lspartition-package: Nonparametric Estimation and Inference using...

Description Author(s) References


This package provides tools for statistical analysis using B-splines, wavelets, and piecewise polynomials (generalized regressogram) as described in Cattaneo, Farrell and Feng (2018a). lsprobust for least squares point estimation with robust bias-corrected pointwise and uniform inference procedures. lspkselect for data-driven procedures for selecting the IMSE-optimal number of partitioning knots. lsprobust.plot for regression plots with robust confidence intervals and confidence bands. lsplincom for estimation and inference for linear combination of regression functions of different groups.

The companion software article, Cattaneo, Farrell and Feng (2018b), provides further implementation details and empirical illustration.


Matias D. Cattaneo, University of Michigan, Ann Arbor, MI. [email protected].

Max H. Farrell, University of Chicago, Chicago, IL. [email protected].

Yingjie Feng, University of Michigan, Ann Arbor, MI. [email protected].


Cattaneo, M. D., M. H. Farrell, and Y. Feng (2018a): Large Sample Properties of Partitioning-Based Series Estimators. Working paper.

Cattaneo, M. D., M. H. Farrell, and Y. Feng (2018b): lspartition: Partitioning-Based Least Squares Regression. Working paper.

lspartition documentation built on Dec. 4, 2018, 1:04 a.m.