Semiparametric estimation and inference using doubly robust moment conditions

We study semiparametric two-step estimators which have the same structure as parametric doubly robust estimators in their second step. The key difference is that we do not impose any parametric restriction on the nuisance functions that are estimated in a first stage, but retain a fully nonparametric model instead. We call these estimators semiparametric doubly robust estimators (SDREs), and show that they possess superior theoretical and practical properties compared to generic semiparametric two-step estimators. In particular, our estimators have substantially smaller first-order bias, allow for a wider range of nonparametric first-stage estimates, rate-optimal choices of smoothing parameters and data-driven estimates thereof, and their stochastic behavior can be well-approximated by classical first-order asymptotics. SDREs exist for a wide range of parameters of interest, particularly in semiparametric missing data and causal inference models. We illustrate our method with a simulation exercise.

Effects of fiscal consolidations in Latin America

We use new data on cyclically adjusted primary balances for Latin America and the Caribbean to estimate e ects of scal consolidations on GDP and some of its components. Identi cation is conducted through a doubly-robust estimation procedure that controls for non-randomness in the "treatment assignment" by inverse probability weighting and impulse responses are generated by local projections. Results suggest output contraction by more than one percent on impact, with economy starting to recover from the second year on. Composition e ects indicate that revenue-based adjustments are way more contractionary than expenditure-based ones. Disentangling efects between demand components, we nd consumption being in general less responsive to consolidations than investment, although nonlinearities associated to initial levels of debt and taxation might play an important role.