with Todd E. Clark (FRB Cleveland), and Gergely Ganics (Banco de España)

• https://doi.org/10.1162/rest_a_01565 

• latest draft: pdf and online appendix: pdf

• Slides: pdf

• replication code: https://github.com/elmarmertens/ClarkGanicsMertensSPFfancharts and REStat Dataverse

Abstract: We develop models that take point forecasts from the Survey of Professional Forecasters (SPF) as inputs and produce estimates of survey-consistent term structures of expectations and uncertainty at arbitrary forecast horizons. Our models combine fixed-horizon and fixed-event forecasts, accommodating time-varying horizons and availability of survey data, as well as potential inefficiencies in survey forecasts. The estimated term structures of SPF-consistent expectations are comparable in quality to the published, widely used short-horizon forecasts. Our estimates of time-varying forecast uncertainty reflect historical variations in realized errors of SPF point forecasts, and generate fan charts with reliable coverage rates.

Parts of this paper were earlier circulated under the title “Constructing the Term Structure of Uncertainty from the Ragged Edge of SPF Forecasts.”

with Andrea Carriero (Queen Mary University of London, U Bologna), Todd Clark (Federal Reserve Bank of Cleveland), Massimiliano Marcellino (Bocconi, IGIER and CEPR)

• draft: pdf 

• supplementary appendix: pdf

• draft and supplement at QE: https://www.econometricsociety.org/publications/quantitative-economics/forthcoming-papers

• slides: pdf (updated version of our presentation at NBER SI 2023)

• earlier Bundesbank DP at IDEAS

• earlier Cleveland Fed WP version: https://doi.org/10.26509/frbc-wp-202109

• replication code (github) and https://doi.org/10.5281/zenodo.14814807

Abstract: Vector autoregressions (VARs) are popular for forecasting, but ill-suited to handle occasionally binding constraints, like the effective lower bound on nominal interest rates. We examine reduced-form ``shadow-rate VARs'' that model interest rates as censored observations of a latent shadow-rate process and develop an efficient Bayesian estimation algorithm that accommodates large models.  When compared to a standard VAR, our better-performing shadow-rate VARs generate superior predictions for interest rates and broadly similar predictions for macroeconomic variables.  We obtain this result for shadow-rate VARs in which the federal funds rate is the only interest rate and in models including additional interest rates. Our shadow-rate VARs also deliver notable gains in forecast accuracy relative to a VAR that omits shorter-term interest rate data in order to avoid modeling the lower bound.

Earlier versions of this paper were also earlier circulated under the title “Shadow-Rate VARs.”

with Todd E. Clark (Federal Reserve Bank of Cleveland)

• Chapter for the  Handbook of Research Methods and Applications on Macroeconomic Forecasting  edited by Mike Clements and Ana Galvao, 2024, Edward Elgar Publishing Ltd. 

• Edward Elgar weblink 

Abstract: In recent decades, the collection of survey expectations for macroeconomic variables has gained considerable attention. A bourgeoning literature has developed that studies not only the predictive content of survey data, but also its usefulness in testing economic theories about the behavior of forward-looking decision makers under uncertainty. Leading examples of economic survey data include surveys of experts (including professional forecasters and financial market participants), households and firms.  Point forecasts of professional forecasters have, on balance, emerged as competitive (albeit not fully optimal) predictors of future outcomes. Meanwhile, ex-ante measures of uncertainty derived from probabilistic surveys have been found to be systematically at variance with the distribution of ex-post outcomes. Nevertheless, puzzles remain and the analysis and design of expectation surveys is an active field of research.