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  • 1.
    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. 

  • 2.
    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.

  • 3.
    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. 

  • 4.
    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 1028-6276Article 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.

  • 5.
    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 model2017In: Statistical papers, ISSN 0932-5026, E-ISSN 1613-9798Article 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. 

  • 6. 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)
  • 7.
    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. 

  • 8.
    Duras, Toni
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    A comparison of two estimation methods for common principal componentsManuscript (preprint) (Other academic)
  • 9.
    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)
  • 10.
    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.

  • 11.
    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.

  • 12.
    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)
  • 13.
    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)
  • 14.
    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. 

  • 15.
    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.

  • 16.
    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.

  • 17.
    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.

  • 18.
    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.

  • 19.
    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.

  • 20.
    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)
  • 21.
    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.

  • 22.
    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.

  • 23.
    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.

  • 24.
    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)
  • 25.
    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)
  • 26.
    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)
  • 27.
    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)
  • 28.
    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)
  • 29.
    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)
  • 30.
    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.

  • 31.
    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)
  • 32.
    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.

  • 33.
    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.

  • 34.
    Holgersson, Thomas
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Nordström, Louise
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Öner, Özge
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Dummy variables vs. category-wise models2014In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 41, no 2, p. 233-241Article in journal (Refereed)
    Abstract [en]

    Empirical research frequently involves regression analysis with binary categorical variables, which are traditionally handled through dummy explanatory variables. This paper argues that separate category-wise models may provide a more logical and comprehensive tool for analysing data with binary categories. Exploring different aspects of both methods, we contrast the two with a Monte Carlo simulation and an empirical example to provide a practical insight.

  • 35.
    Holgersson, Thomas
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Nordström, Louise
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Öner, Özge
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    On regression modelling with dummy variables versus separate regressions per group: comment on Holgersson et al.2016In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 43, no 8, p. 1564-1565Article in journal (Other academic)
  • 36.
    Holgersson, Thomas
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Norman, Therese
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Tavassoli, Sam
    Blekinge Institute of Technology.
    In the quest for economic significance: assessing variable importance through mean value decomposition2013Report (Refereed)
    Abstract [en]

    Economic significance is frequently assessed through statistical hypothesis testing. This habitual use is, however, usually not matching with the implicit economical questions being addressed. In this paper we propose using mean value decomposition to assess economic significance. Unlike most previously suggested methods the proposed one is intuitive and simple to conduct. The technique is demonstrated and contrasted with hypothesis tests by an empirical example involving the income of Mexican children, which shows that the two inference approaches provide different and supplementary pieces of information.

  • 37.
    Johansson, Ulf
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Department of Information Technology, University of Borås, Sweden.
    Linusson, Henrik
    Department of Information Technology, University of 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, Sweden.
    Boström, Henrik
    School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Sweden.
    Interpretable regression trees using conformal prediction2018In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 97, p. 394-404Article in journal (Refereed)
    Abstract [en]

    A key property of conformal predictors is that they are valid, i.e., their error rate on novel data is bounded by a preset level of confidence. For regression, this is achieved by turning the point predictions of the underlying model into prediction intervals. Thus, the most important performance metric for evaluating conformal regressors is not the error rate, but the size of the prediction intervals, where models generating smaller (more informative) intervals are said to be more efficient. State-of-the-art conformal regressors typically utilize two separate predictive models: the underlying model providing the center point of each prediction interval, and a normalization model used to scale each prediction interval according to the estimated level of difficulty for each test instance. When using a regression tree as the underlying model, this approach may cause test instances falling into a specific leaf to receive different prediction intervals. This clearly deteriorates the interpretability of a conformal regression tree compared to a standard regression tree, since the path from the root to a leaf can no longer be translated into a rule explaining all predictions in that leaf. In fact, the model cannot even be interpreted on its own, i.e., without reference to the corresponding normalization model. Current practice effectively presents two options for constructing conformal regression trees: to employ a (global) normalization model, and thereby sacrifice interpretability; or to avoid normalization, and thereby sacrifice both efficiency and individualized predictions. In this paper, two additional approaches are considered, both employing local normalization: the first approach estimates the difficulty by the standard deviation of the target values in each leaf, while the second approach employs Mondrian conformal prediction, which results in regression trees where each rule (path from root node to leaf node) is independently valid. An empirical evaluation shows that the first approach is as efficient as current state-of-the-art approaches, thus eliminating the efficiency vs. interpretability trade-off present in existing methods. Moreover, it is shown that if a validity guarantee is required for each single rule, as provided by the Mondrian approach, a penalty with respect to efficiency has to be paid, but it is only substantial at very high confidence levels.

  • 38.
    Johansson, Ulf
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Löfström, Tuve
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Boström, Henrik
    School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Sweden.
    Calibrating probability estimation trees using Venn-Abers predictors2019In: SIAM International Conference on Data Mining, SDM 2019, Society for Industrial and Applied Mathematics, 2019, p. 28-36Conference paper (Refereed)
    Abstract [en]

    Class labels output by standard decision trees are not very useful for making informed decisions, e.g., when comparing the expected utility of various alternatives. In contrast, probability estimation trees (PETs) output class probability distributions rather than single class labels. It is well known that estimating class probabilities in PETs by relative frequencies often lead to extreme probability estimates, and a number of approaches to provide more well-calibrated estimates have been proposed. In this study, a recent model-agnostic calibration approach, called Venn-Abers predictors is, for the first time, considered in the context of decision trees. Results from a large-scale empirical investigation are presented, comparing the novel approach to previous calibration techniques with respect to several different performance metrics, targeting both predictive performance and reliability of the estimates. All approaches are considered both with and without Laplace correction. The results show that using Venn-Abers predictors for calibration is a highly competitive approach, significantly outperforming Platt scaling, Isotonic regression and no calibration, with respect to almost all performance metrics used, independently of whether Laplace correction is applied or not. The only exception is AUC, where using non-calibrated PETs together with Laplace correction, actually is the best option, which can be explained by the fact that AUC is not affected by the absolute, but only relative, values of the probability estimates. 

  • 39.
    Johansson, Ulf
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Löfström, Tuve
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Linusson, Henrik
    Högskolan i Borås, Department of Information Technology, Borås, Sweden.
    Boström, Henrik
    The Royal Institute of Technology (KTH), School of Electrical Engineering and Computer Science, Stockholm, Sweden.
    Efficient Venn Predictors using Random Forests2019In: Machine Learning, ISSN 0885-6125, E-ISSN 1573-0565, Vol. 108, no 3, p. 535-550Article in journal (Refereed)
    Abstract [en]

    Successful use of probabilistic classification requires well-calibrated probability estimates, i.e., the predicted class probabilities must correspond to the true probabilities. In addition, a probabilistic classifier must, of course, also be as accurate as possible. In this paper, Venn predictors, and its special case Venn-Abers predictors, are evaluated for probabilistic classification, using random forests as the underlying models. Venn predictors output multiple probabilities for each label, i.e., the predicted label is associated with a probability interval. Since all Venn predictors are valid in the long run, the size of the probability intervals is very important, with tighter intervals being more informative. The standard solution when calibrating a classifier is to employ an additional step, transforming the outputs from a classifier into probability estimates, using a labeled data set not employed for training of the models. For random forests, and other bagged ensembles, it is, however, possible to use the out-of-bag instances for calibration, making all training data available for both model learning and calibration. This procedure has previously been successfully applied to conformal prediction, but was here evaluated for the first time for Venn predictors. The empirical investigation, using 22 publicly available data sets, showed that all four versions of the Venn predictors were better calibrated than both the raw estimates from the random forest, and the standard techniques Platt scaling and isotonic regression. Regarding both informativeness and accuracy, the standard Venn predictor calibrated on out-of-bag instances was the best setup evaluated. Most importantly, calibrating on out-of-bag instances, instead of using a separate calibration set, resulted in tighter intervals and more accurate models on every data set, for both the Venn predictors and the Venn-Abers predictors.

  • 40.
    Karlsson, Peter
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    On the Asymptotics of Increasing Dimension Models: Methods for Complete or Incomplete Data2009Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    In some multivariate contexts there is a close relation between the number of parameters (p) and the number of observations (n). In a situation where p grows with n it frequently happens that the statistic does not converge to its true parameter. An additional issue is if the data set also contain missing observations and , for example, as p grows with n so does the number of missing observations. This situation arises in the context of empirical applications of the Arbitrage Pricing Theory model, where the data is incomplete due to the nature of stocks leaving or entering the stock exchange. In this thesis two situations of increasing dimension are considered: firstly, the case of complete data sets where the statistic of interest is the inverse covariance matrix where three types of shrinkage estimators of the inverse covariance matrix are investigated, particularly as an ingredient of a composite estimator, specifically Zellners seemingly unrelated regression  models and the Mahalanobis distance. Secondly, the case arising in empirical application of the APT model where the data set is incomplete and the interest is to model the underlying covariance structure among the variables by a few factors. Two possible solutions to the problem are considered and a case study using the Swedish OMX data is conducted for demonstration.

  • 41.
    Karlsson, Peter S.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Issues of incompleteness, outliers and asymptotics in high dimensional data2011Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis consists of four individual essays and an introduction chapter. The essays are in the field of multivariate statistical analysis of High dimensional data. The first essay presents the issue of estimating the inverse covariance matrix alone and when it is used within the Mahalanobis distance in High-dimensional data. Three types of ridge-shrinkage estimators of the inverse covariance matrix are suggested and evaluated through Monte Carlo simulations. The second essay deals with incomplete observations in empirical applications of the Arbitrage Pricing Theory model and the interest is to model the underlying covariance structure among the variables by a few common factors. Two possible solutions to the problem are considered and a

    case study using the Swedish OMX data is conducted for demonstration. In the third essay the issue of outlier detection in High-dimensional data is treated. A number of point estimators of the Mahalanobis distance are suggested and their properties are evaluated. In the fourth and last essay the relation between the second central moment of a distribution to its first raw moment is considered in an financial context. Three possible estimators are considered and it is shown that they are consistent even when the dimension increases proportionally to the number of observations.

  • 42.
    Karlsson, Peter S.
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Point estimators of the Mahalanobis distance in high-dimensional dataManuscript (preprint) (Other academic)
    Abstract [en]

    This paper concerns the problem of estimating the Mahalanobis distance when the dimension of the data matrix is comparable to the sample size. Two different ridge-shrinkage estimators are considered and estimators of related risk functions are derived. The properties of these point estimators are investigated in terms of excess risk and bias relative to the traditional estimator.

  • 43.
    Karlsson, Peter S.
    et al.
    Departments of Economics and Statistics, Linnaeus University, Växjö, Sweden .
    Behrenz, Lars
    Departments of Economics and Statistics, Linnaeus University, Växjö, Sweden .
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Performances of Model Selection Criteria When Variables are Ill Conditioned2019In: Computational Economics, ISSN 0927-7099, E-ISSN 1572-9974, Vol. 54, no 1, p. 77-98Article 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 ((Formula presented.)-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 (Formula presented.)-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 (Formula presented.). 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. 

  • 44.
    Khalaf, Ghadban
    et al.
    Department of Mathematics, King Khalid University, Abha, Saudi Arabia.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Modified Ridge Regression Estimators2013In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 42, no 8, p. 1476-1487Article in journal (Refereed)
    Abstract [en]

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

  • 45.
    Kibria, B. M. Golam
    et al.
    Department of Mathematics and Statistics, Florida International University, Miami, Florida, USA.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    A simulation study of some biasing parameters for the ridge type estimation of Poisson regression2015In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 44, no 4, p. 943-957Article in journal (Refereed)
    Abstract [en]

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

  • 46.
    Kim Karlsson, Hyunjoo
    et al.
    The Linnaeus University, Växjö, Sweden.
    Karlsson, Peter
    Jönköping University, Jönköping International Business School, JIBS, Statistics. The Linnaeus University, Växjö, Sweden .
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Wavelet quantile analysis of asymmetric pricing on the Swedish power market2017In: Empirica, ISSN 0340-8744, E-ISSN 1573-6911, Vol. 44, no 2, p. 249-260Article in journal (Refereed)
    Abstract [en]

    In this article we investigate if the Swedish consumer prices for electricity are adjusted equally fast regardless of whether the NordPool power market prices are decreased or increased. Due to relatively moderate variations in the variables, we have applied quantile regression, since it is mainly the large changes (above the median) that essentially tend to have a considerable effect on the consumer prices. Moreover, in order to adjust for stochastic- and deterministic trends, autocorrelation, structural breaks as well as to measure APT effects in the short- and in the medium-run, we apply a wavelet decomposition approach. Our results show evidence that significantly positive asymmetric price transmission (APT) effects exist in this market. More specifically, in the short-run (based on the wavelet decomposition D1 for 1–2 months cycles), we find that that there is a higher propensity to rapidly and systematically increase the consumer prices subsequently to an increase in the NordPool market price, compared with the propensity to decrease their customers prices subsequently to a corresponding drop in the NordPool market prices. However, no significant APT effects were detected in the medium- or in the long-run (i.e. the asymmetric price transmission effects are observed only in the short-run). In summary, we could isolate significant APT effects in the short-run (1–2 months decomposition cycles), and for large changes in the dependent variable (percentiles = 0.9). Therefore, only large changes in the NordPool prices lead to feedback effects in the form of asymmetric price transmission effects. Our evidence supports the notion of firms’ downward stickiness of retail prices for maximizing profit, which are not expected to be found on a fully efficient market. Although our finding shows that the price inefficiency is short-lived, these large temporal inefficiencies are still costly for the consumers. It should be noted that blunt traditional powerless methods do not detect these APT effects, while our wavelet quantile methods are powerful and make a significant contribution in the literature by providing new empirical evidence.

  • 47.
    Lantz, Björn
    et al.
    Chalmers University of Technology, Göteborg, Sweden .
    Andersson, Roy
    Jönköping University, School of Engineering, JTH, Industrial Engineering and Management. Jönköping University, School of Engineering, JTH. Research area Industrial Production.
    Manfredsson, Peter
    University of Borås, Sweden.
    Preliminary tests of normality when comparing three independent samples2016In: Journal of Modern Applied Statistical Methods, ISSN 1538-9472, Vol. 15, no 2, p. 135-148Article in journal (Refereed)
    Abstract [en]

    This paper uses simulation to explore the performance of a two-stage procedure where a preliminary Shapiro–Wilk test is used to choose between ANOVA and the Kruskal–Wallis test as three-sample location test. The results suggest that the two-stage procedure actually seems to be preferable when conducting such location tests.

  • 48.
    Li, Yushu
    et al.
    Department of Economic and Statistics, Center for Labor Market Policy Research (CAFO), Linaeus University, Växjö, Sweden.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Testing for Unit Root Against LSTAR Models: Wavelet Improvement under GARCH Distortion2010In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 39, no 2, p. 277-286Article in journal (Refereed)
    Abstract [en]

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

  • 49.
    Li, Yushu
    et al.
    Department of Economic and Statistics, Center for Labor Market Policy Research (CAFO), Linnaeus University, Sweden .
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Wavelet Improvement of the Over-rejection of Unit root test under GARCH errors: An Application to Swedish Immigration Data2011In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 40, no 13, p. 2385-2396Article in journal (Refereed)
    Abstract [en]

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

  • 50.
    Lindström, Fredrik
    et al.
    Försäkringskassan.
    Holgersson, Thomas
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Forecast mean squared error reductionin the VAR(1) process2009In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 36, no 12, p. 1369-1384Article in journal (Refereed)
12 1 - 50 of 80
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