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  • 1.
    Almasri, Abdullah
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    Tests for Trend: a Simulation Study2010In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 39, no 3, p. 598-611Article in journal (Refereed)
    Abstract [en]

    In this study, we use the wavelet analysis to construct a test statistic to test for the existence of a trend in the series. We also propose a new approach for testing the presence of trend based on the periodogram of the data. Since we are also interested in the presence of a long-memory process among the data, we study the properties of our test statistics under different degrees of dependency. We compare the results when using the band periodogram test and the wavelet test with results obtained by applying the ordinary least squares (OLS) method under the same conditions.

  • 2.
    Holgersson, Thomas
    Högskolan i Jönköping.
    A Modified Skewness Measure for Testing Asymmetry2010In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 39, no 2, p. 335-346Article in journal (Refereed)
    Abstract [en]

    Statistical practitioners frequently wish to know whether a variable is symmetrically distributed. There are a number of different tests available but the most commonly used one is perhaps that based on the standardized third central moment, as defined by Pearson and Fisher in the early 1900's. While this traditional skewness measure uniquely determines the symmetry of a variable within the Pearson family, it does not uniquely determine symmetry for a general distribution. In this article, we propose a modified version of the classical skewness test which is easy to conduct and consistent against a wide family of asymmetric distributions.

  • 3.
    Holgersson, Thomas
    et al.
    Högskolan i Jönköping.
    Lindström, Fredrik
    Göteborgs universitet.
    A Comparison of Conditioned Versus Unconditioned Forecasts of the VAR(1) Process2005In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 34, no 2, p. 415-427Article in journal (Refereed)
    Abstract [en]

    The properties of a forecast usually depend upon whether or not the forecast is conditioned on the final period observation. In the case of unconditioned forecasts, it is well known that the point predictions are unbiased. If, on the other hand, the forecast is conditional, then the forecast may be biased. Existing analytical results in literature are insufficient for describing the properties of the conditioned forecast properly, particularly in multivariate models. This article examines some finite sample properties of conditioned forecasts of the VAR(1) process by means of Monte Carlo experiments. We use a number of parameter settings for the VAR(1) process to demonstrate that the forecast bias of the conditioned forecast may be considerable. Hence, unless the analyst has a clear idea of whether the conditioned or unconditioned forecast is relevant for the time series being analyzed, statistical inferences may be seriously erratic.

  • 4.
    Holgersson, Thomas
    et al.
    Jönköping University.
    Mansoor, Rashid
    Jönköping University.
    Assessing Normality of High-Dimensional Data2013In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 42, no 2, p. 360-369Article in journal (Refereed)
    Abstract [en]

    The assumption of normality is crucial in many multivariate inference methods and may be even more important when the dimension of data is proportional to the sample size. It is therefore necessary that tests for multivariate non normality remain well behaved in such settings. In this article, we examine the properties of three common moment-based tests for non normality under increasing dimension asymptotics (IDA). It is demonstrated through Monte Carlo simulations that one of the tests is inconsistent under IDA and that one of them stands out as uniformly superior to the other two.

  • 5.
    Holgersson, Thomas
    et al.
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics. Jönköping University.
    Månsson, Kristofer
    University of Gothenburg.
    Shukur, Ghazi
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics. Jönköping University.
    Testing for Panel Unit Roots under General Cross-Sectional Dependence2016In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 45, no 5, p. 1785-1801Article in journal (Refereed)
    Abstract [en]

    In this paper we generalize four tests of multivariate linear hypothesis to panel data unit root testing. The test statistics are invariant to certain linear transformations of data and therefore simulated critical values may conveniently be used. It is demonstrated that all four tests remains well behaved in cases of where there are heterogeneous alternatives and cross-correlations between marginal variables. A Monte Carlo simulation is included to compare and contrast the tests with two well-established ones.

  • 6.
    Holgersson, Thomas
    et al.
    Göteborg University.
    Shukur, Ghazi
    Göteborg University.
    Some Aspects of Non-Normality Tests in Systems of Regressions Equations2001In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 30, no 2, p. 291-310Article in journal (Refereed)
    Abstract [en]

    In this paper, a short background of the Jarque and McKenzie (JM) test for non-normality is given, and the small sample properties of the test is examined in view of robustness, size and power. The investigation has been performed using Monte Carlo simulations where factors like, e.g., the number of equations, nominal sizes, degrees of freedom, have been varied.

    Generally, the JM test has shown to have good power properties. The estimated size due to the asymptotic distribution is not very encouraging though. The slow rate of convergence to its asymptotic distribution suggests that empirical critical values should be used in small samples.

    In addition, the experiment shows that the properties of the JM test may be disastrous when the disturbances are autocorrelated. Moreover, the simulations show that the distribution of the regressors may also have a substantial impact on the test, and that homogenised OLS residuals should be used when testing for non-normality in small samples.

  • 7. Hussain, Shakir
    et al.
    Mohamed, A
    Holder, Roger
    Almasri, Abdullah
    Shukur, Ghazi
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Nationalekonomi och Statistik.
    Performance Evaluation Based on the Robust Mahalanobis Distance and Multilevel Modelling Using Two New Strategies2008In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 37, no 10, p. 1966-1980Article in journal (Refereed)
    Abstract [en]

    In this article, we propose a general framework for performance evaluation of organizations and individuals over time using routinely collected performance variables or indicators. Such variables or indicators are often correlated over time, with missing observations, and often come from heavy-tailed distributions shaped by outliers. Two new double robust and model-free strategies are used for evaluation (ranking) of sampling units. Strategy 1 can handle missing data using residual maximum likelihood (RML) at stage two, while strategy two handles missing data at stage one. Strategy 2 has the advantage that overcomes the problem of multicollinearity. Strategy one requires independent indicators for the construction of the distances, where strategy two does not. Two different domain examples are used to illustrate the application of the two strategies. Example one considers performance monitoring of gynecologists and example two considers the performance of industrial firms.

  • 8.
    Javed, Farrukh
    et al.
    Lund University.
    Mantalos, Panagiotis
    Örebro University.
    GARCH-Type Models and Performance of Information Criteria2013In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 42, no 8, p. 1917-1933Article in journal (Refereed)
    Abstract [en]

    This article discusses the ability of information criteria toward the correct selection of different especially higher-order generalized autoregressive conditional heteroscedasticity (GARCH) processes, based on their probability of correct selection as a measure of performance. Each of the considered GARCH processes is further simulated at different parameter combinations to study the possible effect of different volatility structures on these information criteria. We notice an impact from the volatility structure of time series on the performance of these criteria. Moreover, the influence of sample size, having an impact on the performance of these criteria toward correct selection, is observed.

  • 9.
    Kibria, B. M. Golam
    et al.
    Florida International University.
    Månsson, Kristofer
    Internationella Handelshögskolan i Jönköping.
    Shukur, Ghazi
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
    A simulation study of some biasing parameters for the ridge type estimation of Poisson regression2015In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 44, no 4, p. 943-957Article in journal (Refereed)
    Abstract [en]

    This paper proposes several estimators for estimating the ridge parameter k based for Poisson ridge regression (RR) model. These estimators have been evaluated by means of Monte Carlo simulations. As performance criteria, we have calculated the mean squared error (MSE), the mean value and the standard deviation of k. The first criterion is commonly used, while the other two have never been used when analyzing Poisson RR. However, these performance criterion are very informative because, if several estimators have an equal estimated MSE then those with low average value and standard deviation of k should be preferred. Based on the simulated results we may recommend some biasing parameters which may be useful for the practitioners in the field of health, social and physical sciences.

  • 10.
    Li, Yushu
    et al.
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    Shukur, Ghazi
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    Testing for Unit Root Against LSTAR Models: Wavelet Improvement under GARCH Distortion2010In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 39, no 2, p. 277-286Article in journal (Refereed)
    Abstract [en]

    In this article, we propose a nonlinear Dickey-Fuller F test for unit root against first-order Logistic Smooth Transition Autoregressive (LSTAR) (1) model with time as the transition variable. The nonlinear Dickey-Fuller F test statistic is established under the null hypothesis of random walk without drift and the alternative model is a nonlinear LSTAR (1) model. The asymptotic distribution of the test is analytically derived while the small sample distributions are investigated by Monte Carlo experiment. The size and power properties of the test were investigated using Monte Carlo experiment. The results showed that there is a serious size distortion for the test when GARCH errors appear in the Data Generating Process (DGP), which led to an over-rejection of the unit root null hypothesis. To solve this problem, we use the Wavelet technique to count off the GARCH distortion and improve the size property of the test under GARCH error. We also discuss the asymptotic distributions of the test statistics in GARCH and wavelet environments.

  • 11.
    Locking, Håkan
    et al.
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
    Månsson, Kristofer
    Shukur, Ghazi
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
    Performance of Some Ridge Parameters for Probit Regression: With Application to Swedish Job Search Data2013In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 42, no 3, p. 698-710Article in journal (Refereed)
    Abstract [en]

    In ridge regression the estimation of the ridge parameter is an important issue. This paper generalizes some methods for estimating the ridge parameter for probit ridge regression (PRR) model based on the work of Kibria et al. (2011). The performance of these new estimators are judged by calculating the mean square error (MSE) usingMonte Carlo simulations.  In the design of the experiment we chose to vary the sample size and the number of regressors. Furthermore, we generate explanatory variables that are linear combinations of other regressors, which is a common situation in economics. In an empirical application regarding Swedish job search data we also illustrate the benefits of the new method.

  • 12.
    Mantalos, Panagiotis
    et al.
    University of Lund.
    Mattheou, K.
    University of Cyprus, Cyprus.
    Karagrigoriou, A.
    University of Cyprus, Cyprus.
    An Improved Divergence Information Criterion for the Determination of the Order of an AR Process2010In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 39, no 5, p. 865-879Article in journal (Refereed)
    Abstract [en]

    In this article we propose a modification of the recently introduced divergence information criterion (DIC, Mattheou et al., 2009) for the determination of the order of an autoregressive process and show that it is an asymptotically unbiased estimator of the expected overall discrepancy, a nonnegative quantity that measures the distance between the true unknown model and a fitted approximating model. Further, we use Monte Carlo methods and various data generating processes for small, medium, and large sample sizes in order to explore the capabilities of the new criterion in selecting the optimal order in autoregressive processes and in general in a time series context. The new criterion shows remarkably good results by choosing the correct model more frequently than traditional information criteria.

  • 13. Månsson, Kristofer
    et al.
    Shukur, Ghazi
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics.
    Granger Causality Test in the Presence of Spillover Effects2009In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 38, no 10, p. 2039-2059Article in journal (Refereed)
    Abstract [en]

    In this article, we investigate the effect of spillover (i.e., causality in variance) on the reliability of Granger causality test based on ordinary least square estimates. We studied eight different versions of the test both, with and without Whites heteroskedasticity consistent covariance matrix (HCCME). The properties of the tests are investigated by means of a Monte Carlo experiment where 21 different data generating processes (DGP) are used and a number of factors that might affect the test are varied. The result shows that the best choice to test for Granger causality under the presence of spillover is the Lagrange Multiplier test with HCCME.

  • 14.
    Shukur, Ghazi
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    The Robustness of the Systemwise Breauch-Godfrey Autocorrelation Test for Non-normal Error Terms2000In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 29, no 2, p. 419-448Article in journal (Refereed)
  • 15.
    Shukur, Ghazi
    et al.
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
    Doszyń, Mariusz
    Szczecin University, Poland.
    Dmytrów, Krzysztof
    Szczecin University, Poland.
    Comparison of the effectiveness of forecasts obtained by means of selected probability functions with respect to forecast error distributions2017In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 46, no 5, p. 3667-3679Article in journal (Refereed)
    Abstract [en]

    The Forecasting of sales in a company is one of the crucial challenges that must be faced. Nowadays, there is a large spectrum of methods that enable making reliable forecasts. However, sometimes the nature of time series excludes many well-known and widely used forecasting methods (e.g. econometric models). Therefore, the authors decided to forecast on the basis of a seasonally adjusted median of selected probability distributions. The obtained forecasts were verified by means of distributions of the Theil U2 coefficient and unbiasedness coefficient.

  • 16.
    Shukur, Ghazi
    et al.
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    Holgersson, Thomas
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    Some Aspects of Non-Normality Tests in Systems of Regression Equations2001In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 30, no 2, p. 291-310Article in journal (Refereed)
  • 17.
    Shukur, Ghazi
    et al.
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    Månsson, Kristofer
    Kibria, B. M. Golam
    A Simulation Study of Some Ridge Regression Estimators under Different Distributional Assumptions 2010In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 39, no 8, p. 1639-1670Article in journal (Refereed)
    Abstract [en]

    Based on the work of Khalaf and Shukur (2005), Alkhamisi et al. (2006), and Muniz et al. (2010), this article considers several estimators for estimating the ridge parameter k. This article differs from aforementioned articles in three ways: (1) Data are generated from Normal, Student's t, and F distributions with appropriate degrees of freedom; (2) The number of regressors considered are from 4-12 instead of 2-4, which are the usual practice; (3) Both mean square error (MSE) and prediction sum of square (PRESS) are considered as the performance criterion. A simulation study has been conducted to compare the performance of the estimators. Based on the simulation study we found that, increasing the correlation between the independent variables has negative effect on the MSE and PRESS. However, increasing the number of regressors has positive effect on MSE and PRESS. When the sample size increases the MSE decreases even when the correlation between the independent variables is large. It is interesting to note that the dominance pictures of the estimators are remained the same under both the MSE and PRESS criterion. However, the performance of the estimators depends on the choice of the assumption of the error distribution of the regression model.

  • 18.
    Sjölander, Pär
    et al.
    Jönköping University.
    Månsson, Kristofer
    Jönköping University.
    Shukur, Ghazi
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics. Jönköping University.
    A new nonlinear asymmetric cointegration approach using error correction models2017In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 46, no 2, p. 1661-1668Article in journal (Refereed)
    Abstract [en]

    In this article, two new powerful tests for cointegration are proposed. The general idea is based on an intuitively appealing extension of the traditional, rather restrictive cointegration concept. In this paper we allow for a nonlinear, but most importantly a different, asymmetric convergence process to account for negative and positive changes in our cointegration approach. Using Monte Carlo simulations we verify, that the estimated size of the first test depends on the unknown value of a signal-to-noise ratio q. However, our second test – which is based on the original ideas of Kanioura and Turner (2005) – is more successful and robust in the sense that it works in all of the different evaluated situations. Furthermore it is shown to be more powerful than the traditional residual based Enders and Siklos (2001) method. The new optimal test is also applied in an empirical example in order to test for potential nonlinear asymmetric price transmission effects on the Swedish power market. We find that there is a higher propensity for power retailers to rapidly and systematically increase their retail electricity prices subsequent to increases in Nordpool's wholesale prices, than there is for them to reduce their prices subsequent to a drop in wholesale spot prices.

  • 19.
    Sjölander, Pär
    et al.
    Jönköping University ; HUI Research.
    Månsson, Kristofer
    Jönköping University ; HUI Research.
    Shukur, Ghazi
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics. Jönköping University ; HUI Research.
    Testing for panel cointegration in an error-correction framework with an application to the Fisher hypothesis2017In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 46, no 3, p. 1735-1745Article in journal (Refereed)
    Abstract [en]

    In this article, three innovative panel error-correction model (PECM) tests are proposed. These tests are based on the multivariate versions of the Wald (W), likelihood ratio (LR), and Lagrange multiplier (LM) tests. Using Monte Carlo simulations, the size and power of the tests are investigated when the error terms exhibit both cross-sectional dependence and independence. We find that the LM test is the best option when the error terms follow independent white-noise processes. However, in the more empirically relevant case of cross-sectional dependence, we conclude that the W test is the optimal choice. In contrast to previous studies, our method is general and does not rely on the strict assumption that a common factor causes the cross-sectional dependency. In an empirical application, our method is also demonstrated in terms of the Fisher effect—a hypothesis about the existence of which there is still no clear consensus. Based on our sample of the five Nordic countries we utilize our powerful test and discover evidence which, in contrast to most previous research, confirms the Fisher effect.

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