| 1. |
- Mantalos, Panagiotis, et al.
(författare)
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The Effect of the Spillover on the Granger Causality Test
- 2010
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Ingår i: Journal of Applied Statistics. - : Taylor & Francis. - 0266-4763 .- 1360-0532. ; 37:9, s. 1473-1486
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we investigate the properties of the Granger causality test in stationary and stable vector autoregressive models under the presence of spillover effects, i.e., causality in variance. The Wald test and the WW test (the Wald test with White’s proposed heteroskedasticity-consistent covariance matrix estimator imposed) are analyzed. The investigation is undertaken by using Monte Carlo simulation in which two different sample sizes and six different kinds of data generating process are used. The results show that the Wald test overrejects the null hypothesis both with and without the spillover effect, and that the overrejection in the latter case is more severe in larger samples. The size properties of the WW test are satisfactory when there is spillover between the variables. Only when there is feedback in the variance is the size of the WW test slightly affected. The Wald test is shown to have higher power than the WW test when the errors follow a GARCH(1,1) process without a spillover effect. When there is spillover, the power of both tests deteriorates, which implies that the spillover has a negative effect on the causality tests.
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| 2. |
- Mantalos, Panagiotis, et al.
(författare)
-
The Robustness of the RESET Test to Non-Normal Error Terms
- 2007
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Ingår i: Computational Economics. - : Springer Science and Business Media LLC. - 0927-7099 .- 1572-9974. ; 30:4, s. 393-408
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Tidskriftsartikel (refereegranskat)abstract
- In systems ranging from 1 to 10 equations, the size and power of various generalization of the Regression Specification Error Test (RESET) test for functional misspecification are investigated, using both the assymptotic and the bootsrap critical values. Furthermore, the robusteness of the RESET test to various numbers of non-normal error terms has been investigated. The properties of eight versions of the test are studied using Monte Carlo methods. Using the assyptotic critical values together with normally distributed error terms,we find theRao’smultivariate F-test to be best among all other alternative testmethods (i.e.Wald, Lagrange Multiplier and Likelihood Ratio). In the cases of heavy tailed error terms, short or long tailed errors, however, the properties of the bestRao test deteriorates especially in larg systems of equations.By using the bootstrap critical values, we find that the Rao test exhibits correct size but still slightlyunder reject the null hypothesis in cases when the error terms are short tailed. The powerof the test is low, however, in small samples and when the number of equations grows.
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