We develop a new class of nonlinear time-series models to identify nonlinearities in the data and to evaluate nonlinear DSGE models. U.S. output growth and the federal funds rate display nonlinear conditional mean dynamics, while inflation and nominal wage growth feature conditional...

We develop and estimate a general equilibrium model in which monetary policy can deviate from active inflation stabilization and agents face uncertainty about the nature of these deviations. When observing a deviation, agents conduct Bayesian learning to infer its likely duration. Under...

Based on subjective survival probability questions in the Health and Retirement Study (HRS), we use an econometric model to estimate the determinants of individual-level uncertainty about personal longevity. This model is built around the Modal Response Hypothesis (MRH), a mathematical...

Vector autoregressions (VARs) are flexible time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, their dense parameterization leads to unstable inference and inaccurate out-of-sample forecasts, particularly for models with many variables....

We consider the problem of short-term time series forecasting (nowcasting) when there are more possible predictors than observations. Our approach combines three Bayesian techniques: Kalman filtering, spike-and-slab regression, and model averaging. We illustrate this approach using search engine...

Dynamic stochastic general equilibrium models that include policy rules for government spending, lump-sum transfers, and distortionary taxation on labor and capital income and on consumption expenditures are fit to U.S. data under a variety of specifications of fiscal policy rules. We obtain...

This paper studies how stable over time are the so-called "structural parameters" of dynamic stochastic general equilibrium (DSGE) models. To answer this question, we estimate a medium-scale DSGE model with real and nominal rigidities using U.S. data. In our model, we allow for parameter...

Are excess returns predictable and if so, what does this mean for investors? Previous literature has tended toward two polar viewpoints: that predictability is useful only if the statistical evidence for it is incontrovertible, or that predictability should affect portfolio choice, even if the...

We develop and estimate a unified model of house prices, loan-to-value ratios (LTVs), and trade and foreclosure behavior. House prices are only observed for traded properties, and trades are endogenous, creating sample-selection problems for traditional estimators. We develop a Bayesian...

Bayesian prior predictive analysis of five nested DSGE models suggests that model specifications and prior distributions tightly circumscribe the range of possible government spending multipliers. Multipliers are decomposed into wealth and substitution effects, yielding uniform comparisons...