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  • 1.
    Alkhamisi, Mahdi A.
    et al.
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Nationalekonomi och Statistik.
    Shukur, Ghazi
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Nationalekonomi och Statistik.
    Developing Ridge Parameters for SUR Model2008In: Communication in Statistics, Theory and Methods, Vol. 37, no 4, p. 544-564Article in journal (Other (popular science, discussion, etc.))
  • 2.
    Alkhamisi, Mahdi
    et al.
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Nationalekonomi och Statistik.
    Khalaf, Gadban
    Shukur, Ghazi
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Nationalekonomi och Statistik.
    Some modifications for choosing ridge parameter2006In: Communications in Statistics, theory and Methods, Vol. 35, no 11, p. 2005-2021Article in journal (Refereed)
  • 3.
    Alkhamisi, Mahdi
    et al.
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Nationalekonomi och Statistik.
    Shukur, Ghazi
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Nationalekonomi och Statistik.
    A Monte Carlo study of recent ridge parameters2007In: Communications in Statistics, Simulation and Computation, Vol. 36, no 3Article in journal (Other (popular science, discussion, etc.))
  • 4. Almasri, Abdullah
    et al.
    Locking, Håkan
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Nationalekonomi och Statistik.
    Shukur, Ghazi
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Nationalekonomi och Statistik.
    Testing for Climate Change in Sweden During 1850-1999, using Wavelet analysis2008In: Journal of Applied Statistics, Vol. 35, no 4Article in journal (Other (popular science, discussion, etc.))
  • 5.
    Almasri, Abdullah
    et al.
    Karlstad University.
    Locking, Håkan
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
    Shukur, Ghazi
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics. Jönköping University.
    Testing for trends and causality in Swedish environmental data, using Wavelet analysis2013Conference paper (Refereed)
    Abstract [en]

    This paper utilizes Wavelet based methodology to estimate and test for trends and granger causality in temperature andprecipitation. We use quarterly data from Sweden for the period 1884 up to 2011. The analysis suggests that temperatureand precipitation in Sweden currently have a positive trend in 2011. Thus the recent lower levels of the variables 2009-2010are estimated to be temporary fluctuations or deviations from the trend. Moreover, in the short run there are feedbackeffects between the variables and over longer periods, 4-8 years, temperature granger cause precipitation.

  • 6.
    Almasri, Abdullah
    et al.
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics.
    Locking, Håkan
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics.
    Shukur, Ghazi
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics.
    Wavelet Based Forecasting Approach, with Application2009In: 2009 International Conference on Financial Theory and Engineering / [ed] Patrick Kellenberger, 2009Conference paper (Refereed)
    Abstract [en]

    In this paper we outline a framework for forecasting using maximal overlap discrete wavelet transform (MODWT) based multiresoulution analysis (MRA). This framework has been applied for forecasting the tourism arrival series from Denmark to Norway. We compare forecasted values obtained from modeling the data in the time domain with the forecasted values from the wavelet domain using the traditional Box-Jenkins methodology. In both cases, diagnostic tests have been conducted to insure the specification of the model. The results have shown that the wavelet based forecasts outperforms the traditional Box-Jenkins approach in term of forecasts accuracy.

  • 7.
    Almasri, Abdullah
    et al.
    Karlstad University.
    Månsson, Kristofer
    Jönköping University.
    Sjölander, Pär
    Jönköping University.
    Shukur, Ghazi
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics. Jönköping University.
    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) testand 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 interms 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.
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Nationalekonomi och Statistik.
    Shukur, Ghazi
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Nationalekonomi och Statistik.
    Clustering using Wavelet Transformation2008In: Handbook of Research on Clusters:: Theories, Policies and Case Studies, Edward Elgar , 2008Chapter in book (Other (popular science, discussion, etc.))
  • 9.
    Andersson, Lina (current name Aldén, Lina)
    et al.
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    Hammarstedt, Mats
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    Hussain, Shakir
    University of Birmingham, UK.
    Shukur, Ghazi
    Jönköping University.
    Ethnic origin, local labour markets and self-employment in Sweden: A multlilevel approach2012Report (Other academic)
    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 i.e. 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.

  • 10.
    Andersson, Lina (current name Aldén, Lina)
    et al.
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
    Hammarstedt, Mats
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
    Hussain, Shakir
    University of Birmingham.
    Shukur, Ghazi
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
    Etnic 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.
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    Hussain, Shakir
    Shukur, Ghazi
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    The Demand of Part-time in  European Companies: A Multilevel Modeling 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.
    Ekberg, Jan
    et al.
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    Hammarstedt, Mats
    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.
    SUR estimation of earnings differentials between three generations of immigrants and natives2010In: The annals of regional science, ISSN 0570-1864, E-ISSN 1432-0592, Vol. 45, no 3, p. 705-720Article in journal (Refereed)
    Abstract [en]

    This paper presents a Seemingly Unrelated Regressions estimation of earnings differentials between three generations of immigrants and natives in Sweden. The results show that male first-generation immigrants were at an earnings advantage compared to male natives. Among male second-generation immigrants the earnings differentials compared to natives were very small, while third-generation immigrants were at an earnings disadvantage compared to natives. The same pattern was found among females. Thus, the results indicate that ethnic differences in earnings are likely to occur even after several generations spent in a country and that the problem of immigrant assimilation that exists in many European countries may last for several generations.

  • 13.
    Hammarstedt, Mats
    et al.
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Nationalekonomi och Statistik.
    Shukur, Ghazi
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Nationalekonomi och Statistik.
    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)
  • 14.
    Hammarstedt, Mats
    et al.
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Nationalekonomi och Statistik.
    Shukur, Ghazi
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Nationalekonomi och Statistik.
    Immigrants' relative earnings in Sweden - a quantile regression approach2007In: International Journal of Manpower, Vol. 28, p. 456-473Article in journal (Refereed)
  • 15.
    Hammarstedt, Mats
    et al.
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics.
    Shukur, Ghazi
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and 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.

  • 16.
    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.

  • 17.
    Hussain, Shakir
    et al.
    University of Birmingham.
    Al-Alak, Mehdi
    Central Organization for Statistics, Baghdad.
    Shukur, Ghazi
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
    Conditional two level mixture with known mixing proportions: applications to school and student level overweight and obesity data from Birmingham, England2014In: International Journal of Statistics in Medical Research, ISSN 1929-6029, Vol. 3, no 3, p. 298-308Article in journal (Refereed)
    Abstract [en]

    Two Level (TL) models allow the total variation in the outcome to be decomposed as level one and level two or ‘individual and group’ variance components. Two Level Mixture (TLM) models can be used to explore unobserved heterogeneity that represents different qualitative relationships in the outcome.

    In this paper, we extend the standard TL model by introducing constraints to guide the TLM algorithm towards a more appropriate data partitioning. Our constraints-based methods combine the mixing proportions estimated by parametric Expectation Maximization (EM) of the outcome and the random component from the TL model. This forms new two level mixing conditional (TLMc) approach by means of prior information. The new framework advantages are: 1. avoiding trial and error tactic used by TLM for choosing the best BIC (Bayesian Information Criterion), 2. permitting meaningful parameter estimates for distinct classes in the coefficient space and finally 3. allowing smaller residual variances. We show the benefit of our method using overweight and obesity from Body Mass Index (BMI) for students in year 6. We apply these methods on hierarchical BMI data to estimate student multiple deprivation and school Club effects.

  • 18. Hussain, Shakir
    et al.
    Lindh, Jörgen
    Shukur, Ghazi
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Nationalekonomi och Statistik.
    The effect of LEGO training on pupils' school performance in mathematics, problem solving ability and attitude: Swedish data2006In: Educational Technology and Society Journal, Vol. 7, p. 182-194Article in journal (Refereed)
  • 19. 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.

  • 20.
    Hussain, Shakir
    et al.
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
    Mohammed, Mohamed A
    University of Birmingham.
    Shukur, Ghazi
    Jönköping International Business School.
    Congenial Multiple Imputation and Matched Pairs Models for Square Tables: An Example of patients’ self-management2013In: Journal of Business Administration Research, ISSN 1927-9507, Vol. 2, no 1, p. Article ID: 1-Article 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.
    Karlsson, Peter S.
    et al.
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
    Behrenz, Lars
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
    Shukur, Ghazi
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics. Jönköping University.
    Performances of model selection criteria when variables are ill conditioned2018In: Computational Economics, ISSN 0927-7099, E-ISSN 1572-9974, p. 1-22Article in journal (Refereed)
    Abstract [en]

    Model selection criteria are often used to find a "proper" model for the data under investigation when building models in cases in which the dependent or explained variables are assumed to be functions of several independent or explanatory variables. For this purpose, researchers have suggested using a large number of such criteria. These criteria have been shown to act differently, under the same or different conditions, when trying to select the "correct" number of explanatory variables to be included in a given model; this, unfortunately, leads to severe problems and confusion for researchers. In this paper, using Monte Carlo methods, we investigate the properties of four of the most common criteria under a number of realistic situations. These criteria are the adjusted coefficient of determination (R2-adj), Akaike's information criterion (AIC), the Hannan–Quinn information criterion (HQC) and the Bayesian information criterion (BIC). The results from this investigation indicate that the HQC outperforms the BIC, the AIC and the R2-adj under specific circumstances. None of them perform satisfactorily, however, when the degree of multicollinearity is high, the sample sizes are small or when the fit of the model is poor (i.e., there is a low R2) . In the presence of all these factors, the criteria perform very badly and are not very useful. In these cases, the criteria are often not able to select the true model.

  • 22.
    Khalaf, G
    et al.
    Department of Mathematics , King Khalid University , Saudi Arabia.
    Månsson, Kristofer
    Department of Economics , Finance and Statistics, Jönköping International Business School, Jönköping University.
    Sjölander, Pär
    Department of Economics , Finance and Statistics, Jönköping International Business School, Jönköping University.
    Shukur, Ghazi
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics. Department of Economics , Finance and Statistics, Jönköping International Business School, Jönköping University.
    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.

  • 23. Khalaf, Ghadban
    et al.
    Månsson, Kristofer
    Shukur, Ghazi
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics. Jönköping University.
    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.

  • 24.
    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.

  • 25. Kibria, B. M. Golam
    et al.
    Månsson, Kristofer
    Shukur, Ghazi
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    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).

  • 26. Kibria, B. M. Golam
    et al.
    Månsson, Kristofer
    Shukur, Ghazi
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
    Some Ridge Regression estimator 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 very common when analysing economic data that comes 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 estimators and some methods of estimating the ridge parameter k for the non-negative model. A simulation study has been conducted to compare the performance of the estimators. Both mean squared error (MSE) and mean absolute error (MAE) are considered as performance criterion. The simulation study shows that some estimators are better than the commonly used maximum likelihood estimator and some other ridge regression estimators. Based on the simulation study and an empirical application, some useful estimators are recommended for the practitioners.

  • 27. Kibria, B. M. Golam
    et al.
    Månsson, Kristofer
    Sjölander, Pär
    Shukur, Ghazi
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    Improved Liu Estimators for the Poisson Regression Model2012In: International Journal of Statistics and Probability, ISSN 1927-7032, Vol. 1, no 1, p. 2-6Article in journal (Refereed)
  • 28.
    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.
    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)
  • 29.
    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.

  • 30.
    Li, Yushu
    et al.
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
    Shukur, Ghazi
    Linnaeus University, School of Business and Economics, Department of Economics 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 of the tests concentrate in the time domain. Recently, Fan and Gracay (2010) proposed a wavelet ratio test which took advantage of the information from frequency domain by using wavelet spectrum decompose methodology. This test shows a better power over many time domain based unit root test including the Dickey-Fuller (1979) type of test in the univariate time series case. On the other hand, various unit root tests in multivariate time series appear since the pioneering work of Levin and Lin (1993). Among them, the Im-Pesaran-Shin (IPS) (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 series can be flexible, this paper will apply the wavelet ratio statistic to give a comparison with the test by using Dickey-Fuller  statistic in the single series. Simulation result shows a gain in power by employing the wavelet ratio test instead of the Dickey-Fuller  statistic in the panel data case. As the IPS test is sensitive to the cross sectional dependence, we further compare the robustness of both test statistics to the cross sectional. 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. 

  • 31.
    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.
    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)
  • 32.
    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.

  • 33.
    Locking, Håkan
    et al.
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
    Månsson, Kristofer
    Jönköping University.
    Shukur, Ghazi
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics. Jönköping University.
    Ridge estimators for probit regression: with an application to labour market data2014In: 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.

     

  • 34.
    Mantalos, Panagiotis
    et al.
    Jönköping University.
    Månsson, Kristofer
    Jönköping University.
    Shukur, Ghazi
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics. Jönköping University.
    The effect of spillover on the Johansens tests for Cointegration: A Monte Carlo Analysis2010In: International Journal of Computational Economics and Econometrics, ISSN 1757-1170, E-ISSN 1757-1189, 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.

  • 35.
    Mantalos, Panagiotis
    et al.
    Lund University.
    Shukur, Ghazi
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Jönköping University.
    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)
    Abstract [en]

    By using bootstrap technique we investigate the properties of the Breusch [Breusch, T.S., 1978. Testing for autocorrelation in dynamic linear models. Australian Economic Papers 17, 334–355]–Godfrey [Godfrey, L.G., 1978. Testing for higher order serial correlation in regression equations when the regressors include lagged dependent variables. Econometrica 46, 1303–1310] autocorrelation tests in dynamic models with uncorrelated but not independent errors. In this paper we show that, under conditions when the errors are uncorrelated but not independent, even the best likelihood ratio test cannot achieve the asymptotic distribution under the null hypothesis of no autocorrelation. Standard bootstrap methods also fail to produce consistent results. To overcome this problem we applied several bootstrap testing methods for the same purpose and found the stationary bootstrap and Wild bootstrap with static model to perform adequately among the other bootstrap methods.

  • 36.
    Mantalos, Panagiotis
    et al.
    Lund University.
    Shukur, Ghazi
    Göteborg University.
    Bootstrapped Johansen Tests for Cointegrating Relationships: A Graphical analysis2001In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 68, no 4, p. 351-371Article in journal (Refereed)
    Abstract [en]

    Using Monte Carlo methods together with the bootstrap critical values, we have studied the properties of two tests (Trace and Lmax), derived by Johansen (1988) for testing for cointegration in VAR 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 presence of cointegration vector(s). Finally, when considering the size and power properties, we could not find any noticeable differences between the two test methods.

  • 37.
    Mantalos, Panagiotis
    et al.
    Lund University.
    Shukur, Ghazi
    Lund University.
    Size and Power of the Error Correction Model (ECM) of Cointegration Tests. A Bootstrap Approach1998In: Oxford Bulletin of Economics and Statistics, ISSN 0305-9049, E-ISSN 1468-0084, Vol. 60, no 2, p. 249-255Article in journal (Refereed)
    Abstract [en]

    The size and power of the ECM cointegration test are investigated by using the ‘bootstrap critical values’. The purpose of this paper is to show the ability of the bootstrap technique to produce critical values which are much more accurate than the asymptotic ones. The properties of the test have been studied, using Monte Carlo methods, for three different data generating processes. As regards the size of the test, we find that the ECM cointegration test together with the bootstrap critical values perform better than the ECM cointegration test based on the asymptotic critical values. While as regards the power of the tests, the results prove to be similar for the different versions.

  • 38.
    Mantalos, Panagiotis
    et al.
    Lund University .
    Shukur, Ghazi
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Jönköping University.
    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. 35, no 16, p. 1907-1913Article in journal (Refereed)
    Abstract [en]

    Using Monte Carlo methods, the properties of systemwise generalizations of the Breusch–Godfrey test for autocorrelated errors are studied when there are some kinds of GARCH effects among the errors. The analysis, regarding the size of the test, reveals that the GARCH have considerable effects of the properties of the test regarding the size, especially in large systems of equations. The corrected LR tests, however, have been shown to perform satisfactorily in small systems when the errors are white noise or they have low GARCH effects, whilst the commonly used TR2 test behaves badly even in single equations. All tests perform badly, however, when the number of equations increases and the GARCH effect is strong. As regards the power of the test, the GARCH was not found to have any significant effects on the power properties of the test.

  • 39.
    Mantalos, Panagiotis
    et al.
    Lund University.
    Shukur, Ghazi
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    The Effect of the Spillover on the Granger Causality Test 2010In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 37, no 9, p. 1473-1486Article in journal (Refereed)
    Abstract [en]

    In this paper, we investigate the properties of the Granger causality test in stationary and stable vector autoregressive models under the presence of spillover effects, i.e., causality in variance. The Wald test and the WW test (the Wald test with White’s proposed heteroskedasticity-consistent covariance matrix estimator imposed) are analyzed. The investigation is undertaken by using Monte Carlo simulation in which two different sample sizes and six different kinds of data generating process are used. The results show that the Wald test overrejects the null hypothesis both with and without the spillover effect, and that the overrejection in the latter case is more severe in larger samples. The size properties of the WW test are satisfactory when there is spillover between the variables. Only when there is feedback in the variance is the size of the WW test slightly affected. The Wald test is shown to have higher power than the WW test when the errors follow a GARCH(1,1) process without a spillover effect. When there is spillover, the power of both tests deteriorates, which implies that the spillover has a negative effect on the causality tests.

  • 40.
    Mantalos, Panagiotis
    et al.
    Lund University.
    Shukur, Ghazi
    Växjö University, Faculty of Humanities and Social Sciences, School of Management and Economics. Nationalekonomi och Statistik.
    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.
    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.

  • 42.
    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, Pär
    Jönköping University.
    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 1837-5685, Vol. 3, no 6, p. 280-296Article in journal (Refereed)
    Abstract [en]

    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.

  • 43.
    Muniz, Gisela
    et al.
    Florida International University, USA.
    Kibria, B. M. Golam
    Florida International University, USA.
    Månsson, Kristofer
    Jönköping University.
    Shukur, Ghazi
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics. Jönköping University.
    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.

  • 44.
    Månsson, Kristofer
    et al.
    Jönköping University.
    Kibria, B. M. Golam
    Florida International University, USA.
    Shukur, Ghazi
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics. Jönköping University.
    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.
    Kibria, B. M. Golam
    Shukur, Ghazi
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.
    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.
    Kibria, B. M. Golam
    Florida Int Univ, USA.
    Shukur, Ghazi
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics. Jönköping University.
    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.
    Kibria, B. M. Golam
    Florida International University, USA.
    Shukur, Ghazi
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics. Jönköping University.
    Sjölander, Pär
    Jönköping University.
    On the Estimation of the CO2 Emission, Economic Growth and Energy Consumption Nexus Using Dynamic OLS in the Presence of Multicollinearity2018In: Sustainability, ISSN 2071-1050, E-ISSN 2071-1050, Vol. 10, no 5, article id 1315Article in journal (Refereed)
    Abstract [en]

    This paper introduces shrinkage estimators (Ridge DOLS) for the dynamic ordinary least squares (DOLS) cointegration estimator, which extends the model for use in the presence of multicollinearity between the explanatory variables in the cointegration vector. Both analytically and by using simulation techniques, we conclude that our new Ridge DOLS approach exhibits lower mean square errors (MSE) than the traditional DOLS method. Therefore, based on the MSE performance criteria, our Monte Carlo simulations demonstrate that our new method outperforms the DOLS under empirically relevant magnitudes of multicollinearity. Moreover, we show the advantages of this new method by more accurately estimating the environmental Kuznets curve (EKC), where the income and squared income are related to carbon dioxide emissions. Furthermore, we also illustrate the practical use of the method when augmenting the EKC curve with energy consumption. In summary, regardless of whether we use analytical, simulation-based, or empirical approaches, we can consistently conclude that it is possible to estimate these types of relationships in a considerably more accurate manner using our newly suggested method.

  • 48.
    Månsson, Kristofer
    et al.
    Jönköping University.
    Kibria, B.M. Golam
    Florida International University, USA.
    Shukur, Ghazi
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics. Jönköping University.
    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.

  • 49.
    Månsson, Kristofer
    et al.
    Jönköping university.
    Kibria, B.M. Golam
    Jönköping university.
    Shukur, Ghazi
    Linnaeus University, School of Business and Economics, Department of Economics and Statistics. Jönköping university.
    Some Liu Type Estimators for the dynamic OLS estimator: With an application to the carbon dioxide Kuznets curve for Turkey2017In: Communications in Statistics: Case studies, Data Analysis and Applications, E-ISSN 2373-7484, Vol. 3, no 3-4, p. 55-61Article in journal (Refereed)
    Abstract [en]

    This paper suggests some Liu type shrinkage estimators for the dynamic ordinary least squares (DOLS) estimator that may be used to combat the multicollinearity problem. DOLS is an estimator suggested to solve the finite sample bias of OLS caused by endogeneity issue when estimating regression models based on cointegrated variables. In this paper using simulation techniques it is shown that multicollinearity and non-normality of the error term is a problem in finite samples for the DOLS model. The merit of proposed Liu type estimator are shown by means of a Monte Carlo simulation study and using an empirical application.

  • 50.
    Månsson, Kristofer
    et al.
    Jönköping University.
    Shukur, Ghazi
    Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics. Jönköping University.
    A Poisson ridge regression Estimator2011In: Economic Modelling, ISSN 0264-9993, E-ISSN 1873-6122, Vol. 28, no 4, p. 1475-1481Article in journal (Refereed)
    Abstract [en]

    The standard statistical method for analyzing count data is the Poisson regression model, which is usually estimated using maximum likelihood (ML) method. The ML method is very sensitive to multicollinearity. Therefore, we present a new Poisson ridge regression estimator (PRR) as a remedy to the problem of instability of the traditional ML method. To investigate the performance of the PRR and the traditional ML approaches for estimating the parameters of the Poisson regression model, we calculate the mean squared error (MSE) using Monte Carlo simulations. The result from the simulation study shows that the PRR method outperforms the traditional ML estimator in all of the different situations evaluated in this paper.

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