Download or read book MULTICOLLINEARITY IN ECONOMETRIC MODELS written by Dr.M. Chandrasekhar Reddy & Dr.P. Balasubramanyam and published by KY Publications. This book was released on 2021-09-01 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are several textbooks are available in literature in Econometrics, but we thought it is really beneficial to students and researchers to have a special textbook on multicollinearity problem in the general linear model. The topic of multicollinearity has gained high importance in recent times as the data getting generated is increased enormously. Because of this data exploration, many variables are representing the same amount of information which leads to the problem of multicollinearity. In the current textbook, the authors tried to explore the topic of multicollinearity along with the basic definitions and key tests available to detect multicollinearity. For all practical application purposes, we included a chapter on empirical analysis that will show how the model goes improved through dealing with the problem of multicollinearity. This book acts as a textbook, reference manual for all students who are studying econometrics at their graduate and post-graduate levels and also for research scholars. The design of contents is structured in such a way that users find it easy to understand and implement the same in their research works.

Download or read book Coping with Multicollinearity written by B. Desta Fekedulegn and published by . This book was released on 2002 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multicollinearity in linear economic models written by D. Neeleman and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: It was R. Frisch, who in his publications 'Correlation and Scatter Analysis in Statistical Variables' (1929) and 'Statistical Confluence Analysis by means of Complete Regression Systems' (1934) first pointed out the complications that arise if one applies regression analysis to variables among which several independent linear relations exist. Should these relationships be exact, then there exist two closely related solutions for this problem, viz. 1. The estimation of 'stable' linear combinations of coefficients, the so-called estimable functions. 2. The dropping of the wen-known condition of unbiasedness of the estimators. This leads to minimum variance minimum bias estimators. This last solution is generalised in this book for the case of a model consisting of several equations. In econometrics however, the relations among variables are nearly always approximately linear so that one cannot apply one of the solutions mentioned above, because in that case the matrices used in these methods are, although ill-conditioned, always of full rank. Approximating these matrices by good-conditioned ones of the desired rank, it is possible to apply these estimation methods. In order to get an insight in the consequences of this approximation a simulation study has been carried out for a two-equation model. Two Stage Least Squares estimators and estimators found with the aid of the above mentioned estimation method have been compared. The results of this study seem to be favourable for this new method.

Download or read book Ridge Fuzzy Regression Modelling for Solving Multicollinearity written by Hyoshin Kim and published by Infinite Study. This book was released on with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper proposes an a-level estimation algorithm for ridge fuzzy regression modeling, addressing the multicollinearity phenomenon in the fuzzy linear regression setting.

Download or read book Applied Multivariate Research written by Lawrence S. Meyers and published by SAGE. This book was released on 2006 with total page 768 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multivariate designs were once the province of the very few exalted researchers who understood the underlying advanced mathematics. Today, through the sophistication of statistical software packages such as SPSS, virtually all graduate students across the social and behavioural sciences are exposed to the complex multivariate statistical techniques without having to learn the mathematical computations needed to acquire the data output. These students - in psychology, education, political science, etc. - will never be statisticians and appropriately so, their preparation and coursework reflects less of an emphasis on the mathematical complexities of multivariate statistics and more on the analysis and the interpretation of the methods themselves and the actual data output. This book provides full coverage of the wide range of multivariate topics in a conceptual, rather than mathematical, approach. The author gears toward the needs, level of sophistication, and interest in multivariate methodology of students in applied areas that need to focus on design and interpretation rather than the intricacies of specific computations. The book includes: - Coverage of the most widely used multivariate designs: multiple regression, exploratory factor analysis, MANOVA, and structural equation modeling. - Integrated SPSS examples for hands-on learning from one large study (for consistency of application throughout the text). - Examples of written results to enable students to learn how the results of these procedures are communicated. - Practical application of the techniques using contemporary studies that will resonate with students.

Download or read book Partial Least Squares Path Modeling written by Hengky Latan and published by Springer Nature. This book was released on 2023-12-16 with total page 495 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its second edition, this edited book presents recent progress and techniques in partial least squares path modeling (PLS-PM), and provides a comprehensive overview of the current state-of-the-art in PLS-PM research. Like the previous edition, the book is divided into three parts: the first part emphasizes the basic concepts and extensions of the PLS-PM method; the second part discusses the methodological issues that have been the focus of recent developments, and the last part deals with real-world applications of the PLS-PM method in various disciplines. This new edition broadens the scope of the first edition and consists of entirely new original contributions, again written by expert authors in the field, on a wide range of topics, including: how to perform quantile composite path modeling with R; the rationale and justification for using PLS-PM in top-tier journals; psychometric properties of three weighting schemes and why PLS-PM is a better fit to mode B; a comprehensive review of PLS software; how to perform out-of-sample predictions with ordinal consistent partial least squares; multicollinearity issues in PLS-PM using ridge regression; theorizing and testing specific indirect effects in PLS and considering their effect size; how to run hierarchical models and available approaches; and how to apply necessary condition analysis (NCA) in PLS-PM. This book will appeal to researchers interested in the latest advances in PLS-PM as well as masters and Ph.D. students in a variety of disciplines who use PLS-PM methods. With clear guidelines on selecting and using PLS-PM, especially those related to composite models, readers will be brought up to date on recent debates in the field.

Download or read book Introductory Econometrics for Finance written by Chris Brooks and published by Cambridge University Press. This book was released on 2008-05-22 with total page 672 pages. Available in PDF, EPUB and Kindle. Book excerpt: This best-selling introduction to econometrics is specifically written for finance students. The new edition builds on the successful data- and problem-driven approach of the first edition, giving students the skills to estimate and interpret models while developing an intuitive grasp of underlying theoretical concepts.

Download or read book Selecting Models from Data written by P. Cheeseman and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 475 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume is a selection of papers presented at the Fourth International Workshop on Artificial Intelligence and Statistics held in January 1993. These biennial workshops have succeeded in bringing together researchers from Artificial Intelligence and from Statistics to discuss problems of mutual interest. The exchange has broadened research in both fields and has strongly encour aged interdisciplinary work. The theme ofthe 1993 AI and Statistics workshop was: "Selecting Models from Data". The papers in this volume attest to the diversity of approaches to model selection and to the ubiquity of the problem. Both statistics and artificial intelligence have independently developed approaches to model selection and the corresponding algorithms to implement them. But as these papers make clear, there is a high degree of overlap between the different approaches. In particular, there is agreement that the fundamental problem is the avoidence of "overfitting"-Le., where a model fits the given data very closely, but is a poor predictor for new data; in other words, the model has partly fitted the "noise" in the original data.

Download or read book Multicollinearity and Biased Estimation written by Josef Gruber and published by Vandehoeck & Rupprecht. This book was released on 1984 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multiple Regression in Practice written by William Dale Berry and published by SAGE. This book was released on 1985-05 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: The authors provide a systematic treatment of the major problems involved in using regression analysis. They clearly and concisely discuss the consequences of violating the assumptions of the regression model, procedures for detecting violations, and strategies for dealing with these problems.

Download or read book On a Neglected Measure of Multicollinearity written by P. A. V. B. Swamy and published by . This book was released on 1985 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multicollinearity in Municipal Bond Models written by Earl Ray Wilson and published by . This book was released on 1984 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book MULCAN MOD II an Interactive Basic Program for Multicollinearity Analysis written by Néstor E. González Q. and published by . This book was released on 1973 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multicollinearity and the Choice of Estimator Under Squared Error Loss written by George G. Judge and published by . This book was released on 1973 with total page 58 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Efficient inference in random coefficient models with multicollinearity in the time series regressions written by Robert K. Rayner, Roger L. Wright and published by . This book was released on 1980 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Generalized Multicollinearity Index for Estimation written by P. A. V. B. Swamy and published by . This book was released on 1981 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Understanding Regression Analysis written by Michael Patrick Allen and published by Springer Science & Business Media. This book was released on 2007-11-23 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: By assuming it is possible to understand regression analysis without fully comprehending all its underlying proofs and theories, this introduction to the widely used statistical technique is accessible to readers who may have only a rudimentary knowledge of mathematics. Chapters discuss: -descriptive statistics using vector notation and the components of a simple regression model; -the logic of sampling distributions and simple hypothesis testing; -the basic operations of matrix algebra and the properties of the multiple regression model; -testing compound hypotheses and the application of the regression model to the analyses of variance and covariance, and -structural equation models and influence statistics.