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  • 1. A. Alkhamisi, Mahdi
    et al.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics. Statistik.
    A Monte Carlo Study of Recent Ridge Parameters2007In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 36, no 3, p. 535-547Article in journal (Refereed)
  • 2. A. Alkhamisi, Mahdi
    et al.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Developing Ridge Parameters for SUR Model2008In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 37, no 4, p. 544-564Article in journal (Refereed)
  • 3. A. Alkhamisi, Mahdi
    et al.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    The Effect of Fat-Tailed Error Terms on the Properties of the Systemwise RESET Test2008In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 35, no 1, p. 101-113Article in journal (Refereed)
  • 4. Alkamisi, M. A.
    et al.
    Khalaf, G.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Some Modifications for Choosing Ridge Parameters2006In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 35, no 11, p. 2005-2020Article in journal (Refereed)
  • 5. Alkamisi, M. A.
    et al.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Bayesian Analysis of a Linear Mixed Model with AR(p) errors Via MCMC2005In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 32, no 7, p. 741-755Article in journal (Refereed)
  • 6. Almasri, Abdullah
    et al.
    Locking, Håkan
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Testing for climate warming in Sweden during 1850-1999, using wavelets analysis2008In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 35, no 4, p. 431-443Article in journal (Refereed)
  • 7.
    Almasri, Abdullah
    et al.
    Department of Economics and Statistics, Karlstad University, Karlstad, Sweden.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics. Department of Economics and Statistics, Göteborg University, Göteborg, Sweden.
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Statistics. Department of Economics and Statistics, Linnaeus University, Växjö, Sweden.
    A wavelet-based panel unit-root test in the presence of an unknown structural break and cross-sectional dependency, with an application of purchasing power parity theory in developing countries2017In: Applied Economics, ISSN 0003-6846, E-ISSN 1466-4283, Vol. 49, no 21, p. 2096-2105Article in journal (Refereed)
    Abstract [en]

    This article introduces two different non-parametric wavelet-based panel unit-root tests in the presence of unknown structural breaks and cross-sectional dependencies in the data. These tests are compared with a previously suggested non-parametric wavelet test, the parameteric Im-Pesaran and Shin (IPS) test and a Wald type of test. The results from the Monte Carlo simulations clearly show that the new wavelet-ratio tests are superior to the traditional tests both in terms of size and power in panel unit-root tests because of its robustness to cross-section dependency and structural breaks. Based on an empirical Central American panel application, we can, in contrast to previous research (where bias due to structural breaks is simply disregarded), find strong, clear-cut support for purchasing power parity (PPP) in this developing region.

  • 8. Almasri, Abdullah
    et al.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Clustering using Wavelet Transformation2008In: Handbook of research on cluster theory, Cheltenham: Edward Elgar , 2008, p. 169-186Chapter in book (Other (popular science, discussion, etc.))
  • 9. Almasri, Abdullah
    et al.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Testing the casual relation between sunspots and temperature using wavelets analysis2005In: Journal of Modern Applied Statistical Methods, ISSN 1538-9472, Vol. 4, no 1, p. 134-139Article in journal (Refereed)
  • 10.
    Andersson, Lina
    et al.
    Department of Economics and Statistics, Linnaeus University, Växjö, Sweden.
    Hammarstedt, Mats
    Department of Economics and Statistics, Linnaeus University, Växjö, Sweden.
    Hussain, Shakir
    School of Medicine, University of Birmingham, Birmingham, UK.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Ethnic origin, local labour markets and self-employment in Sweden: A Multilevel Approach2013In: The annals of regional science, ISSN 0570-1864, E-ISSN 1432-0592, Vol. 50, no 3, p. 885-910Article in journal (Refereed)
    Abstract [en]

    We investigate the importance of ethnic origin and local labour markets conditions for self-employment propensities in Sweden. In line with previous research, we find differences in the self-employment rate between different immigrant groups as well as between different immigrant cohorts. We use a multilevel regression approach in order to quantify the role of ethnic background, point of time for immigration and local market conditions in order to further understand differences in self-employment rates between different ethnic groups. We arrive at the following: The self-employment decision is to a major extent guided by factors unobservable in register data. Such factors might be, that is, individual entrepreneurial ability and access to financial capital. The individual’s ethnic background and point of time for immigration play a smaller role for the self-employment decision but are more important than local labour market conditions.

  • 11.
    Anxo, Dominique
    et al.
    Department of Economics and Statistics, Linnaeus University, Växjö, Sweden .
    Hussain, Shakir
    Public Health, Epidemiology and Biostatistics, School of Medicine, University of Birmingham, Birmingham, UK .
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    The demand of part-time in European companies: a multilevel modelling approach2012In: Applied Economics, ISSN 0003-6846, E-ISSN 1466-4283, Vol. 44, no 8, p. 1057-1066Article in journal (Refereed)
    Abstract [en]

    Part-time work is one of the most well-known « atypical » working time arrangements. In contrast to previous studies focusing on the supply side, the originality of our research is to investigate the demand-side of part-time work and to examine how and why companies use part-time work. Based on a large and unique sample of European firms operating in 21 member states, we use a multilevel multinomial modeling in a Bayesian environment. Our results suggest that the variations in the extent of part-time workers at the establishment level is determined more by country-specific features than by industry specific factors.

  • 12. Edgerton, David
    et al.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Some Questions Concerning Dynamic Almost Ideal Demand Systems1996In: Applied Economics Letters, ISSN 1350-4851, E-ISSN 1466-4291, Vol. 3, no 11, p. 693-695Article in journal (Refereed)
  • 13. Edgerton, David
    et al.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Testing Autocorrelation in a System Perspective1999In: Econometric Reviews, ISSN 0747-4938, E-ISSN 1532-4168, Vol. 18, no 4, p. 343-386Article in journal (Refereed)
  • 14. Hammarstedt, Mats
    et al.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Immigrants' relative earnings in Sweden: A cohort analysis2006In: Labour, ISSN 1121-7081, E-ISSN 1467-9914, Vol. 20, no 2, p. 285-323Article in journal (Refereed)
  • 15. Hammarstedt, Mats
    et al.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Immigrants' relative earnings in Sweden: A quantile regression approach2007In: International journal of manpower, ISSN 0143-7720, E-ISSN 1758-6577, Vol. 28, no 6, p. 456-473Article in journal (Refereed)
  • 16.
    Hammarstedt, Mats
    et al.
    Centre for Labour Market Policy Research (CAFO), Växjö University, Sweden.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Testing the home-country self-employment hypothesis on immigrants in Sweden2009In: Applied Economics Letters, ISSN 1350-4851, E-ISSN 1466-4291, Vol. 16, no 7, p. 745-748Article in journal (Refereed)
    Abstract [en]

    This article tests the home-country self-employment hypothesis on immigrants in Sweden. The results show that the self-employment rates vary between different immigrant groups but we find no support for the home-country self-employment hypothesis using traditional estimation methods. However, when applying quantile regression method we find such evidence when testing results from the 90th quantile. This indicates that home-country self-employment traditions are important for the self-employment decision among immigrant groups with high self-employment rates in Sweden. Furthermore, the result underlines the importance of utilizing robust estimation methods when the home-country self-employment hypothesis is tested.

  • 17.
    Holgersson, Thomas
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics. Linnaeus University, Sweden.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics. Göteborg University, Sweden .
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    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.

  • 18.
    Holgersson, Thomas
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    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.

  • 19.
    Holgersson, Thomas
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics. Statistik.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics. Statistik.
    Testing for Multivariate Heteroscedasticity2004In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 74, no 12, p. 879-896Article in journal (Refereed)
  • 20.
    Hussai, Shakir
    et al.
    Linnaeus University.
    Mohammed, Mohamed A
    University of Birmingham.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Congenial Multiple Imputation and Matched Pairs Models for Square Tables2013In: Journal of Business Administration Research, ISSN 1927-9507, Vol. 2, no 1, p. -8Article in journal (Refereed)
    Abstract [en]

    Experimental studies often measure an individual’s quality of life before and after an intervention, with the data organized into a square table and analyzed using matched pair modeling. However, it is not unusual to find missing data in either round (i.e., before and/or after) of such studies and the use of multiple imputations with matched-pair modeling remains relatively unreported in the applied statistics literature. In this paper we introduce an approach which maintains dependency of responses over time and makes a match between the imputer and the analyst. We use ‘before’ and ‘after’ quality-of-life data from a randomized controlled trial to demonstrate how multiple imputation and matched-pair modeling can be congenially combined, avoiding a possible mismatch of imputation and analyses, and to derive a properly consolidated analysis of the quality-of-life data. We illustrate this strategy with a real-life example of one item from a quality-of-life study that evaluates the effectiveness of patients’ self-management of anticoagulation versus standard care as part of a randomized controlled trial.

  • 21. Hussain, S.
    et al.
    Elbergali, A.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Almasri, A.
    Parsimonious modelling, testing and forecasting of long-range dependence in wind speed2004In: Environmetrics, ISSN 1180-4009, E-ISSN 1099-095X, Vol. 15, no 2, p. 155-171Article in journal (Refereed)
  • 22. Hussain, Shakir
    et al.
    A. Mohamed, Mohamed
    Holder, Roger
    Almasri, Abdullah
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Performance Evaluation Based on the Robust Mahalanobis Distance and Multilevel Modeling 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)
  • 23. Hussain, Shakir
    et al.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Estimation and Forecasting of Hospital Admission Due to Influenza: Planning for Winter Pressure. The Case of the West Midlands, UK2005In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 32, no 3, p. 191-205Article in journal (Refereed)
  • 24. Khalaf, G.
    et al.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Choosing Ridge Parameter for Regression Problems2005In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 34, no 5, p. 1177-1182Article in journal (Refereed)
  • 25.
    Khalaf, Ghadban
    et al.
    Department of Mathematics, King Khalid University, Abha, Saudi Arabia.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Modified Ridge Regression Estimators2013In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 42, no 8, p. 1476-1487Article in journal (Refereed)
    Abstract [en]

    Ridge regression is a variant of ordinary multiple linear regression whose goal is to circumvent the problem of predictors collinearity. It gives up the Ordinary Least Squares (OLS) estimator as a method for estimating the parameters [] of the multiple linear regression model [] . Different methods of specifying the ridge parameter k were proposed and evaluated in terms of Mean Square Error (MSE) by simulation techniques. Comparison is made with other ridge-type estimators evaluated elsewhere. The new estimators of the ridge parameters are shown to have very good MSE properties compared with the other estimators of the ridge parameter and the OLS estimator. Based on our results from the simulation study, we may recommend the new ridge parameters to practitioners.

  • 26.
    Khalaf, Ghadban
    et al.
    Department of Mathematics, King Khalid University, Saudi Arabia.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    A Tobit Ridge Regression Estimator2014In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 43, no 1, p. 131-140Article in journal (Refereed)
    Abstract [en]

    This article analyzes the effects of multicollienarity on the maximum likelihood (ML) estimator for the Tobit regression model. Furthermore, a ridge regression (RR) estimator is proposed since the mean squared error (MSE) of ML becomes inflated when the regressors are collinear. To investigate the performance of the traditional ML and the RR approaches we use Monte Carlo simulations where the MSE is used as performance criteria. The simulated results indicate that the RR approach should always be preferred to the ML estimation method.

  • 27.
    Kibria, B. M. Golam
    et al.
    Department of Mathematics and Statistics, Florida International University, USA.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Performance of some logistic ridge regression estimators2012In: Computational Economics, ISSN 0927-7099, E-ISSN 1572-9974, Vol. 40, no 4, p. 401-414Article in journal (Refereed)
    Abstract [en]

    In this paper we generalize different approaches of estimating the ridge parameter k proposed by Muniz et al. (Comput Stat, 2011) to be applicable for logistic ridge regression (LRR). These new methods of estimating the ridge parameter in LRR are evaluated by means of Monte Carlo simulations along with the some other estimators of k that has already been evaluated by Månsson and Shukur (Commun Stat Theory Methods, 2010) together with the traditional maximum likelihood (ML) approach. As a performance criterion we use the mean squared error (MSE). In the simulation study we also calculate the mean value and the standard deviation of k. The average value is interesting firstly in order to see what values of k that are reasonable and secondly if several estimators have equal variance then the estimator that induces the smallest bias should be chosen. The standard deviation is interesting as a performance criteria if several estimators of k have the same MSE, then the most stable estimator (with the lowest standard deviation) should be chosen. The result from the simulation study shows that LRR outperforms ML approach. Furthermore, some of new proposed ridge estimators outperformed those proposed by Månsson and Shukur (Commun Stat Theory Methods, 2010).

  • 28.
    Kibria, B. M. Golam
    et al.
    Department of Mathematics and Statistics, Florida International University, Miami, Florida, USA.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, 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.

  • 29.
    Kibria, B. M. Golam
    et al.
    Department of Mathematics and Statistics, Florida International University, Miami, Florida, USA.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Some ridge regression estimators for the zero-inflated Poisson model2013In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 40, no 4, p. 721-735Article in journal (Refereed)
    Abstract [en]

    The zero-inflated Poisson regression model is commonly used when analyzing economic data that come in the form of non-negative integers since it accounts for excess zeros and overdispersion of the dependent variable. However, a problem often encountered when analyzing economic data that has not been addressed for this model is multicollinearity. This paper proposes ridge regression (RR) estimators and some methods for estimating the ridge parameter k for a non-negative model. A simulation study has been conducted to compare the performance of the estimators. Both mean squared error and mean absolute error are considered as the performance criteria. The simulation study shows that some estimators are better than the commonly used maximum-likelihood estimator and some other RR estimators. Based on the simulation study and an empirical application, some useful estimators are recommended for practitioners.

  • 30.
    Li, Yushu
    et al.
    Center for Labor Market Policy Research (CAFO), Department of Economic and Statistics, Linnaeus University, Växjö, Sweden.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics. Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Linear and Nonlinear Causality Test in LSTAR Models: Wavelet Decomposition in Nonlinear Environment2011In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 81, no 12, p. 1913-1925Article in journal (Refereed)
    Abstract [en]

    In this paper, we use simulated data to investigate the power of different causality tests in a two-dimensional vector autoregressive (VAR) model. The data are presented in a nonlinear environment that is modelled using a logistic smooth transition autoregressive function. We use both linear and nonlinear causality tests to investigate the unidirection causality relationship and compare the power of these tests. The linear test is the commonly used Granger causality F test. The nonlinear test is a non-parametric test based on Baek and Brock [A general test for non-linear Granger causality: Bivariate model. Tech. Rep., Iowa State University and University of Wisconsin, Madison, WI, 1992] and Hiemstra and Jones [Testing for linear and non-linear Granger causality in the stock price–volume relation, J. Finance 49(5) (1994), pp. 1639–1664]. When implementing the nonlinear test, we use separately the original data, the linear VAR filtered residuals, and the wavelet decomposed series based on wavelet multiresolution analysis. The VAR filtered residuals and the wavelet decomposition series are used to extract the nonlinear structure of the original data. The simulation results show that the non-parametric test based on the wavelet decomposition series (which is a model-free approach) has the highest power to explore the causality relationship in nonlinear models.

  • 31.
    Li, Yushu
    et al.
    Department of Economic and Statistics, Center for Labor Market Policy Research (CAFO), Linaeus University, Växjö, Sweden.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, 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.

  • 32.
    Li, Yushu
    et al.
    Center for Labor Market Policy Research (CAFO), Department of Economic and Statistics, Linnaeus University, Växjö, Sweden.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Testing for unit roots in panel data using a wavelet ratio method2013In: Computational Economics, ISSN 0927-7099, E-ISSN 1572-9974, Vol. 41, no 1, p. 59-69Article in journal (Refereed)
    Abstract [en]

    For testing unit root in single time series, most tests concentrate on the time domain. Recently, Fan and Gençay (Econom Theory 26:1305–1331, 2010) proposed a wavelet ratio test which took advantage of the information from the frequency domain by using a wavelet spectrum methodology. This test shows a better power than many time domain based unit root tests including the Dickey–Fuller (J Am Stat Assoc74:427–431, 1979) type of test in the univariate time series case. On the other hand, various unit root tests in multivariate time series have appeared since the pioneering work of Levin and Lin (Unit root test in panel data: new results, University of California at San Diego, Discussion Paper, 1993). Among them, the Im–Pesaran–Shin (IPS) (J Econ 115(1):53–74, 1997) test is widely used for its straightforward implementation and robustness to heterogeneity. The IPS test is a group mean test which uses the average of the test statistics for each single series. As the test statistics in each seriescan be flexible, this paper will apply the wavelet ratio statistic to give a comparison with the test by using Dickey–Fuller t statistic in the single series. Simulation results show a gain in power by employing the wavelet ratio test instead of the Dickey–Fullert statistic in the panel data case. As the IPS test is sensitive to cross sectional dependence, we further compare the robustness of both test statistics when there exists crosscorrectional dependence among the units in the panel data. Finally we apply a residual based wavestrapping methodology to reduce the over biased size problem brought up by the cross correlation for both test statistics.

  • 33.
    Li, Yushu
    et al.
    Department of Economic and Statistics, Center for Labor Market Policy Research (CAFO), Linnaeus University, Sweden .
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Wavelet Improvement of the Over-rejection of Unit root test under GARCH errors: An Application to Swedish Immigration Data2011In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 40, no 13, p. 2385-2396Article in journal (Refereed)
    Abstract [en]

    In this article, we use the wavelet technique to improve the over-rejection problem of the traditional Dickey-Fuller tests for unit root when the data is associated with volatility like the GARCH(1, 1) effect. The logic of this technique is based on the idea that the wavelet spectrum decomposition can separate out information of different frequencies in the data series. We prove that the asymptotic distribution of the test in the wavelet environment is still the same as the traditional Dickey-Fuller type of tests. The finite sample property is improved when the data suffers from GARCH error. The investigation of the size property and the finite sample distribution of the test is carried out by Monte Carlo experiment. An empirical example with data on the net immigration to Sweden during the period 1950-2000 is used to illustrate the performance of the wavelet improved test under GARCH errors. The results reveal that using the traditional Dickey-Fuller type of tests, the unit root hypothesis is rejected while our wavelet improved test do not reject as it is more robust to GARCH errors in finite samples.

  • 34.
    Lindh, Jörgen
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Business Informatics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Hussain, S
    The effect of the Lego Training on Pupils' School Performance in Mathematics, Problem Solving Ability and Attitude: Swedish Data2006In: Journal of Educational Technology & Society, ISSN 1176-3647, E-ISSN 1436-4522, Vol. 9, no 3, p. 182-194Article in journal (Refereed)
  • 35.
    Locking, Håkan
    et al.
    Department of Economics and Statistics, Centre for Labour Market and Discrimination Studies, Linnaeus University, Växjö, Sweden.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance 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 article 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 is judged by calculating the mean squared error (MSE) using Monte 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.

  • 36.
    Locking, Håkan
    et al.
    Department of Economics and Statistics, Linnaeus University, Sweden.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Ridge estimators for probit regression: With an application to labour market data2015In: Bulletin of Economic Research, ISSN 0307-3378, E-ISSN 1467-8586, Vol. 66, no S1, p. S92-S103Article in journal (Refereed)
    Abstract [en]

    In this paper we propose ridge regression estimators for probit models since the commonly applied maximum likelihood (ML) method is sensitive to multicollinearity. An extensive Monte Carlo study is conducted where the performance of the ML method and the probit ridge regression (PRR) is investigated when the data is collinear. In the simulation study we evaluate a number of methods of estimating the ridge parameter k that have recently been developed for use in linear regression analysis. The results from the simulation study show that there is at least one group of the estimators of k that regularly has a lower MSE than the ML method for all different situations that has been evaluated. Finally, we show the benefit of the new method using the classical Dehejia and Wahba (1999) dataset which is based on a labor market experiment.

     

  • 37.
    Mantalos, Panagiotis
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    The effect of spillover on the Johansens tests for Cointegration: A Monte Carlo Analysis2010In: International Journal of Computational Economics and Econometrics, ISSN 1757-1189 (Online); 1757-1170 (Print), Vol. 1, no 3/4, p. 327-342Article in journal (Refereed)
    Abstract [en]

    This paper investigates the effect of spillover (i.e. causality in variance) on the Johansens tests for cointegration by conducting a Monte Carlo experiment where 16 different data generating processes (DGP) are used and a number of factors that might affect the properties of the Johansens cointegration tests are varied. The result from the simulation study clearly shows that spillover effect leads to an over-rejection of the true null hypothesis. Hence, in the presence of spillover it becomes very hard to make inferential statements since it will often lead to erroneous claims that cointegration relationships exist.

  • 38. Mantalos, Panagiotis
    et al.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Bootstrap Methods for Autocorrelation Test with Uncorrelated but not Independent Errors2008In: Economic Modelling, ISSN 0264-9993, E-ISSN 1873-6122, Vol. 25, no 5, p. 1040-1050Article in journal (Refereed)
  • 39. Mantalos, Panagiotis
    et al.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    The Effect of the GARCH(1,1) on Autocorrelation Tests in Dynamic Systems of Equations2005In: Applied Economics, ISSN 0003-6846, E-ISSN 1466-4283, Vol. 37, no 16, p. 1907-1913Article in journal (Refereed)
  • 40. Mantalos, Panagiotis
    et al.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    The Robustness of the RESET Test to Non-normal Error Terms2007In: Computational Economics, ISSN 0927-7099, E-ISSN 1572-9974, Vol. 30, no 4, p. 393-408Article in journal (Refereed)
  • 41. Mantalos, Panagiotis
    et al.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics. Statistik.
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    An Examination of the Robustness of the Vector Autoregressive Granger-Causality Test in the Presence of GARCH and Variance Shifts2007In: International Review of Business Research Papers, ISSN 1832-9543, Vol. 3, no 5, p. 280-296Article in journal (Refereed)
  • 42.
    Muniz, Gisela
    et al.
    Department of Mathematics and Statistics, Florida International University, Miami, Florida, USA.
    Kibria, B. M. Golam
    Department of Mathematics and Statistics, Florida International University, Miami, Florida, USA.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    On developing ridge regression parameters: A graphical investigation2012In: SORT - Statistics and Operations Research Transactions, ISSN 1696-2281, E-ISSN 2013-8830, Vol. 36, no 2, p. 115-138Article in journal (Refereed)
    Abstract [en]

    In this paper we review some existing and propose some new estimators for estimating the ridge parameter. All in all 19 different estimators have been studied. The investigation has been carried out using Monte Carlo simulations. A large number of different models have been investigated where the variance of the random error, the number of variables included in the model, the correlations among the explanatory variables, the sample size and the unknown coefficient vector were varied. For each model we have performed 2000 replications and presented the results both in term of figures and tables. Based on the simulation study, we found that increasing the number of correlated variable, the variance of the random error and increasing the correlation between the independent variables have negative effect on the mean squared error. When the sample size increases the mean squared error decreases even when the correlation between the independent variables and the variance of the random error are large. In all situations, the proposed estimators have smaller mean squared error than the ordinary least squares and other existing estimators.

  • 43.
    Månsson, Kristofer
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Kibria, B. M. Golam
    Florida International University.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    A restricted Liu estimator for binary regression models and its application to an applied demand system2016In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 43, no 6, p. 1119-1127Article in journal (Refereed)
    Abstract [en]

    In this article, we propose a restricted Liu regression estimator (RLRE) for estimating the parameter vector, β, in the presence of multicollinearity, when the dependent variable is binary and it is suspected that β may belong to a linear subspace defined by =r. First, we investigate the mean squared error (MSE) properties of the new estimator and compare them with those of the restricted maximum likelihood estimator (RMLE). Then we suggest some estimators of the shrinkage parameter, and a simulation study is conducted to compare the performance of the different estimators. Finally, we show the benefit of using RLRE instead of RMLE when estimating how changes in price affect consumer demand for a specific product.

  • 44.
    Månsson, Kristofer
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Kibria, B M Golam
    Florida International University, USA.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Improved Ridge Regression Estimators for Binary Choice Models: An Empirical Study2014In: International Journal of Statistics in Medical Research, ISSN 1929-6029, Vol. 3, no 3, p. 257-265Article in journal (Refereed)
    Abstract [en]

    This paper suggests some new estimators of the ridge parameter for binary choice models that may be applied in the presence of a multicollinearity problem. These new ridge parameters are functions of other estimators of the ridge parameter that have shown to work well in the previous research. Using a simulation study we investigate the mean square error (MSE) properties of these new ridge parameters and compare them with the best performing estimators from the previous research. The results indicate that we may improve the MSE properties of the ridge regression estimator by applying the proposed estimators in this paper, especially when there is a high multicollinearity between the explanatory variables and when many explanatory variables are included in the regression model. The benefit of this paper is then shown by a health related data where the effect of some risk factors on the probability of receiving diabetes is investigated.

  • 45.
    Månsson, Kristofer
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Kibria, B. M. Golam
    Department of Mathematics and Statistics, Florida International University, Miami, FL, USA.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    On Liu Estimators for the Logit Regression Model2012In: Economic Modelling, ISSN 0264-9993, E-ISSN 1873-6122, Vol. 29, no 4, p. 1483-1488Article in journal (Refereed)
    Abstract [en]

    This paper introduces a shrinkage estimator for the logit model which is a generalization of the estimator proposed by Liu (1993) for the linear regression. This new estimation method is suggested since the mean squared error (MSE) of the commonly used maximum likelihood (ML) method becomes inflated when the explanatory variables of the regression model are highly correlated. Using MSE, the optimal value of the shrinkage parameter is derived and some methods of estimating it are proposed. It is shown by means of Monte Carlo simulations that the estimated MSE and mean absolute error (MAE) are lower for the proposed Liu estimator than those of the ML in the presence of multicollinearity. Finally the benefit of the Lie estimator is shown in an empirical application where different economic factors are used to explain the probability that municipalities have net increase of inhabitants.

  • 46.
    Månsson, Kristofer
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Kibria, B. M. Golam
    Department of Mathematics and Statistics, Florida International University, Miami, FL, USA.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Performance of some weighted Liu estimators for logit regression model: An application to Swedish accident data2015In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 44, no 2, p. 363-375Article in journal (Refereed)
    Abstract [en]

    In this article, we propose some new estimators for the shrinkage parameter d of the weighted Liu estimator along with the traditional maximum likelihood (ML) estimator for the logit regression model. A simulation study has been conducted to compare the performance of the proposed estimators. The mean squared error is considered as a performance criteria. The average value and standard deviation of the shrinkage parameter d are investigated. In an application, we analyze the effect of usage of cars, motorcycles, and trucks on the probability that pedestrians are getting killed in different counties in Sweden. In the example, the benefits of using the weighted Liu estimator are shown. Both results from the simulation study and the empirical application show that all proposed shrinkage estimators outperform the ML estimator. The proposed D9 estimator performed best and it is recommended for practitioners.

  • 47.
    Månsson, Kristofer
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Kibria, B. M. Golam
    Department of Mathematics and Statistics, Florida International University Miami, Florida, USA.
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Improved Liu Estimators for the Poisson Regression Model2012In: International Journal of Statistics and Probability, ISSN 1927-7032, Vol. 1, no 1Article in journal (Refereed)
    Abstract [en]

    A new shrinkage estimator for the Poisson model is introduced in this paper. This method is a generalization of the Liu (1993) estimator originally developed for the linear regression model and will be generalized here to be used instead of the classical maximum likelihood (ML) method in the presence of multicollinearity since the mean squared error (MSE) of ML becomes inflated in that situation. Furthermore, this paper derives the optimal value of the shrinkage parameter and based on this value some methods of how the shrinkage parameter should be estimated are suggested. Using Monte Carlo simulation where the MSE and mean absolute error (MAE) are calculated it is shown that when the Liu estimator is applied with these proposed estimators of the shrinkage parameter it always outperforms the ML.

  • 48.
    Månsson, Kristofer
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, 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.

  • 49.
    Månsson, Kristofer
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics. Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics. Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    On Ridge Parameters in Logistic Regression2011In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 40, no 18, p. 3366-3381Article in journal (Refereed)
    Abstract [en]

    This article applies and investigates a number of logistic ridge regression (RR) parameters that are estimable by using the maximum likelihood (ML) method. By conducting an extensive Monte Carlo study, the performances of ML and logistic RR are investigated in the presence of multicollinearity and under different conditions. The simulation study evaluates a number of methods of estimating the RR parameter k that has recently been developed for use in linear regression analysis. The results from the simulation study show that there is at least one RR estimator that has a lower mean squared error (MSE) than the ML method for all the different evaluated situations.

  • 50.
    Månsson, Kristofer
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Kibria, B. M. Golam
    Department of Mathematics and Statistics, Florida International University, Miami, Florida, USA.
    Performance of Some Ridge Regression Estimators for the Multinomial Logit Model2014In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415XArticle in journal (Refereed)
    Abstract [en]

    This paper considers several estimators for estimating the ridge parameter  for multinomial logit model based on the work of Khalaf and Shukur (2005), Alkhamisi, Khalaf and Shukur (2006) and  Muniz, Kibria and Shukur (2012). The mean square error (MSE) is 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 and the number of regressors has negative effect on the MSE. However, when the sample size increases the MSE decreases even when the correlation between the independent variables is large. Based on the minimum MSE criterion some useful estimators for estimating the ridge parameter k are recommended for the practitioners.

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