We investigate the impact of mergers on R&D incentives within a framework of R&D competition where effort can influence both the probability of innovation and the payoff conditional on success. Our framework nests the results of two classes of existing models and reveals assumptions that are...

Deployment subsidies for nascent technologies have moved to the forefront of climate action, industrial policy, and geoeconomics. To limit the substantial fiscal costs of such programs, policymakers routinely employ subsidy schedules that are nonlinear in the quantity agents deploy. However,...

Directed acyclic graphs (DAGs) provide a transparent framework for encoding causal structures and identifying causal effects. This paper demonstrates how DAGs help specify local projections (LPs) for estimating causal impulse responses. Examples illustrate how graphical rules can be used to...

We develop a model to estimate price-adjustment behavior when prices are observed more frequently than key explanatory variables such as wage costs. We propose a mixed-frequency stochastic (S, s)-model that accommodates infrequently observed costs and allows for plant-, product-, and...

Official statistics increasingly make use of higher-frequency time series. But when users ultimately are interested in a seasonally adjusted temporal aggregate of these data, we have to decide whether to perform seasonal adjustment or aggregation first. Consequently, we must weigh up the...

We propose a new approach to estimate selection-corrected quantiles of the gender wage gap. Our method employs instrumental variables that explain variation in the latent variable but, conditional on the latent process, do not directly affect selection. We provide semiparametric identification...