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  • 1.
    Akram, Muhammad N.
    et al.
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Amin, Muhammad
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    A new biased estimator for the gamma regression model: Some applications in medical sciences2023In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 52, no 11, p. 3612-3632Article in journal (Refereed)
    Abstract [en]

    The Gamma Regression Model (GRM) has a variety of applications in medical sciences and other disciplines. The results of the GRM may be misleading in the presence of multicollinearity. In this article, a new biased estimator called James-Stein estimator is proposed to reduce the impact of correlated regressors for the GRM. The mean squared error (MSE) properties of the proposed estimator are derived and compared with the existing estimators. We conducted a simulation study and employed the MSE and bias evaluation criterion to judge the proposed estimator’s performance. Finally, two medical dataset are considered to show the benefit of the proposed estimator over existing estimators.

  • 2.
    Akram, Muhammad Nauman
    et al.
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Amin, Muhammad
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    A new Liu-type estimator for the Inverse Gaussian Regression Model2020In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 90, no 7, p. 1153-1172Article in journal (Refereed)
    Abstract [en]

    The Inverse Gaussian Regression Model (IGRM) is used when the response variable is positively skewed and follows the inverse Gaussian distribution. In this article, we propose a Liu-type estimator to combat multicollinearity in the IGRM. The variance of the Maximum Likelihood Estimator (MLE) is overstated due to the presence of severe multicollinearity. Moreover, some estimation methods are suggested to estimate the optimal value of the shrinkage parameter. The performance of the proposed estimator is compared with the MLE and some other existing estimators in the sense of mean squared error through Monte Carlo simulation and different real-life applications. Under certain conditions, it is concluded that the proposed estimator is superior to the MLE, ridge, and Liu estimator.

  • 3.
    Alheety, M. I.
    et al.
    Department of Mathematics, College of Education for Pure Sciences, University of Anbar, Ramadi, Iraq.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Kibria, B. M. Golam
    Department of Mathematics and Statistics, Florida International University, Miami, FL, United States.
    A new kind of stochastic restricted biased estimator for logistic regression model2021In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 48, no 9, p. 1559-1578Article in journal (Refereed)
    Abstract [en]

    In the logistic regression model, the variance of the maximum likelihood estimator is inflated and unstable when the multicollinearity exists in the data. There are several methods available in literature to overcome this problem. We propose a new stochastic restricted biased estimator. We study the statistical properties of the proposed estimator and compare its performance with some existing estimators in the sense of scalar mean squared criterion. An example and a simulation study are provided to illustrate the performance of the proposed estimator. 

  • 4.
    Alheety, Mustafa I.
    et al.
    Univ Anbar, Dept Math, Coll Educ Pure Sci, Ramadi, Iraq..
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Kibria, B. M. Golam
    Florida Int Univ, Dept Math & Stat, Miami, FL 33199 USA..
    Modifed almost unbiased two-parameter estimator for the Poisson regression model with an application to accident data2021In: SORT - Statistics and Operations Research Transactions, ISSN 1696-2281, E-ISSN 2013-8830, Vol. 45, no 2, p. 121-142Article in journal (Refereed)
    Abstract [en]

    Due to the large amount of accidents negatively affecting the wellbeing of the survivors and their families, a substantial amount of research is conducted to determine the causes of road accidents. This type of data come in the form of non-negative integers and may be modelled using the Poisson regression model. Unfortunately, the commonly used maximum likelihood estimator is unstable when the explanatory variables of the Poisson regression model are highly correlated. Therefore, this paper proposes a new almost unbiased estimator which reduces the instability of the maximum likelihood estimator and at the same time produce smaller mean squared error. We study the sta-tistical properties of the proposed estimator and a simulation study has been conducted to compare the performance of the estimators in the smaller mean squared error sense. Finally, Swedish traffic fatality data are analyzed to show the beneft of the proposed method.

  • 5.
    Ali, Abdul Aziz
    et al.
    Linnéuniversitetet, Institutionen för nationalekonomi och statistik (NS).
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Shukur, Ghazi
    Linnéuniversitetet, Institutionen för nationalekonomi och statistik (NS).
    A wavelet-based variance ratio unit root test for a system of equations2020In: Studies in Nonlinear Dynamics and Econometrics, ISSN 1081-1826, E-ISSN 1558-3708, Vol. 24, no 3, p. 1-16, article id 20180005Article in journal (Refereed)
    Abstract [en]

    In this paper, we suggest a unit root test for a system of equations using a spectral variance decomposition method based on the Maximal Overlap Discrete Wavelet Transform. We obtain the limiting distribution of the test statistic and study its small sample properties using Monte Carlo simulations. We find that, for multiple time series of small lengths, the wavelet-based method is robust to size distortions in the presence of cross-sectional dependence. The wavelet-based test is also more powerful than the Cross-sectionally Augmented Im et al. unit root test (Pesaran, M. H. 2007. "A Simple Panel Unit Root Test in the Presence of Cross-section Dependence." Journal of Applied Econometrics 22 (2): 265-312.) for time series with between 20 and 100 observations, using systems of 5 and 10 equations. We demonstrate the usefulness of the test through an application on evaluating the Purchasing Power Parity theory for the Group of 7 countries and find support for the theory, whereas the test by Pesaran (Pesaran, M. H. 2007. "A Simple Panel Unit Root Test in the Presence of Cross-section Dependence." Journal of Applied Econometrics 22 (2): 265-312.) finds no such support. 

  • 6.
    Alka,
    et al.
    Department of Mathematics and Statistics, Banasthali University, Jaipur, Rajasthan, India.
    Rai, Piyush Kant
    Department of Statistics, Banaras Hindu University, Varanasi, Uttar Pradesh, India.
    Qasim, Muhammad
    Department of Statistics & Computer Science, University of Veterinary & Animal Sciences, Lahore, Pakistan.
    Two-step calibration of design weights under two auxiliary variables in sample survey2019In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 89, no 12, p. 2316-2327Article in journal (Refereed)
    Abstract [en]

    Calibration on the available auxiliary variables is widely used to increase the precision of the estimates of parameters. Singh and Sedory [Two-step calibration of design weights in survey sampling. Commun Stat Theory Methods. 2016;45(12):3510–3523.] considered the problem of calibration of design weights under two-step for single auxiliary variable. For a given sample, design weights and calibrated weights are set proportional to each other, in the first step. While, in the second step, the value of proportionality constant is determined on the basis of objectives of individual investigator/user for, for example, to get minimum mean squared error or reduction of bias. In this paper, we have suggested to use two auxiliary variables for two-step calibration of the design weights and compared the results with single auxiliary variable for different sample sizes based on simulated and real-life data set. The simulated and real-life application results show that two-auxiliary variables based two-step calibration estimator outperforms the estimator under single auxiliary variable in terms of minimum mean squared error. 

  • 7.
    Alka, Singh
    et al.
    Banasthali Univ, Dept Math & Stat, Jaipur 304022, Rajasthan, India..
    Rai, Piyush Kant
    Banaras Hindu Univ, Dept Stat, Varanasi 221005, Uttar Pradesh, India..
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Two-Step Calibration Estimator with Double Use of Auxiliary Variable: Method and Application2022In: Journal of the Iranian Statistical Society, ISSN 1726-4057, Vol. 21, no 1, p. 37-54Article in journal (Refereed)
    Abstract [en]

    This article introduces a two-step calibration technique for the inverse relationship between study variable and auxiliary variable along with the double use of the auxiliary variable. In the first step, the calibration weights and design weights are set proportional to each other for a given sample. While in the second step, the constant of proportionality is to be obtained on the basis of some different objectives of the investigation viz. bias reduction or minimum Mean Squared Error (MSE) of the proposed estimator. Many estimators based on inverse relationship between x and y have been already developed and are considered to be special cases of the proposed estimator. Properties of the proposed estimator is discussed in details. Moreover, a simulation study has also been conducted to compare the performance of the proposed estimator under Simple Random Sampling Without Replacement (SRSWOR) and Lahiri-Midzuno (L-M) sampling design in terms of percent relative bias and MSE. The benefits of two-step calibration estimator are also demonstrated using real life data.

  • 8.
    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.

  • 9.
    Amin, Muhammad
    et al.
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Amanullah, Muhammad
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Aslam, Muhammad
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Qasim, Muhammad
    Department of Statistics and Computer Science, University of Veterinary and Animal Sciences, Lahore, Pakistan.
    Influence diagnostics in gamma ridge regression model2019In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 89, no 3, p. 536-556Article in journal (Refereed)
    Abstract [en]

    In this article, we proposed some influence diagnostics for the gamma regression model (GRM) and the gamma ridge regression model (GRRM). We assess the impact of influential observations on the GRM and GRRM estimates by extending the work of Pregibon [Logistic regression diagnostics. Ann Stat. 1981;9:705–724] and Walker and Birch [Influence measures in ridge regression. Technometrics. 1988;30:221–227]. Comparison of both models is made and demonstrated with the help of a simulation study and a real data set. We report some momentous results in detecting the influential observations and their effects on the GRM and GRRM estimates. 

  • 10.
    Amin, Muhammad
    et al.
    Department of Statistics, University of Sargodha, Sargodha, Punjab, Pakistan.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Afzal, Saima
    Department of Statistics, Bahauddin Zakariya University, Multan, Punjab, Pakistan.
    Naveed, Khalid
    Department of Statistics, Bahauddin Zakariya University, Multan, Punjab, Pakistan.
    New ridge estimators in the inverse Gaussian regression: Monte Carlo simulation and application to chemical data2022In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 51, no 10, p. 6170-6187Article in journal (Refereed)
    Abstract [en]

    In numerous application areas, when the response variable is continuous, positively skewed, and well fitted to the inverse Gaussian distribution, the inverse Gaussian regression model (IGRM) is an effective approach in such scenarios. The problem of multicollinearity is very common in several application areas like chemometrics, biology, finance, and so forth. The effects of multicollinearity can be reduced using the ridge estimator. This research proposes new ridge estimators to address the issue of multicollinearity in the IGRM. The performance of the new estimators is compared with the maximum likelihood estimator and some other existing estimators. The mean square error is used as a performance evaluation criterion. A Monte Carlo simulation study is conducted to assess the performance of the new ridge estimators based on the minimum mean square error criterion. The Monte Carlo simulation results show that the performance of the proposed estimators is better than the available methods. The comparison of proposed ridge estimators is also evaluated using two real chemometrics applications. The results of Monte Carlo simulation and real applications confirmed the superiority of the proposed ridge estimators to other competitor methods.

  • 11.
    Amin, Muhammad
    et al.
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Statistics. Department of Statistics and Computer Science, University of Veterinary and Animal Sciences, Lahore, Pakistan.
    Amanullah, Muhammad
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Performance of Asar and Genç and Huang and Yang’s Two-Parameter Estimation Methods for the Gamma Regression Model2019In: Iranian Journal of Science and Technology, Transactions A: Science, ISSN 1028-6276, Vol. 43, no 6, p. 2951-2963Article in journal (Refereed)
    Abstract [en]

    This study assesses the performance of two-parameter estimation methods to combat multicollinearity in the Gamma regression model. We derived optimal values for two-parameter estimation methods in the Gamma regression model. Furthermore, we proposed some estimation methods to estimate the shrinkage parameters and these methods improve the efficiency of the two-parameter estimator. We compare the performance of these estimators by means of Monte Carlo simulation study where the mean squared error (MSE) is considered as a performance criterion. Finally, consider a reaction rate data to evaluate the performance of the estimators. The simulation and numerical example results showed that the two-parameter biased estimators have smaller MSE than the maximum likelihood estimator under certain conditions.

  • 12.
    Amin, Muhammad
    et al.
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Qasim, Muhammad
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Amanullah, Muhammad
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Afzal, Saima
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Performance of some ridge estimators for the gamma regression model2020In: Statistical papers, ISSN 0932-5026, E-ISSN 1613-9798, Vol. 61, no 3, p. 997-1026Article in journal (Refereed)
    Abstract [en]

    In this study, we proposed some ridge estimators by considering the work of Månsson (Econ Model 29(2):178–184, 2012), Dorugade (J Assoc Arab Univ Basic Appl Sci 15:94–99, 2014) and some others for the gamma regression model (GRM). The GRM is a special form of the generalized linear model (GLM), where the response variable is positively skewed and well fitted to the gamma distribution. The commonly used method for estimation of the GRM coefficients is the maximum likelihood (ML) estimation method. The ML estimation method perform better, if the explanatory variables are uncorrelated. There are the situations, where the explanatory variables are correlated, then the ML estimation method is incapable to estimate the GRM coefficients. In this situation, some biased estimation methods are proposed and the most popular one is the ridge estimation method. The ridge estimators for the GRM are proposed and compared on the basis of mean squared error (MSE). Moreover, the outperforms of proposed ridge estimators are also calculated. The comparison has done using a Monte Carlo simulation study and two real data sets. Results show that Kibria’s and Månsson and Shukur’s methods are preferred over the ML method. 

  • 13.
    Amin, Muhammad
    et al.
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Yasin, Ahad
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Amanullah, Muhammad
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Almost unbiased ridge estimator in the gamma regression model2022In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 51, no 7, p. 3830-3850Article in journal (Refereed)
    Abstract [en]

    This article introduces the almost unbiased gamma ridge regression estimator (AUGRRE) estimator based on the gamma ridge regression estimator (GRRE). Furthermore, some shrinkage parameters are proposed for the AUGRRE. The performance of the AUGRRE by using different shrinkage parameters is compared with the existing GRRE and maximum likelihood estimator. A Monte Carlo simulation is carried out to assess the performance of the estimators where the bias and mean squared error performance criteria are used. We also used a real-life dataset to demonstrate the benefit of the proposed estimators. The simulation and real-life example results show the superiority of AUGRRE over the GRRE and the maximum likelihood estimator for the gamma regression model with collinear explanatory variables.

  • 14.
    Amin, Muhammad
    et al.
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Ullah, Muhammad Aman
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Diagnostic techniques for the inverse Gaussian regression model2022In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 51, no 8, p. 2552-2564Article in journal (Refereed)
    Abstract [en]

    In this article, we propose some diagnostic techniques for the inverse Gaussian regression model (IGRM), which are appropriate for modeling the response variable that undertakes positively skewed continuous dataset. Moreover, two new diagnostic methods are mainly proposed for the IGRM, which named as covariance ratio (CVR) and Welsch?s distance (WD). The comparison of our proposed methods of influence diagnostics with the existing approaches has been made through Monte Carlo simulation under different factors. In addition, the benefit of the proposed methods is assessed using a real application. Based on the simulation and empirical application results, we observed that the performance of the proposed method is better than the existing methods for detection of influential observations.

  • 15. Bjellerup, Mårten
    et al.
    Holgersson, Thomas
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    A simple multivariate test for asymmetry2009In: Applied Economics, ISSN 0003-6846, E-ISSN 1466-4283, Vol. 41, no 11, p. 1405-1416Article in journal (Refereed)
  • 16.
    Blennborn, Linus
    Jönköping University, School of Education and Communication.
    Känslan säger att det är lika stor sannolikhet: Lärarstudenters kunskaper och uppfattningar om det matematiska området sannolikhetslära2021Independent thesis Advanced level (professional degree), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Today´s modern society consists of different probability models. It can include everything from weather forecasts and insurance to predicting probability measures in games where chance is reminded. Teacher-students encounter concepts of probability and chance through their basic teacher education, which is part of mathematics teaching. The importance of gaining specific knowledge in probability theory becomes clear when TIMSS (Sollerman & Nydahl, 2020) latest report shows that statistics and probability is an area where younger students results drops for each four-year period that the survey is conducted. Because probability theory is the basis for statistical assumptions, the area is important, both in everyday life and in teaching when basic knowledge and perceptions are held. This in turn requires that teachers-students in their future professional role have knowledge of known misconceptions and intuitive assumptions.

    The study is based on Mathematical Knowledge for Teaching, which is a framework for teacher knowledge and aims at the importance of teachers mathematical and pedagogical knowledge. This study aims to examine teacher-students knowledge and perceptions of random events in the mathematical area of probability theory. This is done through a quantitative data collection method in the form of a digital survey of varying questions. 55 teacher-students responded to the questionnaire, which was analyzed quantitatively and qualitatively. The study divided the participants into two groups. First and last half of the education to study differences. The following questions form the basis for fulfilling the aim of the study:

    • What difficulties and misconceptions are noticed by teacher-students?
    • How do teachers-students reason about a specific area i probability theory?
    • How does a teacher education over time affect teacher-students ability to reason mathematically or intuitively?

    The result shows that relative frequency, representativeness and compound random events are problematic areas. Intuitive assumptions result in problematic reasoning and the teacher-students personal sense of probability is noticed. Furthermore, differences of teacher-students who attend the last half of the education show a better result.

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  • 17.
    Blennborn, Linus
    et al.
    Jönköping University, School of Education and Communication.
    Sjölander, Daniella
    Jönköping University, School of Education and Communication.
    Elevers förståelse av sannolikhetslära: En litteraturstudie om sannolikhet och slump utifrån ett elev- och undervisningsperspektiv2020Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesis
    Abstract [sv]

    Sannolikhet och slump påverkar oss människor i stor utsträckning och vi blir ofta påminda om dess innebörd i våra vardagliga liv. När elever möter sannolikhetslära i undervisning är det operationer med händelser det handlar om och inte operationer med tal. I denna litteraturstudie är syftet att undersöka hur sannolikhetslära behandlas i matematikdidaktisk forskning, såväl från ett elevperspektiv som från ett undervisningsperspektiv. Syftet besvarades med frågorna: Vad säger forskning om elevsvårigheter av sannolikhet och slump samt vilka faktorer är viktiga i undervisning för att utveckla elevers förståelse.

    Grunden till studien har skett genom litteratursökningar där forskning har jämförts och undersökts. Materialet som har analyserats i denna litteraturstudie hämtades från olika databaser. Svårigheter för elever kan vara begrepp och dess innebörd vilket elever i vissa fall kan ha svårt att koppla till en kontext. Det visar sig även att antalet slumpade försök har betydelse för hur elever tolkar sannolikhetens resultat och kan vara missvisande vid färre försök. Vidare är heuristisk representativitet en subjektiv missuppfattning som grundar sig i individens känsla för slump. Undervisning ska fördelaktigt utgå från elevers erfarenheter av vardagsnära händelser. Elever möter slump och sannolikhet i spel och lekar, därav är det av betydelse för verksamma lärare att använda material som eleverna är bekanta med. Genom experimentell undervisning kan elevernas förståelse utvecklas i sannolikhetslära.

  • 18.
    Boström, Henrik
    et al.
    Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden.
    Linusson, Henrik
    Department of Information Technology, University of Borås, Borås, Sweden.
    Löfström, Tuwe
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Department of Information Technology, University of Borås, Borås, Sweden.
    Johansson, Ulf
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Accelerating difficulty estimation for conformal regression forests2017In: Annals of Mathematics and Artificial Intelligence, ISSN 1012-2443, E-ISSN 1573-7470, Vol. 81, no 1-2, p. 125-144Article in journal (Refereed)
    Abstract [en]

    The conformal prediction framework allows for specifying the probability of making incorrect predictions by a user-provided confidence level. In addition to a learning algorithm, the framework requires a real-valued function, called nonconformity measure, to be specified. The nonconformity measure does not affect the error rate, but the resulting efficiency, i.e., the size of output prediction regions, may vary substantially. A recent large-scale empirical evaluation of conformal regression approaches showed that using random forests as the learning algorithm together with a nonconformity measure based on out-of-bag errors normalized using a nearest-neighbor-based difficulty estimate, resulted in state-of-the-art performance with respect to efficiency. However, the nearest-neighbor procedure incurs a significant computational cost. In this study, a more straightforward nonconformity measure is investigated, where the difficulty estimate employed for normalization is based on the variance of the predictions made by the trees in a forest. A large-scale empirical evaluation is presented, showing that both the nearest-neighbor-based and the variance-based measures significantly outperform a standard (non-normalized) nonconformity measure, while no significant difference in efficiency between the two normalized approaches is observed. The evaluation moreover shows that the computational cost of the variance-based measure is several orders of magnitude lower than when employing the nearest-neighbor-based nonconformity measure. The use of out-of-bag instances for calibration does, however, result in nonconformity scores that are distributed differently from those obtained from test instances, questioning the validity of the approach. An adjustment of the variance-based measure is presented, which is shown to be valid and also to have a significant positive effect on the efficiency. For conformal regression forests, the variance-based nonconformity measure is hence a computationally efficient and theoretically well-founded alternative to the nearest-neighbor procedure. 

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  • 19.
    Cao, Xiaofeng
    et al.
    Department of Agricultural Economics, Humboldt-Universität zu Berlin, Berlin, Germany.
    Okhrin, Ostap
    Chair of Statistics and Econometrics, Technische Universität Dresden, Dresden, Germany; SFB 649, School of Business and Economics, Humboldt-Universität zu Berlin, Berlin, Germany.
    Odening, Martin
    Department of Agricultural Economics, Humboldt-Universität zu Berlin, Berlin, Germany.
    Ritter, Matthias
    Department of Agricultural Economics, Humboldt-Universität zu Berlin, Berlin, Germany.
    Modelling spatio-temporal variability of temperature2015In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 30, no 3, p. 745-766Article in journal (Refereed)
    Abstract [en]

    Forecasting temperature in time and space is an important precondition for both, the design of weather derivatives and the assessment of the hedging effectiveness of index based weather insurance. In this article, we show how this task can be accomplished by means of Kriging techniques. Moreover, we compare Kriging with a dynamic semiparametric factor model (DSFM) that has been recently developed for the analysis of high dimensional financial data. We apply both methods to comprehensive temperature data covering a large area of China and assess their performance in terms of predicting a temperature index at an unobserved location. The results show that the DSFM performs worse than standard Kriging techniques. Moreover, we show how geographic basis risk inherent to weather derivatives can be mitigated by regional diversification.

  • 20.
    Duras, Toni
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    A comparison of two estimation methods for common principal components2019In: Communications in statistics. Case studies, data analysis and applications, E-ISSN 2373-7484, Vol. 5, no 4, p. 366-393Article in journal (Refereed)
    Abstract [en]

    Common principal components (CPCs) are often estimated using maximum likelihood estimation through an algorithm called the Flury–Gautschi (FG) Algorithm. Krzanowski proposed a simpler estimation method via a principal component analysis of a weighted sum of the sample covariance matrices. These methods are compared for real-world datasets and in a Monte Carlo simulation. The real-world data is used to compare the selection of a common eigenvector model and the estimated coefficients. The simulation study investigates how the accuracy of the methods is affected by autocorrelation, the number of covariance matrices, dimensions, and sample sizes for multivariate normal and chi-square distributed data. The findings in this article support the use of Krzanowski’s method in situations where the CPC assumption is appropriate. 

  • 21.
    Duras, Toni
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    An extension of the fixed effects principal component model to a common principal component environmentManuscript (preprint) (Other academic)
  • 22.
    Duras, Toni
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Applications of common principal components in multivariate and high-dimensional analysis2019Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis consists of four papers, all exploring some aspect of common principal component analysis (CPCA), the generalization of PCA to multiple groups. The basic assumption of the CPC model is that the space spanned by the eigenvectors is identical across several groups, whereas eigenvalues associated with the eigenvectors can vary. CPCA is used in essentially the same areas and applications as PCA.

    The first paper compares the performance of the maximum likelihood and Krzanowski’s estimators of the CPC model for two real-world datasets and in a Monte Carlo simulation study. The simplicity and intuition of Krzanowski's estimator and the findings in this paper support and promote the use of this estimator for CPC models over the maximum likelihood estimator.

    Paper number two uses CPCA as a tool for imposing restrictions on system-wise regression models. The paper contributes to the field by proposing a variety of explicit estimators, deriving their properties and identifying the appropriate amount of smoothing that should be imposed on the estimator. 

    In the third paper, a generalization of the fixed effects PCA model to multiple populations in a CPC environment is proposed. The model includes mainly geometrical, rather than probabilistic, assumptions, and is designed to account for any possible prior information about the noise in the data to yield better estimates, obtained by minimizing a least squares criterion with respect to a specified metric.

    The fourth paper survey some properties of the orthogonal group and the associated Haar measure on it. It is demonstrated how seemingly abstract results contribute to applied statistics and, in particular, to PCA.

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  • 23.
    Duras, Toni
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Aspects of common principal components2017Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    The focus of this thesis is the common principal component (CPC) model, the generalization of principal components to several populations. Common principal components refer to a group of multidimensional datasets such that their inner products share the same eigenvectors and are therefore simultaneously diagonalized by a common decorrelator matrix. Common principal component analysis is essentially applied in the same areas and analysis as its one-population counterpart. The generalization to multiple populations comes at the cost of being more mathematically involved, and many problems in the area remains to be solved.

    This thesis consists of three individual papers and an introduction chapter.In the first paper, the performance of two different estimation methods of the CPC model is compared for two real-world datasets and in a Monte Carlo simulation study. The second papers show that the orthogonal group and the Haar measure on this group plays an important role in PCA, both in single- and multi-population principal component analysis. The last paper considers using common principal component analysis as a tool for imposing restrictions on system-wise regression models. When the exogenous variables of a multi-dimensional model share common principal components, then each of the marginal models in the system is, up to their eigenvalues, identical. They henceform a class of regression models situated in between the classical seemingly unrelated regressions, where each set of explanatory variables is unique, and multivariate regression, where each marginal model shares the same common set of regressors.

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  • 24.
    Duras, Toni
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    The fixed effects PCA model in a common principal component environment2022In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 51, no 6, p. 1653-1673Article in journal (Refereed)
    Abstract [en]

    This paper explores multivariate data using principal component analysis (PCA). Traditionally, two different approaches to PCA have been considered, an algebraic descriptive one and a probabilistic one. Here, a third type of PCA approach, lying somewhere between the two traditional approaches, called the fixed effects PCA model, is considered. This model includes mainly geometrical, rather than probabilistic assumptions, such as the optimal choice of dimensionality and metric. The model is designed to account for any possible prior information about the noise in the data to yield better estimates. Parameters are estimated by minimizing a least-squares criterion with respect to a specified metric. A suggestion of how the fixed effects PCA estimates can be improved in a common principal component (CPC) environment is made. If the CPC assumption is fulfilled, then the fixed effects PCA model can consider more information by incorporating common principal component analysis (CPCA) theory into the estimation procedure. 

  • 25.
    Duras, Toni
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Holgersson, Thomas
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    A small excursion on the Haar measure on OpManuscript (preprint) (Other academic)
  • 26.
    Duras, Toni
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Holgersson, Thomas
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Common principal components with applications in regressionManuscript (preprint) (Other academic)
  • 27.
    Duras, Toni
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Javed, F.
    Lund University, Lund, Sweden.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics. Jönköping University, Jönköping International Business School, JIBS, Centre for Entrepreneurship and Spatial Economics (CEnSE).
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Söderberg, M.
    Griffith University, Brisbane, Australia.
    Using machine learning to select variables in data envelopment analysis: Simulations and application using electricity distribution data2023In: Energy Economics, ISSN 0140-9883, E-ISSN 1873-6181, Vol. 120, article id 106621Article in journal (Refereed)
    Abstract [en]

    Agencies that regulate electricity providers often apply nonparametric data envelopment analysis (DEA) to assess the relative efficiency of each firm. The reliability and validity of DEA are contingent upon selecting relevant input variables. In the era of big (wide) data, the assumptions of traditional variable selection techniques are often violated due to challenges related to high-dimensional data and their standard empirical properties. Currently, regulators have access to a large number of potential input variables. Therefore, our aim is to introduce new machine learning methods for regulators of the energy market. We also propose a new two-step analytical approach where, in the first step, the machine learning-based adaptive least absolute shrinkage and selection operator (ALASSO) is used to select variables and, in the second step, selected variables are used in a DEA model. In contrast to previous research, we find, by using a more realistic data-generating process common for production functions (i.e., Cobb–Douglas and Translog), that the performance of different machine learning techniques differs substantially in different empirically relevant situations. Simulations also reveal that the ALASSO is superior to other machine learning and regression-based methods when the collinearity is low or moderate. However, in situations of multicollinearity, the LASSO approach exhibits the best performance. We also use real data from the Swedish electricity distribution market to illustrate the empirical relevance of selecting the most appropriate variable selection method.

  • 28.
    Farghali, Rasha A.
    et al.
    Department of Mathematics, Insurance and Applied Statistics, Faculty of Commerce and Business Administration, Helwan University, Cairo, Egypt.
    Qasim, Muhammad
    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.
    Abonazel, Mohamed R.
    Department of Applied Statistics and Econometrics, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, Egypt.
    Generalized two-parameter estimators in the multinomial logit regression model: methods, simulation and application2023In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 52, no 7, p. 3327-3342Article in journal (Refereed)
    Abstract [en]

    In this article, we propose generalized two-parameter (GTP) estimators and an algorithm for the estimation of shrinkage parameters to combat multicollinearity in the multinomial logit regression model. In addition, the mean squared error properties of the estimators are derived. A simulation study is conducted to investigate the performance of proposed estimators for different sample sizes, degrees of multicollinearity, and the number of explanatory variables. Swedish football league dataset is analyzed to show the benefits of the GTP estimators over the traditional maximum likelihood estimator (MLE). The empirical results of this article revealed that GTP estimators have a smaller mean squared error than the MLE and can be recommended for practitioners.

  • 29.
    Habimana, Olivier
    Jönköping University, Jönköping International Business School, JIBS, Economics. Department of Applied Statistics, College of Business and Economics, University of Rwanda, Kigali, Rwanda.
    Asymmetric nonlinear mean reversion in real effective exchange rates: A Fisher-type panel unit root test applied to Sub-Saharan Africa2016In: Journal of Economic Asymmetries, ISSN 1703-4949, Vol. 14, p. 189-198Article in journal (Refereed)
    Abstract [en]

    This study investigates the nonlinear data generating processes of real effective exchange rates in a panel of Sub-Saharan African countries; the region with the highest transportation costs, trade barriers in international arbitrage and frequent central bank intervention in the foreign exchange market, which are plausible main sources of nonlinear and asymmetric deviations from purchasing power parity. By means of Monte Carlo simulations, we use the empirical distributions of the exponential smooth transition autoregressive (ESTAR), and the asymmetric ESTAR data generating processes to test for mean reversion in monthly real effective exchange rates. We then apply Fisher's inverse chi-square test that combines the observed significance levels of independent univariate unit root tests to test for panel unit roots. The findings suggest that once nonlinearities and asymmetries are taken into account, there is more evidence in favor of the purchasing power parity hypothesis. 

  • 30.
    Habimana, Olivier
    Jönköping University, Jönköping International Business School, JIBS, Economics. Department of Applied Statistics, College of Business and Economics, University of Rwanda, Kigali, Rwanda.
    Asymmetric Nonlinear Mean Reversion in Real Effective Exchange Rates: Evidence from Sub-Saharan AfricaManuscript (preprint) (Other academic)
    Abstract [en]

    This paper evaluates the purchasing power parity (PPP) theory in a panel of Sub-Saharan African (SSA) countries. The study applies unit root tests that are based on exponential smooth transition autoregressive (ESTAR) models to account for nonlinearities and asymmetries in real exchange rate adjustment towards its equilibrium (mean) value. Nonlinearities and asymmetries are very relevant for these countries and are potentially due to transaction costs, trade barriers and other market frictions, and frequent official interventions in the foreign exchange market. Results indicate that once nonlinearities and asymmetries are taken into account there is more empirical support for the PPP theory in SSA.

  • 31.
    Habimana, Olivier
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Asymmetry and multiscale dynamics in macroeconomic time series analysis2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis consists of three independent articles preceded by an introductory chapter. The first two articles focus on exchange rate dynamics in emerging market and developing economies, taking into account nonlinearities and asymmetries which are relevant for these countries and are potentially due to (i) transaction costs and other market frictions, and (ii) official intervention in the foreign exchange market. The third article is devoted to the analysis of the effects of monetary policy at different time horizons.

    The first article evaluates the purchasing power parity (PPP) theory in a panel of Sub-Saharan African countries. Unit root tests that are based on exponential smooth transition autoregressive (ESTAR) models are applied to account for nonlinearities and asymmetries in real exchange rate adjustment towards its equilibrium (mean) value. The results indicate empirical support for the PPP theory.

    The second article examines the relationship between current account adjustment and exchange rate flexibility in a panel of emerging market and developing economies. The purpose of this article is to (i) obtain a measure of exchange rate flexibility that considers autoregressive conditional heteroscedasticity and possible asymmetric responses of the exchange rate to shocks, and (ii) apply suitable dynamic panel data estimators to investigate this relationship. The results indicate that more flexible exchange rates are associated with faster current account adjustment.

    By means of wavelets the third article investigates the liquidity effect and the long-run neutrality of money at detailed timescales using time series data for Sweden and the US. The results indicate a significant liquidity effect at horizons of one to four years, but there is no evidence of monetary neutrality.

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  • 32.
    Habimana, Olivier
    Jönköping University, Jönköping International Business School, JIBS, Economics. Department of Applied Statistics, College of Business and Economics, University of Rwanda, Kigali, Rwanda.
    Do flexible exchange rates facilitate external adjustment? A dynamic approach with time-varying and asymmetric volatilityManuscript (preprint) (Other academic)
    Abstract [en]

    This paper revisits the claim that flexible exchange rates facilitate external adjustments, in a panel of emerging market and developing economies. In contrast to previous studies which mainly use the exchange rate regime classification as a proxy for exchange rate flexibility, the present study estimates a measure of exchange rate flexibility that considers autoregressive conditional heteroskedasticity (ARCH) effects and possible asymmetric responses of the exchange rate to shocks. Generalized method of moments (GMM) estimators are employed to estimate the dynamic relationship between exchange rate flexibility and the speed of current account adjustment. The results suggest that more flexible exchange rates are associated with faster adjustment of current account imbalances, and when the possibility of an asymmetric response of exchange rate to shocks is taken into account, the estimated speed of adjustment is even higher.

  • 33.
    Habimana, Olivier
    Jönköping University, Jönköping International Business School, JIBS, Economics. Department of Applied Statistics, College of Business and Economics, University of Rwanda, Kigali, Rwanda.
    Wavelet multiresolution analysis of the liquidity effect and monetary neutralityManuscript (preprint) (Other academic)
    Abstract [en]

    This paper employs wavelets to examine the relationship between money, interest and output on a scale-by-scale basis using data for the US and Sweden during 1985-2017. First, series are decomposed into orthogonal timescale components using the discrete wavelet transform (DWT) together with the Daubechies least asymmetric wavelet filter, and then causality analysis (in the Granger sense) is performed at each scale of variations. The dynamics at the finest scale of one-year movements indicate that interest rate and real output respond to movements in the quantity of money. At horizons of four years and above, there is a feedback mechanism. This pattern is very similar in both countries at the mentioned scales and suggests that monetary disturbances have significant real effects and these effects last longer than is assumed in pure real-business cycle models. Further, a locally weighted regression analysis suggests that not only are the direction and strength of the relationship among these variables scale-dependent but also the shape of the relationship may change from one scale to another. This method suggests a negative relationship between money and the short-term interest rate, as predicted by the liquidity preference theory, at cycles of one to four-year periods. Overall, these findings highlight the relevance of timescale decomposition in macroeconomic analysis.

  • 34.
    Habimana, Olivier
    Jönköping University, Jönköping International Business School, JIBS, Economics. Department of Applied Statistics, College of Business and Economics, University of Rwanda, Kigali, Rwanda.
    Wavelet multiresolution analysis of the liquidity effect and monetary neutrality2019In: Computational Economics, ISSN 0927-7099, E-ISSN 1572-9974, Vol. 53, no 1, p. 85-110Article in journal (Refereed)
    Abstract [en]

    This paper employs the maximum overlap discrete wavelet transform to obtain timescale decompositions of monetary aggregates, short-term interest rates and output to investigate two propositions in monetary economics: the liquidity effect and the long-run neutrality of money. Evidence from correlation and Granger causality over five timescales suggests that the liquidity effect is statistically significant in both the US and Sweden’s economies, with a shorter time horizon in the US than in Sweden. There is no evidence of monetary neutrality in both economies; at finest timescales, output Granger causes money in Sweden, whereas it is the other way around in the US. At long time horizons, there is a feedback between money and output in both economies. Key to our findings is that monetary disturbances have significant real effects and these effects last longer than it is assumed in real business cycle models.

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  • 35.
    Habimana, Olivier
    et al.
    Rwanda Academy of Sciences, Kigali, Rwanda.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    A wavelet-based approach for Johansen’s likelihood ratio test for cointegration in the presence of measurement errors: An application to CO2 emissions and real GDP data2021In: Communications in Statistics Case Studies Data Analysis and Applications, ISSN 2373-7484, Vol. 7, no 2, p. 128-145Article in journal (Refereed)
    Abstract [en]

    We suggest a wavelet filtering technique as a remedy to the problem of measurement errors when testing for cointegration using Johansen’s (1988) likelihood ratio test. Measurement errors, which more or less are always present in empirical economic data, essentially indicates that the variable of interest (the true signal) is contaminated with noise, which may induce biased and inconsistent estimates and erroneous inference. Our Monte Carlo experiments demonstrate that measurement errors distort the statistical size of Johansen’s cointegration test in finite samples; the test is significantly oversized. A contribution and major finding of this article is that the proposed wavelet-based technique significantly improves the statistical size of the traditional Johansen test in small and medium sized samples. Since Johansen’s test is a standard cointegration test, and we demonstrate that the constantly present measurement errors in empirical data over sizes the test, this simple alteration can be used in most situations with more reliable finite sample inference. We empirically examine the long-run relation between CO2 emissions and the real GDP in the G7 countries. The traditional Johansen tests provide evidence of an equilibrium relation for Canada and weak evidence for the US. However, the suggested size-unbiased wavelet-filtering approach consistently indicates no evidence of cointegration for all six countries.

  • 36.
    Habimana, Olivier
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics. Department of Applied Statistics, College of Business and Economics, University of Rwanda, Kigali, Rwanda.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Testing for nonlinear unit roots in the presence of a structural break2015In: Festschrift in honor of Professor Ghazi Shukur on the occasion of his 60th birthday / [ed] Thomas Holgersson, Växjö: Linnaeus University Press , 2015Chapter in book (Other academic)
  • 37.
    Habimana, Olivier
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics. Department of Applied Statistics, College of Business and Economics, University of Rwanda, Kigali, Rwanda.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Testing for nonlinear unit roots in the presence of a structural break with an application to the qualified PPP during the 1997 Asian financial crisis2018In: International journal of finance and economics, ISSN 1076-9307, E-ISSN 1099-1158, Vol. 23, no 3, p. 221-232Article in journal (Refereed)
    Abstract [en]

    This paper applies Monte Carlo simulations to evaluate the size and power properties in the presence of a structural break, for the standard Augmented Dickey-Fuller (ADF) test versus nonlinear exponential smooth transition autoregressive unit root tests. The break causes the tests to be undersized, and the statistical power considerably decreases. Moreover, the effect is intensified in small samples and very much increased for more persistent nonlinear series. As a remedy, we modify the standard ADF and exponential smooth transition autoregressive unit root tests in order to adjust for a structural break. This improves both the power and the size considerably, even though the empirical size still is lower than the nominal one. More persistent series are more affected by structural breaks, and the new tests are most powerful under the existence of a rather persistent nonlinear data generating process (which is an empirically relevant and common type of data generating process). The proposed tests are applied to investigate mean reversion in the real effective exchange rates of 5 East and Southeast Asian countries, taking into account the structural change in exchange rate regime brought about by the 1997 Asian financial crisis. The empirical findings corroborate our simulation results; the modified more powerful tests are able to reject the unit root in all 5 countries, whereas the tests that do not consider the structural break could only reject in one of these cases.

  • 38.
    Hacker, R. Scott
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Hatemi-J, A.
    College of Business and Economics, UAE University, Al Ain, United Arab Emirates.
    Model selection in time series analysis: using information criteria as an alternative to hypothesis testing2022In: Journal of economic studies, ISSN 0144-3585, E-ISSN 1758-7387, Vol. 49, no 6, p. 1055-1075Article in journal (Refereed)
    Abstract [en]

    Purpose: The issue of model selection in applied research is of vital importance. Since the true model in such research is not known, which model should be used from among various potential ones is an empirical question. There might exist several competitive models. A typical approach to dealing with this is classic hypothesis testing using an arbitrarily chosen significance level based on the underlying assumption that a true null hypothesis exists. In this paper, the authors investigate how successful the traditional hypothesis testing approach is in determining the correct model for different data generating processes using time series data. An alternative approach based on more formal model selection techniques using an information criterion or cross-validation is also investigated.

    Design/methodology/approach: Monte Carlo simulation experiments on various generating processes are used to look at the response surfaces resulting from hypothesis testing and response surfaces resulting from model selection based on minimizing an information criterion or the leave-one-out cross-validation prediction error.

    Findings: The authors find that the minimization of an information criterion can work well for model selection in a time series environment, often performing better than hypothesis-testing strategies. In such an environment, the use of an information criterion can help reduce the number of models for consideration, but the authors recommend the use of other methods also, including hypothesis testing, to determine the appropriateness of a model.

    Originality/value: This paper provides an alternative approach for selecting the best potential model among many for time series data. It demonstrates how minimizing an information criterion can be useful for model selection in a time-series environment in comparison to some standard hypothesis testing strategies.

  • 39.
    Hacker, R. Scott
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Hatemi-J, Abdulnasser
    UAE University, Department of Economics and Finance.
    A bootstrap test for causality with endogenous lag length choice: theory and application in finance2012In: Journal of economic studies, ISSN 0144-3585, E-ISSN 1758-7387, Vol. 39, no 2, p. 144-160Article in journal (Refereed)
    Abstract [en]

    Purpose – In all existing theoretical papers on causality it is assumed that the lag length is known a priori. However, in applied research the lag length has to be selected before testing for causality. The purpose of this paper is to suggest that in investigating the effectiveness of various Granger causality testing methodologies, including those using bootstrapping, the lag length choice should be endogenized, by which we mean the data-driven preselection of lag length should be taken into account.

    Design/methodology/approach – The size and power of a bootstrap test with endogenized lag-length choice are investigated by simulation methods. A statistical software component is produced to implement the test, which is available online.

    Findings – The simulation results show that this test performs well. An application of the test provides empirical support for the hypothesis that the UAE financial market is integrated with the US market.

    Social implications – The empirical results based on this test are expected to be more precise.

    Originality/value – This paper considers a bootstrap test for causality with endogenous lag order. This test has superior properties compared to existing causality tests in terms of size, with similar if not better power and it is robust to ARCH effects that usually characterize financial data. Practitioners interested in causal inference based on time series data might find the test valuable.

  • 40.
    Hacker, R. Scott
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Hatemi-J, Abdulnasser
    United Arab Emirates University.
    Properties of Procedures Dealing with Uncertainty about Intercept and Deterministic Trend in Unit Root Testing2014In: Empirical Economics Review, ISSN 2222-9736, Vol. 3, no 1, p. 83-97Article in journal (Refereed)
    Abstract [en]

    The classic Dickey-Fuller unit-root test can be applied using three different equations, depending upon the inclusion of a constant and/or a time trend in the regression equation. This paper investigates the size and power properties of a unit-root testing strategy outlined in Enders (2004), which allows for repeated testing of the unit root with the three equations depending on the significance of various parameters in the equations. This strategy is similar to strategies suggested by others for unit root testing. Our Monte Carlo simulation experiments show that serious mass significance problems prevail when using the strategy suggested by Enders. Excluding the possibility of unrealistic outcomes and using a priori information on whether there is a trend in the underlying time series, as suggested by Elder and Kennedy (2001), reduces the mass significance problem for the unit root test and improves power for that test. Subsequent testing for whether a trend exists is seriously affected by testing for the unit root first, however.

  • 41.
    Holgersson, Thomas
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    A Modified Skewness Measure for Testing Asymmetry2010In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 39, no 2, p. 335-346Article in journal (Refereed)
  • 42.
    Holgersson, Thomas
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Festschrift in honor of Professor Ghazi Shukur on the occasion of his 60th birthday2015Collection (editor) (Other academic)
  • 43.
    Holgersson, Thomas
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Testing for Multivariate Autocorrelation2004In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 31, no 4, p. 379-395Article in journal (Refereed)
  • 44.
    Holgersson, Thomas
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Karlsson, Peter
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    An Investigation and Development of Three Estimators of Inverse Covariance Matrices With Applications to the Mahalanobis Distance2010Report (Other academic)
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  • 45.
    Holgersson, Thomas
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Karlsson, Peter
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Model Based vs. Model Independent Tests for Cross-correlation2010In: Journal of Modern Applied Statistical Methods, ISSN 1538-9472, Vol. 9, no 1, p. 75-89Article in journal (Refereed)
    Abstract [en]

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

  • 46.
    Holgersson, Thomas
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Karlsson, Peter
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Dai, Deliang
    Linnaeus University.
    Expected and Unexpected Values of Individual mahalanobis Distances2013Conference paper (Refereed)
  • 47.
    Holgersson, Thomas
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Karlsson, Peter
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Mansoor, Rashid
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Estimating mean-standard deviation ratios of financial data2012In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 39, no 3, p. 657-671Article in journal (Refereed)
    Abstract [en]

    This article treats the problem of linking the relation between excess return and risk of financial assets when the returns follow a factor structure. The authors propose three different estimators and their consistencies are established in cases when the number of assets in the cross-section (n) and the number of observations over time (T) are of comparable size. An empirical investigation is conducted on the Stockholm stock exchange market where the mean-standard deviation ratio is calculated for small- mid- and large cap segments, respectively.

  • 48.
    Holgersson, Thomas
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Lindström, Fredrik
    Göteborg University, Dept. of statistics.
    A Comparison of Conditioned Versus Unconditioned Forecasts of the VAR(1) Process2005In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 34, p. 415-427Article in journal (Refereed)
  • 49.
    Holgersson, Thomas
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Mansoor, Rashid
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Assessing Normality of High-Dimensional Data2013In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 42, no 2, p. 360-369Article in journal (Refereed)
    Abstract [en]

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

  • 50.
    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.

123 1 - 50 of 130
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