Welcome to Probability, Statistics and Econometric Journal

Editor-in-Chief: Professor Francis Diebold
ISSN: 9301 – 4221

(print version) 5342 – 3371 (electronic version)

Current: Vol. 1; Issue 3; 2019: Frequency: Bimonthly

The Probability Statistics and Econometric Journal offers peer-reviewed original papers, reviews and survey articles focusing on statistical and econometric and probability, dealing with the applications of existing or new techniques to a wide variety of problems in business, finance, economics and related subjects.

Coverage includes the most current progress on topics such us: Techniques for evaluating analytically intractable problems such as high-dimensional multivariate integrals, Search and Optimization Methods, Computer Intensive Statistical Methods, Simulation and Monte Carlo, Asymptotic statistics, Bayesian Statistics, Biostatistics, Business statistics, Computational statistics, Econometric Techniques, Regression Analysis, Statistical Analysis with complex data, Time series analysis, Singular Spectrum Analysis, Mathematical Statistics, Markov Processes, Stochastic Differential Equations, and Financial Market Microstructure.

Professor Tim Bollerslev

Duke University, Durham, North Carolina, United States

Ana M. Aguilera, University of Granada, Spain

Christina Beneki

Technological Educational Institution of Ionian Islands, Greece

Cristina Sánchez Figueroa

National University of Distance Education, Spain

Jaya P.N. Bishwal 

University of North Carolina at Charlotte, USA

Junfeng Shang

Bowling Green State University, USA

Junsoo Lee

University of Alabama, USA

Wing-Keung Wong

 Asia University, Taiwan

Xuewen Lu

 University of Calgary, Canada

Professor Francis Diebold

University of Pennsylvania, Philadelphia, United States

Dr. Herman K. Van Dijk

Erasmus University Rotterdam, Rotterdam, Netherlands

Dr. Mike West

Duke University, Durham, North Carolina, United States

Dr. Monica Billio

University of Venice, Italy

Dr. Masayuki Hirukawa, PhD

Ryukoku University, Kyoto, Japan

Dr. Tommaso Proietti

University of Rome, Italy

Dr. Armelle Guillou

University of Strasbourg, France

Coming soon

Recent Publications

DOES METHOD MATTER FOR FDI SPILLOVERS? EVIDENCE FROM MANUFACTURING FIRMS IN NIGERIA

Adamu Jibrilla

Abstract:: Several studies have examined the effects of FDI spillovers through the horizontal and backward channels in different countries, with little success on settling the debate in the literature regarding the benefits of FDI on the productivity of domestic firms. Mixed results have been produced by previous studies which have been attributed to many factors especially country and firm specific factors. While considerable attention has been devoted to these factors, little has been given to examining whether differences in the existing empirical findings are due to differences in productivity measures. This study examines these differences in productivity measures using manufacturing firm level panel data on Nigeria. After estimating twelve different models with four different productivity measures and three estimation techniques, this study consistently finds evidence in support of positive horizontal spillovers effects in eleven models. Backward spillover effects vary according to measures of productivity employed

STANDARDIZATION TECHNIQUE AND CENTERING ON THE VARIANCE INFLATION FACTOR OF A STRUCTURED COLLINEAR MODEL

Ijomah Maxwell Azubuike

Abstract: : Centering and standardizing data are fundamental data manipulations, widely used in the derivation of statistical theory, the improvement of numerical computation, and at times in understanding and reporting statistical models. In this paper, we used variance inflation factor (VIF) from a hypothetical regression model to illustrate the effectiveness of centering and standardization in solving multicollinear regression problem. This procedure allowed us to identify how VIF as a collinearity diagnostic responds when a model is centered or standardized for linear, quadratic and interaction components. Our findings revealed that use of centering and standardization resulted in poor efficiency for very severe collinearity. Based on our analysis, we conclude that centering and standardization on mean is only necessary in some conditions

ON THE SEVERITY OF MULTICOLLINEAR VARIABLES IN A LINEAR REGRESSION MODEL

Ijomah Maxwell Azubuike

Abstract: The problem of multicollinearity in regression is well known and published but the severity of this problem using available diagnostics has not been well established. There seems to be disagreement among researchers regarding the cut off point for severe collinearity in a multiple regression model. In this paper, a simulation study was carried out with various scenarios of different collinearity diagnostics to investigate the effects of collinearity under various correlation structures amongst two explanatory variables and to compare these results with existing guidelines to decide harmful collinearity. Our result reveals that a variance inflation factor above 5 (VIF > 5) or eigenvalue less than 0.1 is an indication of severe collinearity

EFFECTS OF ALGEBRAIC BOARD GAME ON SECONDARY SCHOOL STUDENT’S INTEREST AND ACHIEVEMENT IN ALGEBRAIC EXPRESSIONS

Udeh Ikemefuna James, Edeoga Benjamin Odo, Okpube, Nnaemeka Michael

Abstract: TThis study, investigated the Effect of Algebraic Board Game on Students Interest and Achievement in Algebraic Expressions in Junior Secondary Schools in Enugu State. Six research questions and six null hypotheses guided the study. The population of the study comprised all JSS11 students in all public secondary school within the study area. The sample of the study comprised 455 students who participated in the study. Mathematics Interest Inventory and Mathematics Achievement Test of the multiple choice type which contained 28 items and 40 items respectively with a reliability coefficients of 0.74 and 0.71 respectively were used for data collection. Data collected were analyzed using mean and standard deviation to answer the research questions and ANCOVA statistics to test the hypotheses at 0.05 level of significance.