Search Results
Working Paper
Are nonlinear methods necessary at the zero lower bound?
This paper examines the importance of the zero lower bound (ZLB) constraint on the nominal interest rate by estimating three variants of a small-scale New Keynesian model: (1) a nonlinear model with an occassionally binding ZLB constraint; (2) a constrained linear model, which imposes the constraint in the filter but not the solution; and (3) an unconstrained linear model, which never imposes the constraint. The posterior distributions are similar, but important differences arise in their predictions at the ZLB. The nonlinear model fits the data better at the ZLB and primarily attributes the ...
Working Paper
Forward Guidance with Bayesian Learning and Estimation
Considerable attention has been devoted to evaluating the macroeconomic effectiveness of the Federal Reserve's communications about future policy rates (forward guidance) in light of the U.S. economy's long spell at the zero lower bound (ZLB). In this paper, we study whether forward guidance represented a shift in the systematic description of monetary policy by estimating a New Keynesian model using Bayesian techniques. In doing so, we take into account the uncertainty that agents have about policy regimes using an incomplete information setup in which they update their beliefs using Bayes ...
Working Paper
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 ...
Working Paper
Optimal Monetary and Macroprudential Policies: Gains and Pitfalls in a Model of Financial Intermediation
We estimate a quantitative general equilibrium model with nominal rigidities and financial intermediation to examine the interaction of monetary and macroprudential stabilization policies. The estimation procedure uses credit spreads to help identify the role of financial shocks amenable to stabilization via monetary or macroprudential instruments. The estimated model implies that monetary policy should not respond strongly to the credit cycle and can only partially insulate the economy from the distortionary effects of financial frictions/shocks. A counter-cyclical macroprudential instrument ...
Working Paper
Why Are Estimates of Agricultural Supply Response so Variable?
Estimates of the response of agricultural supply to movements in expected price display curiously large variation across crops, regions, and time periods. We argue that this anomoly may be traced, at least in part, to the statistical properties of the commonly-used econometric estimator, which has infinite moments of all orders and may have a bimodal distribution. We propose an alternative minimum- expected-loss estimator, establish its improved sampling properties, and argue for its usefulness in the empirical analysis of agricultural supply response.
Working Paper
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 ...
Working Paper
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, ...
Working Paper
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 ...
Working Paper
Inflation Expectations with Finite Horizon Planning
Under finite horizon planning, households and firms evaluate a full set of state-contingent paths along which the economy might evolve out to a finite horizon but have limited ability to process events beyond that horizon. We show--analytically and empirically--that such a model accounts for an initial underreaction and subsequent overreaction of inflation forecasts. A planning horizon of four quarters can account for the evidence on the predictability of inflation forecast errors and macroeconomic data. Our identification and estimation strategies combine full-information methods based ...
Working Paper
Trend-Cycle Decomposition and Forecasting Using Bayesian Multivariate Unobserved Components
We propose a generalized multivariate unobserved components model to decompose macroeconomic data into trend and cyclical components. We then forecast the series using Bayesian methods. We document that a fully Bayesian estimation, that accounts for state and parameter uncertainty, consistently dominates out-of-sample forecasts produced by alternative multivariate and univariate models. In addition, allowing for stochastic volatility components in variables improves forecasts. To address data limitations, we exploit cross-sectional information, use the commonalities across variables, and ...