Most stringent test of independence for time series

Khan, Waqar Muhammad; Khan, Asad ul Islam

doi:10.1080/03610918.2018.1527350

Communications in Statistics - Simulation and Computation

2018

DOI: 10.1080/03610918.2018.1527350

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Most stringent test of independence for time series

Waqar Muhammad Khan

Asad ul Islam Khan

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2019

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Cited by 2 publications

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“…The detailed discussion on stringency criterion can be found in Zaman et al (2017). The stringency criterion has been used for comparison of tests in a limited number of studies including Khan and Khan (2018) and Islam (2017).…”

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confidence: 99%

Most stringent test of null of cointegration: a Monte Carlo comparison

Khan

Hussan

2019

Communications in Statistics - Simulation and Computation

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To test for the existence of long run relationship, a variety of null of cointegration tests have been developed in literature. This study is aimed at comparing these tests on basis of size and power using stringency criterion: a robust technique for comparison of tests as it provides with a single number representing the maximum difference between a test's power and maximum possible power in the entire parameter space. It is found that in general, asymptotic critical values tends to produce size distortion and size of test is controlled when simulated critical values are used. The simple LM test based on KPSS statistic is the most stringent test at all sample sizes for all three specifications of deterministic component, as it has the maximum difference approaching to zero and lesser than 20% for the entire parameter space.

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confidence: 99%

Most stringent test of null of cointegration: a Monte Carlo comparison

Khan

Hussan

2019

Communications in Statistics - Simulation and Computation

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Two Tests for Dependence (of Unknown Form) between Time Series

Caballero-Pintado

Matilla-García

Rodríguez

et al. 2019

Entropy

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This paper proposes two new nonparametric tests for independence between time series. Both tests are based on symbolic analysis, specifically on symbolic correlation integral, in order to be robust to potential unknown nonlinearities. The first test is developed for a scenario in which each considered time series is independent and therefore the interest is to ascertain if two internally independent time series share a relationship of an unknown form. This is especially relevant as the test is nuisance parameter free, as proved in the paper. The second proposed statistic tests for independence among variables, allowing these time series to exhibit within-dependence. Monte Carlo experiments are conducted to show the empirical properties of the tests

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Most stringent test of independence for time series

Cited by 2 publications

References 20 publications

Most stringent test of null of cointegration: a Monte Carlo comparison

Most stringent test of null of cointegration: a Monte Carlo comparison

Two Tests for Dependence (of Unknown Form) between Time Series

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