working papers

- Improved Inference for Nonparametric Regression (with Giuseppe Cavaliere, Sílvia Gonçalves and Morten Ørregaard Nielsen), arXiv:2512.00566.

[draft, R packages]

Abstract: Nonparametric regression estimators, including those employed in regression-discontinuity designs (RDD), are central to the economist's toolbox. Their application, however, is complicated by the presence of asymptotic bias, which undermines coverage accuracy of conventional confidence intervals. Extant solutions to the problem include debiasing methods, such as the widely applied robust bias-corrected (RBC) confidence interval of Calonico et al. (2014, 2018). We show that this interval is equivalent to a prepivoted interval based on an invalid residual-based bootstrap method. Specifically, prepivoting performs an implicit bias correction while adjusting the nonparametric regression estimator's standard error to account for the additional uncertainty introduced by debiasing. This idea can also be applied to other bootstrap schemes, leading to new implicit bias corrections and corresponding standard error adjustments. We propose a prepivoted interval based on a bootstrap that generates observations from nonparametric regression estimates at each regressor value and show how it can be implemented as an RBC-type interval without the need for resampling. Importantly, we show that the new interval is shorter than the existing RBC interval. For example, with the Epanechnikov kernel, the length is reduced by 17%, while maintaining accurate coverage probability. This result holds irrespectively of: (a) the evaluation point being in the interior or on the boundary; (b) the use of a 'small' or 'large' bandwidths; (c) the distribution of the regressor and the error term.

- 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).

- Bootstrapping Stochastic Time-Varying Coefficient Models.