Robust functional estimation in the multivariate partial linear model

TL;DR

This paper develops a robust, adaptive wavelet-based estimator for the functional component in multivariate partial linear models, effective across various distributions and dimensions, with proven minimax optimality.

Contribution

It introduces a multivariate BlockJS wavelet shrinkage method for adaptive, robust estimation in complex econometric models with unknown error distributions.

Findings

Estimator is adaptive over wide Besov classes.

Method is robust to distributional assumptions.

Achieves local minimax rates at fixed points.

Abstract

We consider the problem of adaptive estimation of the functional component in a multivariate partial linear model where the argument of the function is defined on a $q$-dimensional grid. Obtaining an adaptive estimator of this functional component is an important practical problem in econometrics where exact distributions of random errors and the parametric component are mostly unknown and cannot safely assumed to be normal. An estimator of the functional component that is adaptive in the mean squared sense over the wide range of multivariate Besov classes and robust to a wide choice of distributions of the linear component and random errors is constructed. It is also shown that the same estimator is locally adaptive over the same range of Besov classes and robust over large collections of distributions of the linear component and random errors as well. At any fixed point, this estimator also attains a local adaptive minimax rate. The procedure needed to obtain such an estimator turns out to depend on the choice of the right shrinkage approach in the wavelet domain. We show that one possible approach is to use the multivariate version of the classical BlockJS method. The multivariate version of BlockJS is developed in the manuscript and is shown to represent an independent interest. Finally, the Besov space scale over which the proposed estimator is locally adaptive is shown to depend on the dimensionality of the domain of the functional component; the higher the dimension, the larger the smoothness indicator of Besov spaces must be.