How much do different monetary and non-monetary motivators induce costly effort? Does the effectiveness line up with the expectations of researchers? We present the results of a large-scale real-effort experiment with 18 treatment arms. We compare the effect of three motivators: (i) standard...

We collect a comprehensive set of non-academic characteristics for a representative sample of incoming freshman to explore which measures best predict the wide variance in first-year college performance unaccounted for by past grades. We focus our attention on student outliers. Students whose...

We combine annual stock market data for the most important equity markets of the last four centuries: the Netherlands/U.K. (1629-1812), U.K. (1813-1870) and U.S. (1871-2015). We show that dividend yields are stationary and consistently forecast returns. The documented predictability holds for...

Optimal investment of firms implies that expected stock returns are tied with the expected marginal benefit of investment divided by the marginal cost of investment. Winners have higher expected growth and expected marginal productivity (two major components of the marginal benefit of...

We propose a Bayesian procedure for exploiting small, possibly long-lag linear predictability in the innovations of a finite order autoregression. We model the innovations as having a log-spectral density that is a continuous mean-zero Gaussian process of order 1/√T. This local embedding makes...

This paper develops a new utility-based monetary aggregate which we label the currency equivalent aggregate. This aggregate equals the stock of currency that would be required for households to obtain the same liquidity services that they get from their entire collection of monetary assets. We...

An appropriate metric for the success of an algorithm to forecast the variance of the rate of return on a capital asset could be the incremental profit from substituting it for the next best alternative. We propose a framework to assess incremental profits for competing algorithms to forecast...

We examine the evidence on excess stock return predictability in a Bayesian setting in which the investor faces uncertainty about both the existence and strength of predictability. When we apply our methods to the dividend-price ratio, we find that even investors who are quite skeptical about...

We address the question of the prediction of large failures, busts, or system collapse, and the necessary concepts related to risk quantification, minimization and management. Answering this question requires a new approach since predictions using standard financial techniques and statistical...

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....