Search

The COVID-19 pandemic has led to enormous movements in economic data that strongly affect parameters and forecasts obtained from standard VARs. One way to address these issues is to model extreme observations as random shifts in the stochastic volatility (SV) of VAR residuals. Specifically, we propose VAR models with outlier-augmented SV that combine transitory and persistent changes in volatility. The resulting density forecasts for the COVID-19 period are much less sensitive to outliers in the data than standard VARs. Evaluating forecast performance over the last few decades, we find that outlier-augmented SV schemes do at least as well as a conventional SV model. Predictive Bayes factors indicate that our outlieraugmented SV model provides the best data fit for the period since the pandemic's outbreak, as well as for earlier subsamples of relatively high volatility. [ABSTRACT FROM AUTHOR]

Government debt and forecasts thereof attracted considerable attention during the recent financial crisis. The current paper analyzes potential biases in different U.S. government agencies' one-year-ahead forecasts of U.S. gross federal debt over 1984--2012. Standard tests typically fail to detect biases in these forecasts. However, impulse indicator saturation (IIS) detects economically large and highly significant time-varying biases, particularly at turning points in the business cycle. These biases do not appear to be politically related. IIS defines a generic procedure for examining forecast properties; it explains why standard tests fail to detect bias; and it provides a mechanism for potentially improving forecasts. [ABSTRACT FROM AUTHOR]

Stekler and Symington (2016) construct indexes that quantify the Federal Open Market Committee's views about the U.S. economy, as expressed in the minutes of the FOMC's meetings. These indexes provide insights on the FOMC's deliberations, especially at the onset of the Great Recession. The current paper complements Stekler and Symington's analysis by showing that their indexes reveal relatively minor bias in the FOMC's views when the indexes are reinterpreted as forecasts. Additionally, these indexes provide a proximate mechanism for inferring the Fed staff's Greenbook forecasts of the U.S. real GDP growth rate, years before the Greenbook's public release. [ABSTRACT FROM AUTHOR]