Errors-in-variables regression for dependent data with estimated error covariance matrix: To prewhiten or not?

TL;DR

This paper compares prewhitening and unprewhitening methods in errors-in-variables regression with dependent data, analyzing their efficiency and computational costs, especially in high-dimensional error covariance scenarios.

Contribution

It provides a comparative analysis of prewhitening versus unprewhitening estimators, highlighting efficiency trade-offs and computational resource requirements.

Findings

Prewhitening does not always improve estimation efficiency.

Prewhitening requires larger ensemble sizes for accurate error covariance estimation.

Prewhitening demands more computational resources than unprewhitening.

Abstract

We consider statistical inference for errors-in-variables regression models with dependent observations under the high dimensionality of the error covariance matrix. It is tempting to prewhiten the model and data that had led to efficient weighted least squares estimation in the presence of the measurement errors, as being practised in the optimal fingerprinting approach in climate change studies. However, it is unclear to what extent the prewhitened estimator can improve the estimation efficiency of the unprewhitened estimator for errors-in-variables regression. We compare the prewhitening and unprewhitening estimators in terms of their estimation efficiency and computational cost. It shows that while the prewhitening operation does not necessarily improve the estimation efficiency of its unprewhitening counterpart, it demands more on the ensemble size needed in the error-covariance matrix estimation to ensure the asymptotic normality, and hence it would requires much more computationally resource.