"We conduct a systematic comparison of confidence intervals around estimated probabilities of default (PD), using several analytical approaches from large-sample theory and bootstrapped small-sample confidence intervals. We do so for two different PD estimation methods--cohort and duration...

This paper revisits inflation forecasting using reduced-form Phillips curve forecasts, that is, inflation forecasts that use activity and expectations variables. We propose a Phillips-curve-type model that results from averaging across different regression specifications selected from a set of...

In this paper, we seek to produce forecasts of commodity price movements that can systematically improve on naive statistical benchmarks. We revisit how well changes in commodity currencies perform as potential efficient predictors of commodity prices, a view emphasized in the recent literature....

We suggest a way to perform parsimonious instrumental variables estimation in the presence of many, and potentially weak, instruments. In contrast to standard methods, our approach yields consistent estimates when the set of instrumental variables complies with a factor structure. In this sense,...

Some recent time-series applications use probit models to measure the forecasting power of a set of variables. Correct inferences about the significance of the variables requires a consistent estimator of the covariance matrix of the estimated model coefficients. A potential source of...

In a factor-augmented regression, the forecast of a variable depends on a few factors estimated from a large number of predictors. But how does one determine the appropriate number of factors relevant for such a regression? Existing work has focused on criteria that can consistently estimate the...

This note corrects a mistake in the estimation algorithm of the time-varying structural vector autoregression model of Primiceri (2005) and proposes a new algorithm that correctly applies the procedure proposed by Kim, Shephard, and Chib (1998) to the estimation of VAR or DSGE models with...

We study regression-based estimators for beta representations of dynamic asset pricing models with affine and exponentially affine pricing kernel specifications. These estimators extend static cross-sectional asset pricing estimators to settings where prices of risk vary with observed state...

We estimate the time series and cross section of bond returns by way of three-stage ordinary least squares, which we label dynamic Fama-MacBeth regressions. Our approach allows for estimation of models with a large number of pricing factors. Even though we do not impose yield cross-equation...