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, and Deutsche Bundesbank.
Codes can be found at GitHub. The publications and working papers pages on this site provide links to individual projects.

LATEST RESEARCH:

Fully revised: Precision-based sampling for state space models that have no measurement error

Abstract: This article presents a computationally efficient approach to sample from Gaussian state space models. The method is an instance of precision-based sampling methods that operate on the inverse variance-covariance matrix of the states (also known as precision). The novelty is to handle cases where the observables are modeled as a linear combination of the states without measurement error. In this case, the posterior variance of the states is singular and precision is ill-defined. As in other instances of precision-based sampling, computational gains are considerable. Relevant applications include trend-cycle decompositions, (mixed-frequency) VARs with missing variables and DSGE models.

Shadow-rate VARs

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 a popular tool 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 analyze specifications where actual and shadow rates serve as explanatory variables and find benefits of including both. In comparison to a standard VAR, shadow-rate VARs generate superior predictions for short- and long-term interest rates, and deliver some gains for macroeconomic variables in US data.  Our structural analysis estimates economic responses to shocks in financial conditions, showing strong differences in the reaction of interest rates depending on whether the ELB binds or not. After an adverse shock, our shadow-rate VAR sees a stronger decline of economic activity at the ELB rather than when not. 

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


Constructing Fan Charts from the Ragged Edge of SPF Forecasts

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

Abstract: We develop a model that permits the estimation of a term structure of both expectations and forecast uncertainty for application to professional forecasts such as the Survey of Professional Forecasters (SPF). Our approach exactly replicates a given data set of predictions from the SPF (or a similar forecast source) without measurement error. Our model captures fixed- horizon and fixed-event forecasts, and can accommodate changes in the maximal forecast horizon available from the SPF. The model casts a decomposition of multi-period forecast errors into a sequence of forecast updates that may be partially unobserved, resulting in a multivariate unobserved components model. In our empirical analysis, we provide quarterly term structures of expectations and uncertainty bands. Our preferred specification features stochastic volatility in forecast updates, which improves forecast performance and yields model estimates of forecast uncertainty that vary over time. We conclude by constructing SPF-based fan charts for calendar-year forecasts like those published by the Federal Reserve.

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




What Is the Predictive Value of SPF Point and Density Forecasts? (new method section added)

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

Abstract: This paper presents a new approach to combining the information in point and density forecasts from the Survey of Professional Forecasters (SPF) and assesses the incremental value of the density forecasts.  Our starting point is a model, developed in companion work, that constructs quarterly term structures of expectations and uncertainty from SPF point forecasts for quarterly fixed horizons and annual fixed events. We then employ entropic tilting to bring the density forecast information contained in the SPF's probability bins to bear on the model estimates.  In a novel application of entropic tilting, we let the resulting predictive densities exactly replicate the SPF's probability bins. Our empirical analysis of SPF forecasts of GDP growth shows that tilting to the SPF's probability bins can visibly affect our model-based predictive distributions.  Yet in historical evaluations, tilting does not offer consistent benefits to forecast accuracy relative to the model-based densities that are centered on the SPF's point forecasts and reflect the historical behavior of SPF forecast errors.  That said, there can be periods in which tilting to the bin information helps forecast accuracy.

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

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.