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Books on the topic 'Multicollinearity'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 January 2023

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1

Pineda, Octavio Luis. La multicolinealidad en econometría: Diagnóstico y corrección del problema. México, D.F: SITESA, Sistemas Técnicos de Edición, 1992.

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2

Moineddin, Rahim. Comments on Mallows' Cp statistics and multicollinearity effects on predictions. Ottawa: National Library of Canada, 2001.

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3

Kalivas, John H. Mathematical analysis of spectral orthogonality. New York: M. Dekker, 1994.

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4

Das Problem der Multikollinearität in Regressionsanalysen. Frankfurt am Main: P. Lang, 1994.

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5

Scott Jones, Julie. Learn to Test for Multicollinearity in SPSS With Data From the English Health Survey (Teaching Dataset) (2002). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526485793.

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6

Scott Jones, Julie. Learn to Test for Multicollinearity in R With Data From the English Health Survey (Teaching Dataset) (2002). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526498670.

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7

Wiesen, Christopher. Learn About Multicollinearity in SPSS With Data From Transparency, Class Bias, and Redistribution: Evidence From the American States Dataset (2018). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526499349.

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8

Babeshko, Lyudmila, Mihail Bich, and Irina Orlova. Econometrics and econometric modeling. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1141216.

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The textbook covers a wide range of issues related to econometric modeling. Regression models are the core of econometric modeling, so the issues of their evaluation, testing of assumptions, adjustment and verification are given a significant place. Various aspects of multiple regression models are included: multicollinearity, dummy variables, and lag structure of variables. Methods of linearization and estimation of nonlinear models are considered. An apparatus for evaluating systems of simultaneous and apparently unrelated equations is presented. Attention is paid to time series models. Detailed solutions of the examples in Excel and the R software environment are included. Meets the requirements of the federal state educational standards of higher education of the latest generation. For undergraduate and graduate students studying in the field of "Economics", the curriculum of which includes the disciplines "Econometrics"," Econometric Modeling","Econometric research".

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9

Babeshko, Lyudmila, and Irina Orlova. Econometrics and econometric modeling in Excel and R. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1079837.

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The textbook includes topics of modern econometrics, often used in economic research. Some aspects of multiple regression models related to the problem of multicollinearity and models with a discrete dependent variable are considered, including methods for their estimation, analysis, and application. A significant place is given to the analysis of models of one-dimensional and multidimensional time series. Modern ideas about the deterministic and stochastic nature of the trend are considered. Methods of statistical identification of the trend type are studied. Attention is paid to the evaluation, analysis, and practical implementation of Box — Jenkins stationary time series models, as well as multidimensional time series models: vector autoregressive models and vector error correction models. It includes basic econometric models for panel data that have been widely used in recent decades, as well as formal tests for selecting models based on their hierarchical structure. Each section provides examples of evaluating, analyzing, and testing models in the R software environment. Meets the requirements of the Federal state educational standards of higher education of the latest generation. It is addressed to master's students studying in the Field of Economics, the curriculum of which includes the disciplines Econometrics (advanced course)", "Econometric modeling", "Econometric research", and graduate students."

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10

Neeleman, D. Multicollinearity in linear economic models. Springer, 2013.

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11

Neeleman, D. Multicollinearity in Linear Economic Models. Springer London, Limited, 2012.

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12

Farrar, Donald Eugene, and Robert R. Glauber. Multicollinearity in Regression Analysis; The Problem Revisited. Franklin Classics Trade Press, 2018.

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13

Multicollinearity in Regression Analysis; the Problem Revisited. Franklin Classics, 2018.

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14

Parandvash, G. Hossein. On the incorporation of nonnumeric information into the estimation of economic relationships in the presence of multicollinearity. 1987.

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15

Desta, Fekedulegn B., and United States. Forest Service. Northeastern Research Station., eds. Coping with multicollinearity: An example on application of principal components regression in dendroecology. Newtown Square, PA: U.S. Dept. of Agriculture, Forest Service, Northeastern Research Station, 2002.

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16

Robertson, Rob. Effects of collinearity, sample size, multiple correlation, and predictor-criterion correlation salience on the order of variable entry in stepwise regression. 1997.

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17

Hj Jubok, Zainodin, Khuneswari Gopal Pillay, and Noraini Abdullah. Model Building Approach in Multiple Regression. UMS Press, 2018. http://dx.doi.org/10.51200/modelbuildingumspress2018-978-967-2166-14-6.

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This book is the outcome of our three years of research work. The problems faced by the undergraduate students in analysing the data on multiple regressions and the importance of this method had inspired the authors to come up with this book. Regression analysis is commonly used by most researchers in business, social and behavioural sciences, biological sciences and many other fields. But there is no proper procedure or approach of model-building in regression analysis. Therefore, this book is aimed at illustrating the procedures to find the best model and the model-building approach. The model-building approach is important in obtaining the best model that well describes the corresponding data set. This approach can be used in various research fields such as in economics, environmental, biological and medical sciences. The multiple regression model-building approach is applicable in various fields of research. It is very useful for researchers to obtain the best model that well describes the data. The approach is also useful in identifying the factors that will affect the dependent variable. The overall thrusts of the authors’ efforts have been geared in explaining explicitly the statistical method using regression analysis, the model building procedures and its applications, so as to meet the needs of today’s students. A substantial effort has gone in addressing multicollinearity issues and illustrating steps to overcome them. The authors hope that readers will find them helpful.

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