This paper studies the design of optimal fiscal policy when a government that fully trusts the probability model of government expenditures faces a fearful public that forms pessimistic expectations. We identify two forces that shape our results. On the one hand, the government has an incentive...

In the causal inference literature a class of semi-parametric estimators is called robust if the estimator has desirable properties under the assumption that at least one of the working models is correctly specified. A standard example is a doubly robust estimator that specifies parametric...

We estimate monetary policy rules in Switzerland for 1981-1997. In addition to an inflation gap, we find that forward-looking rules with output and exchange rate gaps nicely fit monetary aggregates as well as the call rate. We split the sample in 1990 when the Swiss National Bank replaced annual...

We propose instrumental variable(IV) estimators for quantile marginal effects and the parameters upon which they depend in a semiparametric outcome model with endogenous discrete treatment variables. We prove identification, consistency, and asymptotic normality of the estimators. We also show...

Fees are omnipresent in markets but, with few exceptions, are omitted in economic models-such as Double Auctions-of these markets. Allowing for general fee structures, we show that their impact on incentives and efficiency in large Double Auctions hinges on whether the fees are homogeneous (as,...

This paper analyzes optimal policy in setups where both the leader and the follower have doubts about the probability model of uncertainty. I illustrate the methodology in two environments: a) an industry populated with a large firm and many small firms in a competitive fringe, where both types...

We propose a method for forecasting individual outcomes and estimating random effects in linear panel data models and value-added models when the panel has a short time dimension. The method is robust, trivial to implement and requires minimal assumptions. The idea is to take a weighted average...