We document a substantial increase in downside risk to US economic growth over the last 30 years. By modelling secular trends and cyclical changes of the predictive density of GDP growth, we find an accelerating decline in the skewness of the conditional distributions, with significant,...

The paper compares one-period ahead forecasting performance of linear vector-autoregressive (VAR) models and single-equation Markov-switching (MS) models for two cases: when leading information is available and when it is not. The results show that single-equation MS models tend to perform...

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 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...

This study sheds new light on the question of whether or not sentiment surveys, and the expectations derived from them, are relevant to forecasting economic growth and stock returns, and whether they contain information that is orthogonal to macroeconomic and financial data. I examine 16...

We show that factor forecasting models deliver real-time gains over autoregressive models for US real activity variables during the recent period, but are less successful for nominal variables. The gains are largely due to the Financial Crisis period, and are primarily at the shortest (one...

The Great Recession and the subsequent period of subdued GDP growth in most advanced economies have highlighted the need for macroeconomic forecasters to account for sudden and deep recessions, periods of higher macroeconomic volatility, and fluctuations in trend GDP growth. In this paper, we...

This paper proposes a measure of real-time inflation expectations based on metadata, i.e., data about data, constructed from internet search queries performed on the search engine Google. The forecasting performance of the Google Inflation Search Index (GISI) is assessed relative to 37 other...

This paper studies the comparative predictive accuracy of forecasting methods using mixed-frequency data, as applied to nowcasting Philippine inflation, real GDP growth, and other related macroeconomic variables. It focuses on variations of mixed-frequency dynamic latent factor models (DFM for...

We develop a forecasting model of GDP growth that features regime-switching behavior and an error-correction mechanism (ECM). Regime changes are manifested in the behavior of a stochastic regime drift component that moves between expansionary and contractionary phases, thus generating cycles in...