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...

This paper explores two perspectives on the rational expectations hypothesis. One perspective is that of economic agents in such a model, who form inferences about the future using probabilities implied by the model. The other is that of an econometrician who makes inferences about the...

A recent literature has developed that combines two prominent empirical approaches to ex ante policy evaluation: randomized controlled trials (RCT) and structural estimation. The RCT provides a "gold-standard'' estimate of a particular treatment, but only of that treatment. Structural estimation...

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....

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...

A large sample approximation of the posterior distribution of partially identified structural parameters is derived for models that can be indexed by a finite-dimensional reduced form parameter vector. It is used to analyze the differences between frequentist confidence sets and Bayesian...

This paper develops and illustrates a simple method to generate a DSGE model-based forecast for variables that do not explicitly appear in the model (non-core variables). We use auxiliary regressions that resemble measurement equations in a dynamic factor model to link the non-core variables to...

We use Bayesian methods to estimate two models of post WWII U.S. inflation rates with drifting stochastic volatility and drifting coefficients. One model is univariate, the other a multivariate autoregression. We define the inflation gap as the deviation of inflation from a pure random walk...

When a rate of return is regressed on a lagged stochastic regressor, such as a dividend yield, the regression disturbance is correlated with the regressor's innovation. The OLS estimator's finite-sample properties, derived here, can depart substantially from the standard regression setting....

This paper analyzes the evolution of the degree of global cyclical interdependence over the period 1960-2005. We categorize the 106 countries in our sample into three groups -- industrial countries, emerging markets, and other developing economies. Using a dynamic factor model, we then decompose...