Browsing by Author "Shukur, Ghazi"

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    Multivariate based causality tests of twin deficits in the US
    (University of Gothenburg, 2000-01-01) Hatemi-J, Abdulnasser; Shukur, Ghazi
    This paper provides an alternative methodology for testing the causality direction between Twin deficits in the US. Rao' s multivariate F -test combined with bootstraps simulation technique has appealing properties, especially when the data generating process is characterised by unit roots. In addition the results show that the effect of structural breaks are of paramount importance when the causality tests are conducted.
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    Testing for cointegrating relations - A bootstrap approach
    (University of Gothenburg, 1999-05-01) Mantalos, Panagiotis; Shukur, Ghazi
    Using Monte Carlo methods together with the Bootstrap critical values, we have studied the properties of two tests (Trace and L-max), derived by Johansen (1988) for testing for cointegration in V AR systems. Regarding the size of the tests, the results show that both of the test methods perform satisfactorily when there are mixed stationary and nonstationary components in the model. The analyses of the power functions indicate that both of the test methods can effectively detect the present of cointegration vector(s). Finally, when considering the size and power properties, we could not find any noticeable differences between the two test methods.
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    Testing for multivariate heteroscedasticity
    (University of Gothenburg, 2003-01-01) Holgersson, Thomas; Shukur, Ghazi
    In this paper we propose a testing technique for multivariate heteroscedasticity, which is expressed as a test of linear restrictions in a multivariate regression model. Four test statistics with known asymptotical null distributions are suggested, namely the Wald (W), Lagrange Multiplier (LM), Likelihood Ratio (LR) and the multivariate Rao F-test. The critical values for the statistics are determined by their asymptotic null distributions, but also bootstrapped critical values are used. The size, power and robustness of the tests are examined in a Monte Carlo experiment. Our main findings are that all the tests limit their nominal sizes asymptotically, but some of them have superior small sample properties. These are the F, LM and bootstrapped versions of Wand LR tests.
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    The causal nexus of government spending and revenue in Finland: A bootstrap approach
    (University of Gothenburg, 1998-10-01) Hatemi-J, Abdulnasser; Shukur, Ghazi
    Applying VAR(5), a bootstrap simulation approach and a multivariate Rao's F-test indicate that government revenue Granger causes spending in Finland. This does not agree with Barro's tax smoothing hypothesis. The explanation of this is due to the institutional factors that are specific for Finland.
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    The effect of non-normal error terms on the properties of systemwise RESET test
    (University of Gothenburg, 1999-06-01) Shukur, Ghazi
    The small sample properties of the systemwise RESET test for functional misspecification is investigated using normal and non-normal error terms. When using normally distributed or less heavy tailed error terms, we find the Rao's multivariate F-test to be best among all other alternative test methods. Using the bootstrap critical values, however, all test methods perform satisfactorally in almost all situations. However, the test methods perform extremely badly (even the RAG test) when the error terms are very heavy tailed.
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    The robustness of the systemwise Breauch-Godfrey autocorrelation test for non-normal distributed error terms
    (University of Gothenburg, 1998-11-01) Shukur, Ghazi
    Using Monte Carlo methods, the properties of systemwise generalisations of the BreauchGodfrey test for autocorrelated errors are studied in situations when the error terms follow a normal and non-normal distributions. Edgerton and Shukur (1998) studied the properties of the test using normally distributed error terms. When the errors follow a non-normal distribution, the performances of the tests deteriorate especially when the tails are very heavy, and in this case the results are truly remarkable. The performances of the tests become better (as in the case when the errors are generated by the normal distribution) when the errors are less heavy tailed.