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  • 1. A. Alkhamisi, Mahdi
    et al.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    The Effect of Fat-Tailed Error Terms on the Properties of the Systemwise RESET Test2008In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 35, no 1, p. 101-113Article in journal (Refereed)
  • 2. Alkamisi, M. A.
    et al.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Bayesian Analysis of a Linear Mixed Model with AR(p) errors Via MCMC2005In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 32, no 7, p. 741-755Article in journal (Refereed)
  • 3. Almasri, Abdullah
    et al.
    Locking, Håkan
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Testing for climate warming in Sweden during 1850-1999, using wavelets analysis2008In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 35, no 4, p. 431-443Article in journal (Refereed)
  • 4.
    Hacker, R Scott
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Hatemi-J, Abdulnasser
    Optimal Lag Length Choice in Stable and Unstable VAR Models under Situations of Homoscedasticity and ARCH2008In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 35, no 6, p. 601-615Article in journal (Refereed)
    Abstract [en]

    The performance of different information criteria - namely Akaike, corrected Akaike (AICC), Schwarz-Bayesian (SBC), and Hannan-Quinn - is investigated so as to choose the optimal lag length in stable and unstable vector autoregressive (VAR) models both when autoregressive conditional heteroscedasticity (ARCH) is present and when it is not. The investigation covers both large and small sample sizes. The Monte Carlo simulation results show that SBC has relatively better performance in lag-choice accuracy in many situations. It is also generally the least sensitive to ARCH regardless of stability or instability of the VAR model, especially in large sample sizes. These appealing properties of SBC make it the optimal criterion for choosing lag length in many situations, especially in the case of financial data, which are usually characterized by occasional periods of high volatility. SBC also has the best forecasting abilities in the majority of situations in which we vary sample size, stability, variance structure (ARCH or not), and forecast horizon (one period or five). frequently, AICC also has good lag-choosing and forecasting properties. However, when ARCH is present, the five-period forecast performance of all criteria in all situations worsens.

  • 5.
    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)
  • 6.
    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.
    Three estimators of the Mahalanobis distance in high-dimensional data2012In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 39, no 12, p. 2713-2720Article in journal (Refereed)
    Abstract [en]

    This paper treats the problem of estimating the Mahalanobis distance for the purpose of detecting outliersin high-dimensional data. Three ridge-type estimators are proposed and risk functions for deciding anappropriate value of the ridge coefficient are developed. It is argued that one of the ridge estimator hasparticularly tractable properties, which is demonstrated through outlier analysis of real and simulated data.

  • 7.
    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.

  • 8.
    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.

  • 9.
    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)
  • 10. Hussain, Shakir
    et al.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Estimation and Forecasting of Hospital Admission Due to Influenza: Planning for Winter Pressure. The Case of the West Midlands, UK2005In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 32, no 3, p. 191-205Article in journal (Refereed)
  • 11.
    Kibria, B. M. Golam
    et al.
    Department of Mathematics and Statistics, Florida International University, Miami, Florida, USA.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Some ridge regression estimators for the zero-inflated Poisson model2013In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 40, no 4, p. 721-735Article in journal (Refereed)
    Abstract [en]

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

  • 12.
    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)
  • 13.
    Månsson, Kristofer
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Kibria, B. M. Golam
    Florida International University.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    A restricted Liu estimator for binary regression models and its application to an applied demand system2016In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 43, no 6, p. 1119-1127Article in journal (Refereed)
    Abstract [en]

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

  • 14.
    Shukur, Ghazi
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Almasri, Abdullah
    An Illustration of the Causality Relation between Government Spending and Revenue Using Wavelet Analysis on Finnish Data2003In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 30, no 5, p. 571-584Article in journal (Refereed)
  • 15.
    Shukur, Ghazi
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Hatemi-J, Abdulanaser
    Multivariate-based causality tests of twin deficits in the US2002In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 29, no 6, p. 817-824Article in journal (Refereed)
  • 16.
    Shukur, Ghazi
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Hussain, Shakir
    Testing for Autocorrelation in Non-stationary Dynamic Systems of Equations2003In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 30, no 4, p. 441-454Article in journal (Refereed)
  • 17.
    Shukur, Ghazi
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Mantalos, Panagiotis
    A Simple Investigation of the Granger-Causality Test in Integrated-Cointegrated VAR Systems2000In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 27, no 8, p. 1021-1031Article in journal (Refereed)
  • 18.
    Shukur, Ghazi
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Mantalos, Panagiotis
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    The Effect of the Spillover on the Granger Causality Test2010In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 37, no 9, p. 1473-1486Article in journal (Refereed)
  • 19.
    Shukur, Ghazi
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Zeebari, Zangin
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Median regression for SUR models with the same explanatory variables in each equation2012In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 39, no 8, p. 1765-1779Article in journal (Refereed)
    Abstract [en]

    In this paper we introduce an interesting feature of the generalized least absolute deviations method for seemingly unrelated regression equations (SURE) models. Contrary to the collapse of generalized leasts-quares parameter estimations of SURE models to the ordinary least-squares estimations of the individual equations when the same regressors are common between all equations, the estimations of the proposed methodology are not identical to the least absolute deviations estimations of the individual equations. This is important since contrary to the least-squares methods, one can take advantage of efficiency gain due to cross-equation correlations even if the system includes the same regressors in each equation.

  • 20.
    Zeebari, Zangin
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Developing ridge estimation method for median regression2012In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 39, no 12, p. 2627-2638Article in journal (Refereed)
    Abstract [en]

    In this paper, the ridge estimation method is generalized to the median regression. Though the least absolute deviation (LAD) estimation method is robust in the presence of non-Gaussian or asymmetric error terms, it can still deteriorate into a severe multicollinearity problem when non-orthogonal explanatory variables are involved. The proposed method increases the efficiency of the LAD estimators by reducing the variance inflation and giving more room for the bias to get a smaller mean squared error of the LAD estimators. This paper includes an application of the new methodology and a simulation study as well.

1 - 20 of 20
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