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This paper examines the aggregate implications of sovereign credit risk in a business cycle model in which banks are exposed to risky government debt. An increase in the probability of a future sovereign default leads to a reduction in credit to firms because of two channels. First, it lowers the value of government debt on the balance sheet of banks, tightening their funding constraints and leaving them with fewer resources to lend to firms. Second, it raises the required premia demanded by banks for lending to firms because this activity has become riskier: if the sovereign default occurs, the economy falls in a major recession and claims to the productive sector pay out little. I estimate the nonlinear model with Italian data using Bayesian techniques. I find that sovereign credit risk led to a rise in the financing premia of firms that peaked 100 basis points, and cumulative output losses of 4.75% by the end of 2011. Both channels were quantitatively important drivers of the propagation of sovereign credit risk to the real economy. I then use the model to evaluate the effects of subsidized long term loans to banks, calibrated to the ECB's Longer Term Refinancing Operations. The presence of a significant risk channel at the policy enactment explains the limited stimulative effects of these interventions.
(NBER Working Paper No 19693)
We develop a new class of time series models to identify nonlinearities in the data and to evaluate DSGE models. U.S. output growth and the federal funds rate display nonlinear conditional mean dynamics, while inflation and nominal wage growth feature conditional heteroskedasticity. We estimate a DSGE model with asymmetric wage and price adjustment costs and use predictive checks to assess its ability to account for nonlinearities. While it is able to match the nonlinear inflation and wage dynamics, thanks to the estimated downward wage and price rigidities, these do not spill over to output growth or the interest rate.
(Philadelphia Fed Working Paper 13-10)
This paper documents a strong association between total factor productivity (TFP) growth and the value of U.S. corporations (measured as the value of equities and net debt for the U.S. corporate sector) throughout the postwar period. Persistent fluctuations in the first two moments of TFP growth predict two-thirds of the medium-term variation in the value of U.S. corporations relative to gross domestic product (henceforth value-output ratio). An increase in the conditional mean of TFP growth by 1% is associated to a 21% increase in the value-output ratio, while this indicator declines by 12% following a 1% increase in the standard deviation of TFP growth. A possible explanation for these findings is that movements in the first two moments of aggregate productivity affect the expectations that investors have regarding future corporate payouts as well as their perceived risk. We develop a dynamic stochastic general equilibrium model with the aim of verifying how sensible this interpretation is. The model features recursive preferences for the households, Markov-Switching regimes in the first two moments of TFP growth incomplete information and monopolistic rents. Under a plausible calibration and including all these features, the model can account for a sizable fraction of the elasticity of the value-output ratio to the first two moments of TFP growth.
Identifying Neutral Technology Shocks (with Marcus Hagedorn and Iourii Manovskii, draft coming soon)
The role of neutral technology shocks in driving business cycle fluctuations is hotly debated. Yet, we argue that there is no existing empirical methodology that allows to identify neutral shocks in the presence of input heterogeneity in the aggregate production function. We develop a method that identifies a neutral technology shock based on the result that it is the only shock consistent with balanced growth restrictions. Monte Carlo simulations using benchmark business cycle models imply that the proposed method performs very well in small samples. We apply the method to assess the role of neutral technology in driving business cycle fluctuations in U.S. data.
2012 (Summer), Statistics for Economists, UPenn, Instructor.
2010-2011 (Spring), Time Series Econometrics (graduate), UPenn, Teaching Assistant for Prof. Francis X. Diebold.
2009-2010 (Fall), Introduction to Micro and Macro Economics and Its Applications, UPenn, Recitation Instructor for Dr. Rebecca Stein.
Research Analyst, Federal Reserve Bank of Philadelphia, June 2011 - May 2013
+1-(215) 898 8486
I am on the job market and will be available for interviews during the AEA meetings in Philadelphia, from 1/3 to 1/5, 2014.