Bauer, Rudebusch, and Wu (2014) advocate the use of bias-corrected estimates in their comment on Wright (2011). Econometric estimation of a macro-finance VAR provides quite imprecise estimates of future short-term interest rates. Nonetheless, comparison with survey responses indicates that the proposed bias-corrected point estimates are less plausible than their maximum-likelihood counterparts.

This paper provides cross-country empirical evidence on term premia. I construct a panel of zero-coupon nominal government bond yields spanning ten industrialized countries and nearly two decades. I hence compute forward rates and use two different methods to decompose these forward rates into expected future short-term interest rates and term premiums. The first method uses an affine term structure model with macroeconomic variables as unspanned risk factors; the second method uses surveys. I find that term premiums declined internationally over the sample period, especially in countries that apparently reduced inflation uncertainty by making substantial changes in their monetary policy frameworks.

Recently a market in options based on consumer price index inflation (inflation caps and floors) has emerged in the US. This paper uses quotes on these derivatives to construct probability densities for inflation. We study how these probability density functions respond to news announcements and find that the implied odds of deflation are sensitive to certain macroeconomic news releases. We also estimate empirical pricing kernels using these option prices along with time series models fitted to inflation. The options-implied densities assign considerably more mass to extreme inflation outcomes (either deflation or high inflation) than do their time series counterparts. This yields a U-shaped empirical pricing kernel, with investors having high marginal utility in states of the world characterized by either deflation or high inflation.

It is well known that augmenting a standard linear regression model with variables that are correlated with the error term but uncorrelated with the original regressors will increase the asymptotic efficiency of the original coefficients. We argue that in the context of predicting excess returns, valid augmenting variables exist and are likely to yield substantial gains in estimation efficiency and, hence, predictive accuracy. The proposed augmenting variables are ex post measures of an unforecastable component of excess returns: ex post errors from macroeconomic survey forecasts, the surprise components of asset price movements around macroeconomic news announcements, or even the weather. These “surprises” cannot be used directly in forecasting—they are not observed at the time that the forecast is made—but can nonetheless improve forecasting accuracy by reducing parameter estimation uncertainty. We derive formal results about the benefits and limits of this approach and apply it to standard examples of forecasting excess bond and equity returns. We find substantial improvements in out-of-sample forecast accuracy for standard excess bond return regressions; gains for forecasting excess stock returns are much smaller.

Although it is clear that the volatility of asset returns is serially correlated, there is no general agreement as to the most appropriate parametric model for characterizing this temporal dependence. In this paper, we propose a simple way of modeling financial market volatility using high-frequency data. The method avoids using a tight parametric model by instead simply fitting a long autoregression to log-squared, squared, or absolute high-frequency returns. This can either be estimated by the usual time domain method, or alternatively the autoregressive coefficients can be backed out from the smoothed periodogram estimate of the spectrum of log-squared, squared, or absolute returns. We show how this approach can be used to construct volatility forecasts, which compare favorably with some leading alternatives in an out-of-sample forecasting exercise.

This paper develops asymptotic distribution theory for GMM estimators and test statistics when some or all of the parameters are weakly identified. General results are obtained and are specialized to two important cases: linear instrumental variables regression and Euler equations estimation of the CCAPM. Numerical results for the CCAPM demonstrate that weak-identification asymptotics explains the breakdown of conventional GMM procedures documented in previous Monte Carlo studies. Confidence sets immune to weak identification are proposed. We use these results to inform an empirical investigation of various CCAPM specifications; the substantive conclusions reached differ from those obtained using conventional methods.

This paper provides a survey of business cycle facts, updated to take account of recent data. Emphasis is given to the Great Recession, which was unlike most other postwar recessions in the United States in being driven by deleveraging and financial market factors. We document how recessions with financial market origins are different from those driven by supply or monetary policy shocks. This helps explain why economic models and predictors that work well at some times do poorly at other times. We discuss challenges for forecasters and empirical researchers in light of the updated business cycle facts.

We study the recent Australian experience with yield curve control (YCC) as perhaps the best evidence of how this policy might work in other developed economies. YCC seemingly worked well in 2020, when the market expected short rates to stay at zero for a long period of time. As the global recovery and inflation gained momentum in 2021, liftoff expectations moved up, the Reserve Bank of Australia purchased most of the targeted government bond outstanding, and the target bond's yield dislocated from other financial market instruments. The evidence suggests that central bank bond purchase programs can operate more narrowly than previously considered.

This paper provides an overview of the analysis of the term structure of interest rates with a special emphasis on recent developments at the intersection of macroeconomics and finance. The topic is important to investors and also to policymakers, who wish to extract macroeconomic expectations from longer-term interest rates, and take actions to influence those rates. The simplest model of the term structure is the expectations hypothesis, which posits that long-term interest rates are expectations of future average short-term rates. In this paper, we show that many features of the configuration of interest rates are puzzling from the perspective of the expectations hypothesis. We review models that explain these anomalies using time-varying risk premia. Although the quest for the fundamental macroeconomic explanations of these risk premia is ongoing, inflation uncertainty seems to play a large role. Finally, while modern finance theory prices bonds and other assets in a single unified framework, we also consider an earlier approach based on segmented markets. Market segmentation seems important to understand the term structure of interest rates during the recent financial crisis.

We consider forecasting with uncertainty about the choice of predictor variables. The researcher wants to select a model, estimate the parameters, and use this for forecasting. We investigate the dis-tributional properties of a number of different schemes for model choice and parameter estimation: in-sample model selection using the Akaike information criterion, out-of-sample model selection, and splitting the data into subsamples for model selection and parameter estimation. Using a weak-predictor local asymptotic scheme, we provide a representation result that facilitates comparison of the distributional properties of the procedures and their associated forecast risks. We develop a sim-ulation procedure that improves the accuracy of the out-of-sample and split-sample methods uni-formly over the local parameter space. We also examine how bootstrap aggregation (bagging) affects the local asymptotic risk of the estimators and their associated forecasts. Numerically, we find that for many values of the local parameter, the out-of-sample and split-sample schemes perform poorly if implemented in the conventional way. But they perform well, if implemented in conjunction with our risk-reduction method or bagging.