Using a microlevel data set of wind turbine installations in Denmark and Germany, we estimate a structural oligopoly model with cross-border trade and heterogeneous firms. Our approach separately identifies border-related from distance-related variable costs and bounds the fixed cost of exporting for each firm. In the data, firms’ market shares drop precipitously at the border. We find that 40% to 50% of the gap can be attributed to national border costs. Counterfactual analysis indicates that eliminating national border frictions would increase total welfare in the wind turbine industry by 4% in Denmark and 6% in Germany.

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. A solution to this problem is to use informative priors in order to shrink the richly parameterized unrestricted model toward a parsimonious naıve benchmark, and thus reduce estimation uncertainty. This paper studies the optimal choice of the informativeness of these priors, which we treat as additional parameters, in the spirit of hierarchical modeling. This approach, theoretically grounded and easy to implement, greatly reduces the number and importance of subjective choices in the setting of the prior. Moreover, it performs very well in terms of both out-of-sample forecasting—as well as factor models—and accuracy in the estimation of impulse response functions.

Standard economic models assume that the demand for expert predictions arises only under the conditions in which individuals are uncertain about the underlying process generating the data and there is a strong belief that past performances predict future performances. We set up the strongest possible test of these assumptions. In contrast to the theoretical suggestions made in the literature, people are willing to pay for predictions of truly random outcomes after witnessing only a short streak of accurate predictions live in the lab. We discuss potential explanations and implications of such irrational learning in the contexts of economics and finance.

Climate policy has favored costly measures that implicitly or explicitly subsidize lowcarbon fuels.We simulate four transportation sector policies: cap and trade (CAT), ethanol subsidies, a renewable fuel standard (RFS), and a lowcarbon fuel standard. Our simulations confirm that alternatives to CAT are 2.5 to 4 times more costly but are amenable to adoption due to right-skewed distributions of gains. We analyze voting on the Waxman-Markey (WM) CAT bill. Conditional on a district’s CAT gains, a district’s RFS gains are negatively correlated with the likelihood of voting for WM. Our analysis supports campaign contributions as a partial mechanism.