Multicollinearity In Linear Economic Models
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Author | : D. Neeleman |
Publisher | : Springer Science & Business Media |
Total Pages | : 111 |
Release | : 2012-12-06 |
Genre | : Business & Economics |
ISBN | : 9401174865 |
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.
Author | : D. Neeleman |
Publisher | : Springer |
Total Pages | : 103 |
Release | : 1973-07-31 |
Genre | : Business & Economics |
ISBN | : 9789023729105 |
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.
Author | : Dr.M. Chandrasekhar Reddy & Dr.P. Balasubramanyam |
Publisher | : KY Publications |
Total Pages | : 150 |
Release | : 2021-09-01 |
Genre | : Business & Economics |
ISBN | : 8194807549 |
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.
Author | : Roberto Pedace |
Publisher | : John Wiley & Sons |
Total Pages | : 380 |
Release | : 2013-06-05 |
Genre | : Business & Economics |
ISBN | : 1118533879 |
Score your highest in econometrics? Easy. Econometrics can prove challenging for many students unfamiliar with the terms and concepts discussed in a typical econometrics course. Econometrics For Dummies eliminates that confusion with easy-to-understand explanations of important topics in the study of economics. Econometrics For Dummies breaks down this complex subject and provides you with an easy-to-follow course supplement to further refine your understanding of how econometrics works and how it can be applied in real-world situations. An excellent resource for anyone participating in a college or graduate level econometrics course Provides you with an easy-to-follow introduction to the techniques and applications of econometrics Helps you score high on exam day If you're seeking a degree in economics and looking for a plain-English guide to this often-intimidating course, Econometrics For Dummies has you covered.
Author | : Klaus Winckler |
Publisher | : |
Total Pages | : 96 |
Release | : 1969 |
Genre | : |
ISBN | : |
Author | : Alvin C. Rencher |
Publisher | : John Wiley & Sons |
Total Pages | : 690 |
Release | : 2008-01-07 |
Genre | : Mathematics |
ISBN | : 0470192607 |
The essential introduction to the theory and application of linear models—now in a valuable new edition Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed. Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. This modern Second Edition features: New chapters on Bayesian linear models as well as random and mixed linear models Expanded discussion of two-way models with empty cells Additional sections on the geometry of least squares Updated coverage of simultaneous inference The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS® code for all numerical examples. Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance.
Author | : James Kenkel |
Publisher | : Routledge |
Total Pages | : 401 |
Release | : 2018-04-09 |
Genre | : Business & Economics |
ISBN | : 135114071X |
Originally published in 1974. This book provides a rigorous and detailed introductory treatment of the theory of difference equations and their applications in the construction and analysis of dynamic economic models. It explains the theory of linear difference equations and various types of dynamic economic models are then analysed. Including plenty of examples of application throughout the text, it will be of use to those working in macroeconomics and econometrics.
Author | : J. Holton Wilson |
Publisher | : Business Expert Press |
Total Pages | : 144 |
Release | : 2012-08-02 |
Genre | : Business & Economics |
ISBN | : 160649435X |
The technique of regression analysis is used so often in business and economics today that an understanding of its use is necessary for almost everyone engaged in the field. This book will teach you the essential elements of building and understanding regression models in a business/economic context in an intuitive manner. The authors take a non-theoretical treatment that is accessible even if you have a limited statistical background. It is specifically designed to teach the correct use of regression, while advising you of its limitations and teaching about common pitfalls. This book describes exactly how regression models are developed and evaluated —where real data is used, instead of contrived textbook-like problems. Completing this book will allow you to understand and build basic business/economic models using regression analysis. You will be able to interpret the output of those models and you will be able to evaluate the models for accuracy and shortcomings. Even if you never build a model yourself, at some point in your career it is likely that you will find it necessary to interpret one; this book will make that possible. Included are instructions for using Microsoft Excel to build business/economic models using regression analysis with an appendix using screen shots and step-by-step instructions.
Author | : Michael H. Kutner |
Publisher | : McGraw-Hill/Irwin |
Total Pages | : 1396 |
Release | : 2005 |
Genre | : Mathematics |
ISBN | : 9780072386882 |
Linear regression with one predictor variable; Inferences in regression and correlation analysis; Diagnosticis and remedial measures; Simultaneous inferences and other topics in regression analysis; Matrix approach to simple linear regression analysis; Multiple linear regression; Nonlinear regression; Design and analysis of single-factor studies; Multi-factor studies; Specialized study designs.
Author | : Michael D. Intriligator |
Publisher | : Prentice Hall |
Total Pages | : 662 |
Release | : 1978 |
Genre | : Business & Economics |
ISBN | : |
Models and data; Single-equation estimation; Applications of single-equation estimation; Simultaneous equations; Applications of simultaneous-equations estimation; The uses of econometrics.