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Sökning: LAR1:lu > Linnéuniversitetet > (2007) > Shukur Ghazi > Mantalos Panagiotis

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  • Mantalos, Panagiotis, et al. (författare)
  • An Examination of the Robustness of the Vector Autoregressive Granger-Causality Test in the Presence of GARCH and Variance Shifts
  • 2007
  • Ingår i: International Review of Business Research Papers. - 1832-9543. ; 3:5, s. 280-296
  • Tidskriftsartikel (refereegranskat)abstract
    • The properties of the Granger-causality test in stationary and stable Vector Autoregressive (VAR) models are studied with different types of volatility processes imposed on the unconditional variance. For this test, it is examined how the size and power properties are affected by different magnitudes of GARCH processes and by structural shifts in the volatility. The study has been conducted by means of Monte Carlo simulations for different sample sizes. Our analysis reveals that substantial GARCH effects influence the size properties of the Granger-causality test, especially in small samples. The power functions of the test are usually slightly lower in the presence of GARCH disturbances compared to the case of white noise residuals. When a structural variance break is imposed, the size problem is rather severe, and the power functions are lower compared to the case with the pure GARCH processes.
  • Mantalos, Panagiotis, et al. (författare)
  • The Robustness of the RESET Test to Non-Normal Error Terms
  • 2007
  • Ingår i: Computational Economics. - Springer. - 0927-7099. ; 30:4, s. 393-408
  • 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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