From a new data set, we infer time series of term structures of yields on US federal bonds during the gold standard era from 1791-1933 and use our estimates to reassess historical narratives about how the US expanded its fiscal capacity. We show that US debt carried a default risk premium until the end of the nineteenth century when it started being priced as an alternative safe-asset to UK debt. During the Civil War, investors expected the US to return to a gold standard so the federal government was able to borrow without facing denomination risk. After the introduction of the National Banking System, the slope of the yield curve switched from down to up and the premium on US debt with maturity less than one year disappeared.
US federal governments have confronted trade-offs among lowering borrowing costs, maintaining price stability, and maintaining financial stability. During the gold standard era, successive administrations prioritized decreasing government borrowing costs and keeping trend inflation low. Starting with FDR, the government prioritized financial and business cycle stability and was willing to use inflation taxes to lower its debt obligations and redistribute wealth between nominal creditors and debtors. Towards the end of the twentieth century, the government embraced financial deregulation and aggressive inflation targeting. We use our estimates for historical yields and inflation processes to indicate how those changing policy priorities affected or coincided with key macroeconomic correlations. The slope of both the US federal debt yield curve and the “Phillips curve” has changed signs as government priorities have changed.
Estimating 19th century US federal bond yield curves involves challenges because few bonds were traded, bonds had peculiar features, government policies changed often, and there were wars. This paper compares statistical approaches for confronting these difficulties and shows that a dynamic Nelson-Siegel model with stochastic volatility and bond-specific pricing errors does a good job for historical US bond prices. This model is flexible enough to interpolate data across periods in a time-varying way without over-fitting. We exploit new computational techniques to deploy our model and estimate yield curves for US federal debt from 1790-1933.
We revisit the role of long-term nominal corporate debt for the transmission of inflation shocks in the general equilibrium model of Gomes, Jermann, and Schmid (2016). We show that inaccuracies in the model solution and calibration strategy lead GJS to a model equilibrium in which nominal long-term debt is systematically mispriced. As a result, the quantitative importance of corporate leverage in the transmission of inflation shocks to real activity in their framework is six times larger than what arises under the rational expectations equilibrium.
We consider learning with a set of likelihoods when the learner’s set is misspecified. We study welfare implications of entertaining a misspecified set by focusing on the limit point of learning and the associated best-responding policy. Building on such policies, we define consistency requirements for sets of likelihoods that a utility-maximizing agent would find desirable. We characterize a class of decision problems for which exponential families of likelihoods—with payoff-relevant moments as sufficient statistics—exist that satisfy our consistency requirements therefore guaranteeing the asymptotic implementation of optimal policies irrespective of the data generating process.
Uncertainty Shocks in a Monetary Economy
[PDF, November 2019]
This paper studies a stochastic economy with flexible prices in which money has real effects. A representative household faces a portfolio choice between a nominally safe asset that provides transaction services and a risky productive capital with time-varying return volatility. Stochastic volatility and the behavior of the central bank determine an equilibrium asset allocation. When the objective of monetary policy is to stabilize inflation around a fixed target, the nominally safe asset becomes a relatively safe store of value in real terms as well. As a result, in response to higher uncertainty, the private sector shifts resources away from risky capital, causing output and investment to fall. To investigate the resulting non-linear dynamics, I solve the model globally and compute generalized impulse response functions across the support of the stationary distribution. Impulse response functions with respect to volatility shocks exhibit strong state dependence: large falls in investment are more likely in a high-risk (low interest rate) environment than in a low-risk (high interest rate) environment. I use the calibrated model to interpret recent events and find that the model predicts comovements observed among a set of key macro variables during and after the Great Recession.
Published or Forthcoming Papers
Journal of Economic Theory (2022) 199: 105225, doi: 10.1016/j.jet.2021.105225
[PDF, February 2021] [online_appendix] [code]
featured in the inaugural David K. Backus Memorial Lecture by Tom Sargent [video]
Twisted Probabilities, Uncertainty, and Prices
(with Lars Peter Hansen, Thomas J. Sargent, and Lloyd Han)
Journal of Econometrics (2020) 216 (1) : 151-174, doi: 10.1016/j.jeconom.2020.01.011
[PDF, January 2020] [online appendix] [code, binder]