5 papers
Kernel Minimum Distance Estimation and Testing with Conditional Moment Restrictions: A Unified Framework
Yuhao Li, Haokun Lu, Xiaojun Song
We propose a unified Kernel Minimum Distance (KMD) framework for estimating and testing models defined by conditional moment restrictions. By embedding conditional moments into a R…
Kernel Two-Sample Testing via Directional Components Analysis
Rui Cui, Yuhao Li, Xiaojun Song
Standard kernel two-sample tests, such as those based on the Maximum Mean Discrepancy (MMD), aggregate squared differences across all directions in a Reproducing Kernel Hilbert Spa…
Finite-Sample Distortion in Kernel Specification Tests: A Perturbation Analysis of Empirical Directional Components
Cui Rui, Li Yuhao, Song Xiaojun
This paper provides a new theoretical lens for understanding the finite-sample performance of kernel-based specification tests, such as the Kernel Conditional Moment (KCM) test. Ra…
A Powerful Chi-Square Specification Test with Support Vectors
Yuhao Li, Xiaojun Song
Specification tests, such as Integrated Conditional Moment (ICM) and Kernel Conditional Moment (KCM) tests, are crucial for model validation but often lack power in finite samples.…
Testing Goodness-of-Fit for Conditional Distributions: A New Perspective based on Principal Component Analysis
Cui Rui, Li Yuhao
This paper introduces a novel goodness-of-fit test technique for parametric conditional distributions. The proposed tests are based on a residual marked empirical process, for whic…