Testing Conditional Independence Restrictions

Linton, Oliver; Gozalo, Pedro

doi:10.1080/07474938.2013.825135

Cited by 20 publications

(10 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, as this test suffers from a fundamental inconsistency problem, [ 9 ] proposed a modified, consistent version of the test based on kernel density estimators, hereafter referred to as the DP test. Alternative semiparametric and nonparametric tests for conditional independence have been proposed based on, among other, additive models [ 10 ], the Hellinger distance measure [ 11 ], copulas [ 12 ], generalized empirical distribution functions [ 13 ], empirical likelihood ratios [ 14 ] and characteristic functions [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

A Consistent Nonparametric Test for Granger Non-Causality Based on the Transfer Entropy

Diks

Fang

2020

Entropy

View full text Add to dashboard Cite

To date, testing for Granger non-causality using kernel density-based nonparametric estimates of the transfer entropy has been hindered by the intractability of the asymptotic distribution of the estimators. We overcome this by shifting from the transfer entropy to its first-order Taylor expansion near the null hypothesis, which is also non-negative and zero if and only if Granger causality is absent. The estimated Taylor expansion can be expressed in terms of a U-statistic, demonstrating asymptotic normality. After studying its size and power properties numerically, the resulting test is illustrated empirically with applications to stock indices and exchange rates.

show abstract

Section: Introductionmentioning

confidence: 99%

A Consistent Nonparametric Test for Granger Non-Causality Based on the Transfer Entropy

Diks

Fang

2020

Entropy

View full text Add to dashboard Cite

show abstract

“…As just seen, the hypothesis of G non-causality, and thus of structural non-causality under the conditional exogeneity assumption, is a speci…c conditional independence. In the literature, there are many conditional independence tests that apply to IID data (e.g., see Delgado and González-Manteiga, 2001;Fernandes and Flores, 2001;Su and White, 2007, 2014Song, 2009;Linton and Gozalo, 2014;Huang, Sun, and White, 2016). These methods can be computationally challenging, as they are non-parametric.…”

Section: Testing For G Causality and Structural Causality In Cross-sementioning

confidence: 99%

Granger Causality and Structural Causality in Cross-Section and Panel Data

White

2016

Econom. Theory

View full text Add to dashboard Cite

Granger noncausality in distribution is fundamentally a probabilistic conditional independence notion that can be applied not only to time series data but also to cross-section and panel data. In this paper, we provide a natural definition of structural causality in cross-section and panel data and forge a direct link between Granger (G–) causality and structural causality under a key conditional exogeneity assumption. To put it simply, when structural effects are well defined and identifiable,G–non-causality follows from structural noncausality, and with suitable conditions (e.g., separability or monotonicity), structural causality also impliesG–causality. This justifies using tests ofG–non-causality to test for structural noncausality under the key conditional exogeneity assumption for both cross-section and panel data. We pay special attention to heterogeneous populations, allowing both structural heterogeneity and distributional heterogeneity. Most of our results are obtained for the general case, without assuming linearity, monotonicity in observables or unobservables, or separability between observed and unobserved variables in the structural relations.

show abstract

“…Huang (2010) proposed a test based on maximal nonlinear conditional correlation. Linton and Gozalo (2014) developed Kolmogorov-Smirnov and Cramér-von Mises tests based on a generalized empirical distribution. Wang et al (2015) provided a nonparametric measure of conditional correlation based on the conditional characteristic function for conditional independence.…”

Section: Introductionmentioning

confidence: 99%

Testing for conditional independence: A groupwise dimension reduction‐based adaptive‐to‐model approach

Zhu

Lü

Zhang

et al. 2021

Scandinavian J Statistics

View full text Add to dashboard Cite

In this article, we propose an adaptive-to-model test for conditional independence through groupwise dimension reduction developed in sufficient dimension reduction field. The test statistic under the null hypothesis is asymptotically normally distributed. Although it is also based on nonparametric estimation like any local smoothing tests for conditional independence, its behavior is similar to existing local smoothing tests with only the number of covariates under the null hypothesis. Furthermore, it can detect local alternatives distinct from the null at the rate that is also only related to the number of covariates under the null hypothesis. Therefore, the curse of dimensionality is largely alleviated. To achieve the above goal, we also suggest a groupwise least squares estimation for the groupwise central subspace in sufficient dimension reduction. It is of its own importance in estimation theory though it is as a by-product for the model adaptation of test statistic described herewith. Numerical studies and analyses for two real data sets are then conducted to examine the finite sample performance of the proposed test.

show abstract

Testing Conditional Independence Restrictions

Cited by 20 publications

References 37 publications

A Consistent Nonparametric Test for Granger Non-Causality Based on the Transfer Entropy

A Consistent Nonparametric Test for Granger Non-Causality Based on the Transfer Entropy

Granger Causality and Structural Causality in Cross-Section and Panel Data

Testing for conditional independence: A groupwise dimension reduction‐based adaptive‐to‐model approach

Contact Info

Product

Resources

About