"Financial Stress and Economic Dynamics: the transmission of crises" (with Kirstin Hubrich)

Abstract: The recent financial crisis and the associated decline in economic activity have raised some important questions about economic activity and its links to the financial sector. This paper introduces an index of financial stress---an index that was used in real time by the staff of the Federal Reserve Board to monitor the crisis---and shows how stress interacts with real activity, inflation and monetary policy. We define what we call a stress event---a period affected by stress in both shock variances and model coefficients--- and describe how financial stress affects macroeconomic dynamics.  We also examine what constitutes a useful and credible measure of stress and the role of monetary policy. We address these questions using a richly parameterized Markov-switching VAR model, estimated using Bayesian methods. Our results show that allowing for time variation is important: the constant-parameter Gaussian model is a poor characterization of the data. We find that periods of high-stress coefficients in general, and stress events in particular, line up well with financial events in recent U.S. history.  We find that a shift to a stress event is highly deterimental to the outlook for the real economy, and that conventional monetary policy is relatively weak during such periods.  Finally, we argue that our findings have significant implications for DSGE modeling of financial frictions, pointing away from linearized DSGE models toward either MS-DSGE models or fully nonlinear models solved with global methods.
JEL Classification: E44, C11,C32
Keywords: nonlinearity, Markov switching, financial crises, monetary policy transmission, Bayesian econometrics

Substantially revised version: October 18, 2012. Click here for a PDF.

Learning and the Role of Macroeconomic Factors in the Term Structure" (with Thomas Laubach and John C. Williams).

Abstract: Models of the term structure based on only observable variables have had limited success in explaining movements in longer-term interest rates. A key assumption in much of this literature is that agents know all the parameters describing the model of the economy, and that these parameters are fixed for all time. In this paper, we relax both of these assumptions and assume instead that agents regularly re-estimate the parameters of their models--both those determining the point forecasts and those describing economic volatility--based on incoming data. In this way, we allow for the real-time problem of pricing assets based on properly dated information sets. In addition, we allow for discounting of past data reflecting a concern on the part of agents for undetectable structural change in the economy. We find that the learning model with discounting does a much better job at explaining longer-term yields than an equivalent model with constant coefficients estimated over the full sample; in particular, the deviations from the pure expectations hypothesis are much smaller, on average, with our learning model. We then estimate the model term premia imposing an affine arbitrage-free structure. We show that incorporating learning improves the in-sample fit and forecasting performance of the model. Learning also implies time variation in the real-time estimates of macroeconomic volatility that is absent in standard macroeconomic models with constant coefficients. We find that these shifts in estimates of macroeconomic volatility help explain movements in term premia over the past half century. More generally, our analysis highlights the importance of taking into account the information sets of investors in understanding the determinants of bond prices.

JEL Codes: D83, D84, G12

Keywords: affine term structure models, learning, structural change.

Version date: September 11, 2007. here for a PDF of a draft version.

 

"Real-time Model Uncertainty in the United States: 'Robust' policies put to the test"

Abstract: We exploit 46 versions of the Board of Governor's FRB/US model, that differ by vintage; 4 per year since the model's inception in 1996. The surprisingly substantive model changes incurred over a relatively short period of time imply that the policies that were optimal ex ante, were considerably less so ex post. This situation provides a rare opportunity to test in a real-world setting proposed methods for designing policies that are purportedly robust to parameter and model uncertainty. We also examine rules that are robust in the sense of attempting to encompass the range of models represented by the historical range of specifications. Eschewing feedback on some measure of aggregate demand turns out not to be a good idea, however devising policies that are less reliant on latent variables like potential output and the equilibrium real interest rate is. (Revised version, July 2009)

JEL Classifications: E37, E5, C5, C6.

Keywords: monetary policy, model uncertainty, robust policies, real time.

Version date: July 1, 2009. Click here.