with Giovanni  Nicolò (Federal Reserve Board)

slides: pdf

Abstract: The post-pandemic increase in long-term Treasury yields raises questions about the level of the natural rate of interest, R-Star. Policymakers typically assess the stance of monetary policy relative to the natural rate of interest. However, R-Star is unobservable, and policymakers rely on real-time estimates generated from data on output, inflation, and other indicators.  We show that incorporating those estimates into the setting of monetary policy alters the systematic response to endogenous outcomes in ways that can violate the Taylor principle. Even when the policy rule is ``active'' in response to policymakers' real-time estimates of economic conditions, the effective policy rule---which arises from the effects of data on policymakers' real-time estimates---can violate the Taylor principle.  As a result, multiple equilibria may exist, and non-fundamental sunspot shocks can influence macroeconomic outcomes. A policy rule that responds only to observed outcomes, rather than estimates of latent variables like R-Star, would avoid such multiplicity.  

We consider a simple New Keynesian model in which policymakers observe aggregate output and inflation but not the underlying shocks to potential output and the supply side of the economy.  We model policymakers' estimates of R-Star as rational expectations that are consistent with equilibrium dynamics. While the true R-Star is driven only by shocks to potential output, policymakers' estimates of R-Star depend on observed data, which in turn leads to violations of the Taylor principle and multiple equilibria.  In this case, the resulting equilibrium is not unique. In addition, non-fundamental belief shocks distort policymakers’ ability to perfectly infer structural shocks---leading to imperfect inference about R-Star---and can contribute significantly to fluctuations in macroeconomic outcomes.  

We provide a unified framework in which policymakers believe that monetary policy is `active,'' while the policy response to actual data (instead of policymakers' real-time estimates) is consistent with multiple equilibrium outcomes. In this case, sunspot shocks emerge as important drivers of macroeconomic dynamics. Our framework integrates empirical results that find an active reaction function of monetary policy in terms of real-time estimates (Orphanides, 2001, AER), while also recognizing a role for non-fundamental sunspot shocks as drivers of inflation and output due to an inactive policy in terms of actual data (Clarida, Galì, and Gertler, 2000, QJE; Lubik and Schorfheide, 2004, AER). Extending earlier work by Lubik, Matthes, and Mertens (2022, RED), we derive analytic solutions for models in which imperfect information about R-Star faced by policymakers leads to equilibrium outcomes featuring sunspot-driven fluctuations. Our solution framework facilitates the estimation of such models.  

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 (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 (updated version of our presentation at NBER SI 2022)

• 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.”