and its generalized version proposed by Lam (1991) are applied to quarterly GDP from...

The paper questions the reasonability of using forecast error variance decompositions for assessing the role of different structural shocks in business cycle fluctuations. It is shown that the forecast error variance decomposition is related to a dubious definition of the business cycle. A...

The chapter deals with parametric models for the measurement of the business cycle in economic time series. It presents univariate methods based on parametric trend - cycle decompositions and multivariate models featuring a Phillips type relationship between the output gap and inflation and the...

To nowcast output gap turning points, probabilistic indicators are created from a simple and transparent machine-learning algorithm known as Learning Vector Quantization. The real-time ability of the indicators to quickly and accurately detect economic turning points in the United States and in...