Hi, welcome to my website!

I am an applied macroeconomist and time-series econometrician 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.

LATEST RESEARCH:

Addressing COVID-19 Outliers in BVARs with Stochastic Volatility

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


Abstract: The COVID-19 pandemic has led to enormous movements in economic data that strongly affect parameters and forecasts obtained from standard VARs. One way to address these issues is to model extreme observations as random shifts in the stochastic volatility (SV) of VAR residuals. Specifically, we propose VAR models with outlier-augmented SV that combine transitory and persistent changes in volatility. The resulting density forecasts for the COVID-19 period are much less sensitive to outliers in the data than standard VARs. Evaluating forecast performance over the last few decades, we find that outlier-augmented SV schemes do at least as well as a conventional SV model. Predictive Bayes factors indicate that our outlier-augmented SV model provides the best data fit for the period since the pandemic's outbreak, as well as for earlier subsamples of relatively high volatility.

Forecasting with Shadow-rate VARs

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

  • draft: pdf (revised June 2021)

  • FRB Cleveland WP with supplementary appendix: html

  • slides: pdf

Abstract: Interest rate data are an important element of macroeconomic forecasting. Projections of future interest rates are not only an important product themselves, but also typically matter for forecasting other macroeconomic and financial variables. A popular class of forecasting models are linear Vector Autoregressions (VARs) that include shorter- and longer-term interest rates. However, in a number of economies, at least shorter-term interest rates have now been stuck for years at or near their effective lower bound (ELB), with longer-rates drifting toward the constraint as well. In such an environment, linear forecasting models that ignore the ELB constraint on nominal interest rates appear inept.

To handle the ELB on interest rates, we model observed rates as censored observations of a latent shadow-rate process in an otherwise standard VAR setup. The shadow rates are assumed to be equal to observed rates, when above the ELB. Point and density forecasts for interest rates (short-term and long-term) constructed from a shadow-rate VAR for the US since 2009 are superior to predictions from a standard VAR that ignores the ELB. For other indicators of financial conditions and measures of economic activity and inflation, the accuracy of forecasts from our shadow-rate specification is on par with a standard VAR that ignores the ELB.


shadowrateVARccmmCharts.pdf

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.