This paper focuses on a number of newly proposed on-line forecast combination algorithms in Sancetta (2010), Yang (2004), and Wei and Yang (2012). We first establish certain asymptotic properties of these algorithms and compare them with the Bates and Granger (1969) method. We then show that...

We develop a non-linear forecast combination rule based on copulas that incorporate the dynamic interaction between individual predictors. This approach is optimal in the sense that the resulting combined forecast produces the highest discriminatory power as measured by the receiver operating...

We propose autocorrelation-robust asymptotic variances of the Brier score and Brier skill score, which are generally applicable in circumstances with weak serial correlation. An empirical application in macroeconomics underscores the importance of taking care of serial correlation. We find that...

Binary events are involved in many economic decision problems. In recent years, considerable progress has been made in diverse disciplines in developing models for forecasting binary outcomes. We distinguish between two types of forecasts for binary events that are generally obtained as the...

While the yield spread has long been recognized as a good predictor of recessions, it seems to have been largely overlooked by professional forecasters. We examine this puzzle, established by Rudebusch and Williams (2009), in a data-rich environment including not just the yield spread but many...

We study the role of consumer confidence in forecasting real personal consumption expenditure, and contribute to the extant literature in three substantive ways: First, we reexamine existing empirical models of consumption and consumer confidence not only at the quarterly frequency, but using...

This paper evaluates the performance of a few newly proposed on-line forecast combination algorithms, and compares them with some of the existing ones including the simple average and that of Bates and Granger (1969). We derive asymptotic results for the new algorithms that justify certain...

We forecast New York state tax revenues with a mixed-frequency model using a number of machine learning techniques. We found boosting with two dynamic factors extracted from a select list of New York and U.S. leading indicators did best in terms of correctly updating revenues for the fiscal year...

We examine forecast accuracy and efficiency of the Social Security Administration's projections for cost rate, trust fund balance, trust fund ratio made during 1980-2020 with horizons up to 95 years. We find that the reported deterioration in the accuracy of the forecasts during 2010’s has...