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Job Market Paper
I provide empirical evidence of changes in the U.S. Treasury yield curve and related macroeconomic factors, and investigate whether the changes are brought about by external shocks, monetary policy, or by both. To explore this, I characterize bond market exposures to macroeconomic and monetary policy risks, using an equilibrium term structure model with recursive preferences in which inflation dynamics are endogenously determined. In my model, the key risks that affect bond market prices are changes in the correlation between growth and inflation and changes in the conduct of monetary policy. Using a novel estimation technique, I find that the changes in monetary policy affect the volatility of yield spreads, while the changes in the correlation between growth and inflation affect both the level as well as the volatility of yield spreads. Consequently, the changes in the correlation structure are the main contributor to bond risk premia and to bond market volatility. The time variations within a regime and risks associated with moving across regimes lead to the failure of the Expectations Hypothesis and to the excess bond return predictability regression of Cochrane and Piazzesi (2005), as in the data.
Identifying Long-Run Risks: A Bayesian Mixed-Frequency Approach (Joint with Amir Yaron and Frank Schorfheide)
(FRB Philadelphia Working Paper 13-39)
We develop a nonlinear state-space model that captures the joint dynamics of consumption, dividend growth, and asset returns. Building on Bansal and Yaron (2004), our model consists of an economy containing a common predictable component for consumption and dividend growth and multiple stochastic volatility processes. The estimation is based on annual consumption data from 1929 to 1959, monthly consumption data after 1959, and monthly asset return data throughout. We maximize the span of the sample to recover the predictable component and use high-frequency data, whenever available, to efficiently identify the volatility processes. Our Bayesian estimation provides strong evidence for a small predictable component in consumption growth (even if asset return data are omitted from the estimation). Three independent volatility processes capture different frequency dynamics; our measurement error specification implies that consumption is measured much more precisely at an annual than monthly frequency; and the estimated model is able to capture key asset-pricing facts of the data.
Improving GDP Measurement: A Measurement-Error Perspective (Joint with Boragan Aruoba, Francis Diebold, Frank Schorfheide, Jeremy Nalewaik)
(NBER Working Paper 18954)
We provide a new measure of U.S. GDP growth, obtained by applying optimal signal-extraction techniques to the noisy expenditure-side and income-side GDP estimates. The quarter-by-quarter values of our new measure often differ noticeably from those of the traditional measures. Its dynamic properties differ as well, indicating that the persistence of aggregate output dynamics is stronger than previously thought.
Improving U.S. GDP Measurement: A Forecast Combination Perspective (Joint with Boragan Aruoba, Francis Diebold, Frank Schorfheide, Jeremy Nalewaik)
(In X. Chen and N. Swanson (eds) "Causality, Prediction, and Specification Analysis: Recent Advances and Future Directions - Essays in Honour of Halbert L. White Jr," Springer Verlag, 2013, 1-25.)
Two often-divergent U.S. GDP estimates are available, a widely-used expenditure-side version GDP_E, and a much less widely-used income-side version GDP_I. We propose and explore a ``forecast combination" approach to combining them. We then put the theory to work, producing a superior combined estimate of GDP growth for the U.S., GDP_C. We compare GDP_C to GDP_E and GDP_I, with particular attention to behavior over the business cycle. We discuss several variations and extensions.
(NBER Working Paper 19712)
This paper develops a vector autoregression (VAR) for time series which are observed at mixed frequencies - quarterly and monthly. The model is cast in state-space form and estimated with Bayesian methods under a Minnesota-style prior. We show how to evaluate the marginal data density to implement a data-driven hyperparameter selection. Using a real-time data set, we evaluate forecasts from the mixed-frequency VAR and compare them to standard quarterly-frequency VAR and to forecasts from MIDAS regressions. We document the extent to which information that becomes available within the quarter improves the forecasts in real time.
Research Analyst, Federal Reserve Bank of Minneapolis, 2010-2011
Aruoba-Diebold-Nalewaik-Schorfheide-Song (ADNSS) GDPplus Series, Federal Reserve Bank of Philadelphia http://www.philadelphiafed.org/research-and-data/real-time-center/gdpplus/
Francis X. Diebold
+1 (215) 898-1507
+1 (215) 898 8486
+1 (215) 898-1118
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.