Hi, welcome to my website!

 I am an applied macroeconomist and time-series econometrician and hold a position with the Research Centre of the Deutsche Bundesbank.  

My research is concerned with forecast uncertainty,  the dynamics of survey expectations, and informational frictions.  Most of the time, I end up solving signal extraction problems. 

My work is also posted at IDEAS, SSRN, GoogleScholar, ResearcherID, ORCID, GitHub and Deutsche Bundesbank

The word cloud on the right has been generated with Scholar Goggler.


Constructing Fan Charts from the Ragged Edge of SPF Forecasts (fully revised, 2024)

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

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.”

Shadow-rate VARs (working paper)

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

Abstract: VARs are popular for forecasting and structural analysis, but ill-suited to handle occasionally binding constraints, like the effective lower bound on nominal interest rates. We extend the VAR framework by modeling interest rates as censored observations of a latent shadow-rate process, and propose an efficient sampler for Bayesian estimation of such ``shadow-rate VARs.'' We find benefits to including both actual and shadow rates serve as explanatory variables.  Historical shadow-rate estimates indicate that the FOMC would have set the funds rate much lower than it could during recent ELB episodes.  In historical forecast accuracy, when compared to a standard VAR, shadow-rate VARs generate superior predictions for interest rates and deliver some gains for macroeconomic variables.   Our structural analysis of shocks to financial conditions show strong differences in the reaction of interest rates depending on whether the ELB binds or not, with implications for the response of economic activity to the shocks.

Among others, the paper has been presented at NBER SI 2023. Earlier versions of this paper were also earlier circulated under the title “Forecasting with Shadow-Rate VARs.”

NONE of the material posted on this personal website necessarily represents the views of 

the Deutsche Bundesbank, the Eurosystem, the Bank for International Settlements, 

the Board of Governors of the Federal Reserve System or the Federal Open Market Committee.