Pair copula constructions of point-optimal sign-based tests for predictive linear and nonlinear regressions

TL;DR

This paper introduces pair copula-based point-optimal sign tests for predictive regressions, effectively capturing serial dependence and robustly handling heavy-tailed, heteroskedastic, and persistent errors, with demonstrated superior performance in simulations.

Contribution

It develops a novel pair copula construction method for sign tests that improves robustness and power in complex regression settings with serial dependence and heavy tails.

Findings

Tests are exact and valid under heavy tails and heteroskedasticity.

Proposed tests outperform existing methods in size and power in simulations.

Method allows construction of confidence regions for regression parameters.

Abstract

We propose pair copula constructed point-optimal sign tests in the context of linear and nonlinear predictive regressions with endogenous, persistent regressors, and disturbances exhibiting serial (nonlinear) dependence. The proposed approach entails considering the entire dependence structure of the signs to capture the serial dependence, and building feasible test statistics based on pair copula constructions of the sign process. The tests are exact and valid in the presence of heavy tailed and nonstandard errors, as well as heterogeneous and persistent volatility. Furthermore, they may be inverted to build confidence regions for the parameters of the regression function. Finally, we adopt an adaptive approach based on the split-sample technique to maximize the power of the test by finding an appropriate alternative hypothesis. In a Monte Carlo study, we compare the performance of the proposed "quasi"-point-optimal sign tests based on pair copula constructions by comparing its size and power to those of certain existing tests that are intended to be robust against heteroskedasticity. The simulation results maintain the superiority of our procedures to existing popular tests.