working papers

- Improved Inference for Nonparametric Regression and Regression-Discontinuity Designs (with Giuseppe Cavaliere, Sílvia Gonçalves and Morten Ørregaard Nielsen).

Abstract: We consider inference for (possibly) non-linear conditional expectations in the setup of nonparametric regression and regression-discontinuity designs. In this context, inference is challenging due to asymptotic bias of local polynomial estimators. We propose a novel approach to restore valid inference by means of proper implementations of the bootstrap. Specifically, we show conditions under which, even if the bootstrap test statistic is not able to mimic the behavior of the asymptotic bias – making the bootstrap fail using standard arguments – the large sample distribution of the bootstrap p-value only depends on some nuisance parameters which are easily estimable. We introduce two bootstrap algorithms, namely the local polynomial (LP) and fixed-local (FL) bootstrap, which deliver asymptotically valid confidence intervals (CIs) for both interior and boundary points without requiring undersmoothing or direct bias correction. We demonstrate the theoretical validity and analyze the efficiency properties of these methods, highlighting the asymptotic equivalence of the FL bootstrap-based CIs with robust bias correction (RBC) intervals, while showing that LP bootstrap-based CIs achieve greater efficiency. Monte Carlo simulations confirm the practical relevance of our methods.

- Bootstrapping Stochastic Time-Varying Coefficient Models.

Abstract: We propose a novel local pairs bootstrap (LPB) in the context of autoregressive models with stochastic time-varying coefficients. Asymptotic validity is established under general forms of (conditional and unconditional) heteroskedasticity. In the process, we also show asymptotic validity of a fixed-regressor wild bootstrap (FWB) and compare the finite sample properties of the two methods, in the spirit of Gonçalves and Kilian (2004). We introduce a dynamic bandwidth to allow for local adaptive smoothing and provide two data-driven procedures for calibration. Extensive Monte Carlo simulations show that both bootstrap methods outperform the standard asymptotic estimator and that the LPB is generally preferable. Finally, we implement our theoretical findings to investigate the empirical issue of the evolution of inflation and expected inflation persistence.

- When did the Phillips Curve Become Flat? A time-varying estimate of structural parameters (with Claudio Lissona and Antonio Marsi).

Abstract: We provide a time-varying estimate of the parameters of the New Keynesian Phillips Curve (NKPC) by combining three recent contributions from the literature: (i) a non-parametric estimate of a vector auto-regressive model with time-varying parameters (ii) an identification of a demand shock based on the Excess Bond Premium (iii) a regression-in-impulse-response-functions approach to compute the coefficients of structural macroeconomic equations. Our methodology allows to track the evolution of the NKPC coefficients over time, with a precision which would not be achieved by resorting to rolling windows estimation or simple splits of the sample. We show that, for the US, the structural slope of the NKPC has actually decreased over time and that this decline took place relatively early, with the slope being virtually zero from 1990 onward. Furthermore, we observe a growing importance of expected inflation. Our analysis also allows to dismiss the explanation of a flatter NKPC resorting to a stronger and faster reaction over time of the Federal Reserve to demand shocks. We also show results for the Euro Area and by using a different identification strategy based on sign-restrictions.

PAPERS IN PROGRESS

- Bootstrapping Exogeneity Tests in Linear Models with Possibly Weak Instruments (with Prosper Dovonon and Nikolay Gospodinov).