arXiv 9 Nov 2021 · Econometrics
arXiv:2111.04919 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Campbell, B. and J.-M. Dufour (1995) Exact nonparametric orthogonality and random walk tests | 1.000 | 5 | 3 | 100% |
| 2 | Dufour, J.-M. and A. Taamouti (2010) Exact optimal inference in regression models under heteroskedasticity and non-normality of unknown form | 0.983 | 20 | 7 | 95% |
| 3 | Panagiotelis, A., C. Czado, and H. Joe (2012) Pair copula constructions for multivariate discrete data | 0.950 | 7 | 4 | 86% |
| 4 | Joe, H (2014) Dependence modeling with copulas | 0.928 | 5 | 3 | 80% |
| 5 | White, H (1980) A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity | 0.843 | 3 | 3 | 100% |
| 6 | Sklar, M (1959) Fonctions de repartition an dimensions et leurs marges | 0.737 | 3 | 3 | 67% |
| 7 | Coudin, E. and J.-M. Dufour (2009) Finite-sample distribution-free inference in linear median regressions under heteroscedasticity and non-linear dependence of unk… | 0.737 | 3 | 2 | 100% |
| 8 | Denuit, M. and P. Lambert (2005) Constraints on concordance measures in bivariate discrete data | 0.644 | 3 | 2 | 67% |
| 9 | Joe, H (1997) Multivariate models and multivariate dependence concepts | 0.644 | 2 | 2 | 100% |
| 10 | King, M. L (1987) Towards a theory of point optimal testing | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 43 scored citations.