Financial Business Cycles
Using Bayesian methods, I estimate a DSGE model where a recession is initiated by losses suffered by banks and exacerbated by their inability to extend credit to the real sector. The event triggering the recession has the workings of a redistribution shock: a small sector of the economy -- borrowers who use their home as collateral -- defaults on their loans. When banks hold little equity in excess of regulatory requirements, the losses require them to react immediately, either by recapitalizing or by deleveraging. By deleveraging, banks transform the initial shock into a credit crunch, and, ...
Bayesian Estimation of Time-Changed Default Intensity Models
We estimate a reduced-form model of credit risk that incorporates stochastic volatility in default intensity via stochastic time-change. Our Bayesian MCMC estimation method overcomes nonlinearity in the measurement equation and state-dependent volatility in the state equation. We implement on firm-level time-series of CDS spreads, and find strong in-sample evidence of stochastic volatility in this market. Relative to the widely-used CIR model for the default intensity, we find that stochastic time-change offers modest benefit in fitting the cross-section of CDS spreads at each point in time, ...
Measuring Ambiguity Aversion
We confront the generalized recursive smooth ambiguity aversion preferences of Klibanoff, Marinacci, and Mukerji (2005, 2009) with data using Bayesian methods introduced by Gallant and McCulloch (2009) to close two existing gaps in the literature. First, we use macroeconomic and financial data to estimate the size of ambiguity aversion as well as other structural parameters in a representative-agent consumption-based asset pricing model. Second, we use estimated structural parameters to investigate asset pricing implications of ambiguity aversion. Our structural parameter estimates are ...
Does Smooth Ambiguity Matter for Asset Pricing?
We use the Bayesian method introduced by Gallant and McCulloch (2009) to estimate consumption-based asset pricing models featuring smooth ambiguity preferences. We rely on semi-nonparametric estimation of a flexible auxiliary model in our structural estimation. Based on the market and aggregate consumption data, our estimation provides statistical support for asset pricing models with smooth ambiguity. Statistical model comparison shows that models with ambiguity, learning and time-varying volatility are preferred to the long-run risk model. We analyze asset pricing implications of the ...
Dynamic prediction pools: an investigation of financial frictions and forecasting performance
We provide a novel methodology for estimating time-varying weights in linear prediction pools, which we call dynamic pools, and use it to investigate the relative forecasting performance of dynamic stochastic general equilibrium (DSGE) models, with and without financial frictions, for output growth and inflation in the period 1992 to 2011. We find strong evidence of time variation in the pool?s weights, reflecting the fact that the DSGE model with financial frictions produces superior forecasts in periods of financial distress but doesn?t perform as well in tranquil periods. The dynamic ...
Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints
We develop an algorithm to construct approximate decision rules that are piecewise-linear and continuous for DSGE models with an occasionally binding constraint. The functional form of the decision rules allows us to derive a conditionally optimal particle filter (COPF) for the evaluation of the likelihood function that exploits the structure of the solution. We document the accuracy of the likelihood approximation and embed it into a particle Markov chain Monte Carlo algorithm to conduct Bayesian estimation. Compared with a standard bootstrap particle filter, the COPF significantly reduces ...
Priors and the Slope of the Phillips Curve
The slope of the Phillips curve in New Keynesian models is difficult to estimate using aggregate data. We show that in a Bayesian estimation, the priors placed on the parameters governing nominal rigidities significantly influence posterior estimates and thus inferences about the importance of nominal rigidities. Conversely, we show that priors play a negligible role in a New Keynesian model estimated using state-level data. An estimation with state-level data exploits a relatively large panel dataset and removes the influence of endogenous monetary policy.
Short-term Planning, Monetary Policy, and Macroeconomic Persistence
This paper uses aggregate data to estimate and evaluate a behavioral New Keynesian (NK) model in which households and firms plan over a finite horizon. The finite-horizon (FH) model outperforms rational expectations versions of the NK model commonly used in empirical applications as well as other behavioral NK models. The better fit of the FH model reflects that it can induce slow-moving trends in key endogenous variables which deliver substantial persistence in output and inflation dynamics. In the FH model, households and firms are forward-looking in thinking about events over their ...
The Role of News about TFP in U.S. Recessions and Booms
We develop a general equilibrium model to study the historical contribution of TFP news to the U.S. business cycle. Hiring frictions provide incentives for firms to start hiring ahead of an anticipated improvement in technology. For plausibly calibrated hiring costs, employment gradually rises in response to positive TFP news shocks even under standard preferences. TFP news shocks are identified mainly by current and expected unemployment rates since periods in which average unemployment is relatively high (low) are also periods in which average TFP growth is slow (fast). We work out the ...
Uncertainty Shocks, Monetary Policy and Long-Term Interest Rates
We study the relationship between monetary policy and long-term rates in a structural, general equilibrium model estimated on both macro and yields data from the United States. Regime shifts in the conditional variance of productivity shocks, or "uncertainty shocks", are an important model ingredient. First, they account for countercyclical movements in risk premia. Second, they induce changes in the demand for precautionary saving, which affects expected future real rates. Through changes in both risk-premia and expected future real rates, uncertainty shocks account for about 1/2 of the ...