Most of the future growth in energy use is forecast to come from the developing world. Understanding the likely pace and specific location of this growth is essential to inform decisions about energy infrastructure investments and to improve greenhouse gas emissions forecasts. We argue that...

Most of the medium-run growth in energy demand is forecast to come from the developing world, which consumed more total units of energy than the developed world in 2007. We argue that the main driver of the growth is likely to be increased incomes among the poor and near-poor. We document that...

We explore the principal trends that are shaping the future landscape of energy supply, demand, and trade. We take a long-term view, assessing trends on the time scale of a generation by looking 25 years into the past, taking stock of the current situation, and projecting 25 years into the...

Traditional least squares estimates of the responsiveness of gasoline consumption to changes in gasoline prices are biased toward zero, given the endogeneity of gasoline prices. A seemingly natural solution to this problem is to instrument for gasoline prices using gasoline taxes, but this...

We provide a novel methodology for estimating time-varying weights in linear prediction pools, which we call Dynamic Pools, and use it to investigate the relative forecasting performance of DSGE models with and without financial frictions for output growth and inflation from 1992 to 2011. We...

A fruitful emerging literature reveals that shocks to uncertainty can explain asset returns, business cycles and financial crises. The literature equates uncertainty shocks with changes in the variance of an innovation whose distribution is common knowledge. But how do such shocks arise? This...

Ferson, Sarkissian and Simin (2003) warn that persistence in expected returns generates spurious regression bias in predictive regressions of stock returns, even though stock returns are themselves only weakly autocorrelated. Despite this fact a growing literature attempts to explain the...

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...