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  • 1.
    Holgersson, Thomas
    Högskolan i Jönköing.
    Simulation of Non-normal Auto Correlated Variables2006In: Journal of Modern Applied Statistical Methods, ISSN 1538-9472, Vol. 5, no 2, p. 408-416Article in journal (Refereed)
    Abstract [en]

    All statistical methods rely on assumptions to some extent. Two assumptions frequently met in statistical analyses are those of normal distribution and independence. When examining robustness properties of such assumptions by Monte Carlo simulations it is therefore crucial that the possible effects of autocorrelation and non-normality are not confounded so that their separate effects may be investigated. This article presents a number of non-normal variables with non-confounded autocorrelation, thus allowing the analyst to specify autocorrelation or shape properties while keeping the other effect fixed

  • 2. Holgersson, Thomas
    et al.
    Karlsson, Peter S.
    Jönköping International Business School, Sweden.
    Model Based vs. Model Independent Tests for Cross-correlation2010In: Journal of Modern Applied Statistical Methods, ISSN 1538-9472, Vol. 9, no 1, p. 75-89Article in journal (Refereed)
    Abstract [en]

    This article discusses the issue of whether cross correlation should be tested by model dependent or model independent methods. Several different tests are proposed and their main properties are investigated analytically and with simulations. It is argued that model independent tests should be used in applied work.

  • 3.
    Mantalos, Panagiotis
    et al.
    Lund University.
    Shukur, Ghazi
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Nationalekonomi och Statistik.
    Sjölander, Per
    Jönköping University.
    The Effect of the GARCH(1,1) on the Granger Causality Test in Stable VAR Models2007In: Journal of Modern Applied Statistical Methods, ISSN 1538-9472, Vol. 6, no 2, article id 12Article in journal (Refereed)
    Abstract [en]

    Using Monte Carlo methods, the properties of Granger causality test in stable VAR models are studied under the presence of different magnitudes of GARCH effects in the error terms. 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 when GARCH effects are imposed among the residuals compared with the case of white noise residuals.

  • 4.
    Shukur, Ghazi
    et al.
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Jönköping University.
    Mantalos, Panagiotis
    Lund University.
    Size and Power of the RESET Test as Applied to Systems of Equations. A Bootstrap Approach2004In: Journal of Modern Applied Statistical Methods, ISSN 1538-9472, Vol. 3, no 2, p. 370-385Article in journal (Refereed)
    Abstract [en]

    The size and power of various generalization of the RESET test for functional misspecification are investigated, using the “Bootsrap critical values”, in systems ranging from one to ten equations. The properties of 8 versions of the test are studied using Monte Carlo methods. The results are then compared with another study of Shukur and Edgerton (2002), in which they used the asymptotic critical values instead and found that in general only one version of the tests works well regarding size properties. In our study, when applying the bootstrap critical values, we find that all the tests exhibits correct size even in large systems. The power of the test is low, however, when the number of equations grows and the correlation between the omitted variables and the RESET proxies is small.

  • 5.
    Shukur, Ghazi
    et al.
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
    Månsson, Kristofer
    BM Kibria, Golam
    A New Liu Type of Estimators for the Restricted SUR Estimator2019In: Journal of Modern Applied Statistical Methods, ISSN 1538-9472Article in journal (Refereed)
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