We identify structural vector autoregressions using narrative sign restrictions. Narrative sign restrictions constrain the structural shocks and/or the historical decomposition around key historical events, ensuring that they agree with the established narrative account of these episodes. Using models of the oil market and monetary policy, we show that narrative sign restrictions tend to be highly informative. Even a single narrative sign restriction may dramatically sharpen and even change the inference of SVARs originally identified via traditional sign restrictions. Our approach combines the appeal of narrative methods with the popularized usage of traditional sign restrictions. (JEL C32, E52, Q35, Q43)

Military spending has large and persistent effects on output because it shifts the composition of public spending toward R&D. This boosts innovation and private investment in the medium term and increases productivity and GDP at longer horizons. Public R&D expenditure stimulates economic activities beyond the business cycle even when it is not associated with war spending. In contrast, the effects of public investment are shorter-lived, while public consumption has a modest impact at most horizons. We reach these conclusions using BVAR with long lags and 125 years of US data, including newly reconstructed series of government spending by main categories since 1890. (JEL E21, E22, E23, E62, H50, H56, O30)

Abstract A long tradition in macro-finance studies the dynamics of aggregate stock returns and dividends using vector autoregressions, imposing the restrictions implied by the Campbell-Shiller (CS) identity to sharpen inference. We develop Bayesian methods that encode a priori skepticism about return predictability while imposing the restrictions. We highlight that persistence in dividend growth induces “dividend momentum,” a previously overlooked channel for return predictability. By combining Bayesian shrinkage and the CS restrictions, we obtain more plausible degrees of return predictability, superior out-of-sample forecasts, and Sharpe ratios, which cannot be obtained by using either shrinkage or the CS restrictions on their own.

Using a dynamic factor model that allows for changes in both the long-run growth rate of output and the volatility of business cycles, we document a significant decline in long-run output growth in the United States. Our evidence supports the view that most of this slowdown occurred prior to the Great Recession. We show how to use the model to decompose changes in long-run growth into its underlying drivers. At low frequencies, a decline in the growth rate of labor productivity appears to be behind the recent slowdown in GDP growth for both the United States and other advanced economies. When applied to real-time data, the proposed model is capable of detecting shifts in long-run growth in a timely and reliable manner.