We examine ways to describe the maturity structure of public debts using few parameters. We compile a novel data set of all promised future payments for U.S. and UK government debt from every month since 1869, and more recently for Peru, Poland, Egypt, and Nigeria. We show there is a unique parametric form which does not arbitrarily restrict debt issuance. We use this model to parsimoniously describe the evolution of public debt maturities and to characterize the relationship between maturity and the term structure of interest rates in the United States since 1940.
The U.S. Survey of Professional Forecasters produces precise and timely point forecasts for key macro-economic variables. However, the accompanying density forecasts are mostly conducted for “fixed events.” For example, in each quarter, panelists predict output growth and inflation for the current calendar year and the next, hence the forecast horizon changes with each survey round. This limits the forecasts' usefulness to policymakers, researchers, and market participants. We propose a density combination approach that weights fixed-event density forecasts, aiming at obtaining a correctly calibrated fixed-horizon density forecast. We show that our method produces competitive density forecasts relative to widely used alternatives.
This paper investigates the role of credit market sentiment and investor beliefs in credit cycle dynamics and their transmission to businesscycle fluctuations. Using U.S. data from 1968 to 2014, we find that credit market sentiment is indeed able to detect asymmetries in a small-scale macroeconomic model. An unexpected credit market sentiment shock has different impacts in an optimistic and pessimistic credit market environment. While an unexpected movement in the optimistic regime leads to a rather muted impact on output and credit, we find a significant negative impact on these variables in the pessimistic regime. The findings highlight the relevance of expectation formation mechanisms as a source of macroeconomic instability.
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