Description

Chapter 5

These slides accompany Chapter 5 (“Nonparametric Methods”) of the book

Yongmiao Hong, Oliver Linton, Jiajing Sun
Econometrics and Time Series Methods: Theory, Applications, and R Implementation.

The slides provide a systematic introduction to nonparametric econometric and time series methods, focusing on situations where the functional form of relationships is left flexible rather than imposed a priori. They typically cover concepts such as nonparametric regression, kernel smoothing, bandwidth selection, local polynomial methods, and estimation of conditional moments and distributions. The material emphasizes how nonparametric tools can be used to uncover nonlinearities, structural breaks, and other features that parametric models may miss, both in cross-sectional and time series settings.

In line with the book’s integration of theory, applications, and computation, the slides connect the asymptotic theory of nonparametric estimators with practical implementation in R. They illustrate how to estimate nonparametric curves and surfaces, visualize and interpret estimated functions, choose tuning parameters in practice, and apply these methods to real data. The slides are suitable for advanced undergraduate and graduate teaching and can be directly used or adapted by instructors.

Unless otherwise indicated, the slides are shared under the Creative Commons Attribution–NonCommercial 4.0 International License (CC BY-NC 4.0). Readers and instructors who wish to request the LaTeX source files or provide feedback are welcome to contact us at jiajing.sun@gmail.com.