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Bunching Estimation of Elasticities Using Stata
A continuous distribution of agents that face a piecewise-linear schedule of incentives results in a distribution of responses with mass points located where the slope (kink) or intercept (notch) of the schedule changes. Bunching methods use these mass points to estimate an elasticity parameter, which summarizes agents' responses to incentives. This article introduces the command bunching, which implements new non-parametric and semi-parametric identification methods for estimating elasticities developed by Bertanha et al. (2021). These methods rely on weaker assumptions than currently made ...
Hawkish or Dovish Fed? Estimating a Time-Varying Reaction Function of the Federal Open Market Committee's Median Participant
This paper estimates a time-varying reaction function of the median participant of the Federal Open Market Committee, using a Taylor rule with time-varying coefficients estimated on one- to three-year ahead median forecasts of the federal funds rate, inflation, and the unemployment rate from the Summary of Economic Projections (SEP). We estimate the model with Bayesian methods, incorporating the effective lower bound on the median federal funds rate projections. The results indicate that the monetary policy rule has become significantly more persistent after the pandemic than in the years ...
Better Bunching, Nicer Notching
We study the bunching identification strategy for an elasticity parameter that summarizes agents' response to changes in slope (kink) or intercept (notch) of a schedule of incentives. A notch identifies the elasticity but a kink does not, when the distribution of agents is fully flexible. We propose new non-parametric and semi-parametric identification assumptions on the distribution of agents that are weaker than assumptions currently made in the literature. We revisit the original empirical application of the bunching estimator and find that our weaker identification assumptions result in ...