Nonparametric Testing for Differences in Electricity Prices: The Case of the Fukushima Nuclear Accident

TL;DR

This paper develops a nonparametric testing method using Local Linear Kernel estimators to analyze differences in electricity prices before and after the Fukushima nuclear disaster, revealing complex market dynamics.

Contribution

It introduces a novel two-sample test for sparse covariate-adjusted functional data with finite sample correction, addressing size distortions in market price analysis.

Findings

Identifies significant price differences pre- and post-Fukushima

Reveals a Simpson's paradox in electricity market data

Provides a robust nonparametric testing framework for market analysis

Abstract

This work is motivated by the problem of testing for differences in the mean electricity prices before and after Germany's abrupt nuclear phaseout after the nuclear disaster in Fukushima Daiichi, Japan, in mid-March 2011. Taking into account the nature of the data and the auction design of the electricity market, we approach this problem using a Local Linear Kernel (LLK) estimator for the nonparametric mean function of sparse covariate-adjusted functional data. We build upon recent theoretical work on the LLK estimator and propose a two-sample test statistics using a finite sample correction to avoid size distortions. Our nonparametric test results on the price differences point to a Simpson's paradox explaining an unexpected result recently reported in the literature.