Price-level targeting (PLT) is optimal under the fully-informed rational expectations (FIRE) benchmark but lacks empirical support. Given the hurdles to the implementation of macroeconomic field experiments, we utilize a laboratory group experiment - where expectations are elicited from human...

Alger and Weibull (2013) ask the question whether a combination of assortative matching and incomplete information leads to the evolution of moral or altruistic preferences. Their central result states that Homo Hamiltonenis - a type that has moral preferences with a morality parameter equal to...

We study the effect of borrowing constraints in an assignment model of the housing market. When constraints apply symmetrically to all households, these lead to lower prices but unchanged housing consumption. When households can invest their own wealth and may differ in tastes, borrowing...

We propose the river pollution claims problem to distribute a limited pollution budget among agents located along a river. A key distinction with the standard claims problem is that agents are ordered and they are given priority based on their location in this order instead of their identity. We...

We examine recent claims that a particular Q-learning algorithm used by competitors 'autonomously' and systematically learns to collude, resulting in supracompetitive prices and extra profits for the firms sustained by collusive equilibria. A detailed analysis of the inner workings of this...

We assess the predictive ability of 15 economic uncertainty measures in a real-time out-of-sample forecasting exercise for the quantiles of The Conference Board's coincident economic index and its components (industrial production, employment, personal income, and manufacturing and trade sales)....

For reward-based crowdfunding, we introduce the strategy-proof Generalized Moulin-Shenker mechanism (GMS) and compare its performance to the prevailing All-Or-Nothing mechanism (AON). Theoretically, GMS outperforms AON in equilibrium profit and funding success. We test these predictions...

A flexible predictive density combination is introduced for large financial data sets which allows for model set incompleteness. Dimension reduction procedures that include learning allocate the large sets of predictive densities and combination weights to relatively small subsets. Given the...