Nonparametric Inference via Bootstrapping the Debiased Estimator

In this paper, we propose to construct confidence bands by bootstrapping the debiased kernel density estimator (for density estimation) and the debiased local polynomial regression estimator (for regression analysis). The idea of using a debiased estimator was recently employed by Calonico et al. (2018b) to construct a confidence interval of the density function (and regression function) at a given point by explicitly estimating stochastic variations. We extend their ideas of using the debiased estimator and further propose a bootstrap approach for constructing simultaneous confidence bands. This modified method has an advantage that we can easily choose the smoothing bandwidth from conventional bandwidth selectors and the confidence band will be asymptotically valid. We prove the validity of the bootstrap confidence band and generalize it to density level sets and inverse regression problems. Simulation studies confirm the validity of the proposed confidence bands/sets. We apply our approach to an Astronomy dataset to show its applicabilityComment: Accepted to the Electronic Journal of Statistics. 64 pages, 6 tables, 11 figure

Climate Change and Crop Yield Distribution: Some New Evidence From Panel Data Models

This study examines the impact of climate on the yields of seven major crops in Taiwan based on pooled panel data for 15 prefectures over the 1977-1996 period. Unit-root tests and maximum likelihood methods involving a panel data model are explored to obtain reliable estimates. The results suggest that climate has different impacts on different crops and a gradual increase in crop yield variation is expected as global warming prevails. Policy measures to counteract yield variability should therefore be carefully evaluated to protect farmers from exposure to these long-lasting and increasingly climate-related risks.Yield response, Climate change, Panel data, Unit-root test