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Abstract: We study how uncertainty shocks affect the macroeconomy across the inflation cycle using a nonlinear stochastic volatility-in-mean VAR. When inflation is high, uncertainty shocks raise inflation and depress real activity more sharply. A nonlinear New Keynesian model with second-moment shocks and trend inflation explains this via an "inflation-uncertainty amplifier": the interaction between high trend inflation and firms’ upward price bias magnifies the effects of uncertainty by increasing price dispersion. An aggressive policy response can replicate the allocation achieved under standard policy when trend inflation is low.
Presentations:
Monetary Policy Shocks and Narrative Restrictions: Rules Matter!, with E. Castelnuovo and G. Pellegrino. Draft [Submitted]
Abstract: Restrictions on the policy coefficients in a vector autoregressive model can substantially sharpen the identification of monetary policy shocks achieved via narrative restrictions. We reach this conclusion by conducting extensive Monte Carlo simulations with a standard monetary policy model of the business cycle as our data generating process. We show that narrative restrictions dramatically improve the ability of (traditional) restrictions imposed on impulse responses to identify a monetary policy shock; restrictions on the policy rule coefficients improve identification even further. Working with US data, we show that policy coefficient restrictions imply a larger and more precisely estimated short-run response of output to a monetary policy shock than the one predicted by using only traditional and narrative restrictions. This happens because policy coefficient restrictions work in favor of shifting the burden of matching unconditional moments in the data from the systematic policy rule to monetary policy shocks. Working with Euro area data, we show that policy coefficient restrictions sharpen the identification of the contemporaneous response of the corporate bond spread to a monetary policy shock.
Narrative Sign Restrictions in a Daily Vector Autoregression, with Martin M. Andreasen and Giovanni Pellegrino [In progress]
Abstract: This paper introduces a daily vector autoregression (VAR) constructed from financial variables and less frequently observed macroeconomic variables as a useful tool to identify structural shocks by narrative sign restrictions. The estimation is carried out using filtering methods to account for the mixed frequency of the data and pooling of the VAR coefficients in the time domain to efficiently condition on past data in previous months. We illustrate the usefulness of our approach by identifying sentiment shocks from unexplained daily variation in the US stock market and conventional monetary policy shocks from unexplained daily variation in the policy target around Federal open market committee (FOMC) meetings.
Identifying Large-Scale Asset Purchase Shocks: Disentangling the Long End of the Yield Curve, with Marcel Stechert [In progress]
The Rise of Superstars, Markup Fluctuations and Business Cycles, with Mark Weder [In progress]
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