A0422
Title: Nonparametric local projections
Authors: Elena Pesavento - Emory University (United States) [presenting]
Silvia Goncalves - McGill University (Canada)
Ana Maria Herrera - University of Kentucky (United States)
Lutz Kilian - Federal Reserve Bank of Dallas (United States)
Abstract: Nonlinearities play an increasingly important role in applied work when studying the responses of macroeconomic aggregates to policy shocks. Seemingly natural adaptations of the popular local linear projection estimator to nonlinear settings may fail to recover the population responses of interest. The aim is to study the properties of an alternative nonparametric local projection estimator of the conditional and unconditional responses of an outcome variable to an observed identified shock. Alternative ways of implementing this estimator and how to allow for data-dependent tuning parameters are discussed. Results are based on data-generating processes that involve, respectively, nonlinearly transformed regressors, state-dependent coefficients, and nonlinear interactions between shocks and state variables. Monte Carlo simulations show that a local-linear specification of the estimator tends to work well in reasonably large samples and is robust to nonlinearities of unknown form.
