MULTICOLLINEARITY IN ECONOMETRIC MODELS

MULTICOLLINEARITY IN ECONOMETRIC MODELS
Author: Dr.M. Chandrasekhar Reddy & Dr.P. Balasubramanyam
Publisher: KY Publications
Total Pages: 150
Release: 2021-09-01
Genre: Business & Economics
ISBN: 8194807549

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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.

Multicollinearity in linear economic models

Multicollinearity in linear economic models
Author: D. Neeleman
Publisher: Springer Science & Business Media
Total Pages: 111
Release: 2012-12-06
Genre: Business & Economics
ISBN: 9401174865

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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.

Econometrics For Dummies

Econometrics For Dummies
Author: Roberto Pedace
Publisher: John Wiley & Sons
Total Pages: 380
Release: 2013-06-05
Genre: Business & Economics
ISBN: 1118533879

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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.

Multicollinearity in linear economic models

Multicollinearity in linear economic models
Author: D. Neeleman
Publisher: Springer
Total Pages: 103
Release: 1973-07-31
Genre: Business & Economics
ISBN: 9789023729105

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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.

Multicollinearity and Biased Estimation

Multicollinearity and Biased Estimation
Author: Josef Gruber
Publisher: Vandehoeck & Rupprecht
Total Pages: 156
Release: 1984
Genre: Business & Economics
ISBN:

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Regression and factor analysis applied in econometrics

Regression and factor analysis applied in econometrics
Author: J.H.F. Schilderinck
Publisher: Springer Science & Business Media
Total Pages: 247
Release: 2012-12-06
Genre: Business & Economics
ISBN: 1461340519

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This book deals with the methods and practical uses of regression and factor analysis. An exposition is given of ordinary, generalized, two- and three-stage estimates for regression analysis, the method of principal components being applied for factor analysis. When establishing an econometric model, the two ways of analysis complement each other. The model was realized as part of the 'Interplay' research project concerning the economies of the European Common Market countries at the Econometrics Department of the Tilburg School of Economics. The Interplay project aims at: a. elaborating more or less uniformly defined and estimated models; b. clarifying the economic structure and the economic policy possible with the linked models of the European Community countries. Besides the model for the Netherlands published here, the models for Belgium, Italy, West Germany and the United Kingdom are ready for linking and for publishing later on. The econometric model presented in this book and upon which the Interplay model is based comprises eleven structural and twenty-one definitional equations; it is estimated with ordinary, two- and three-stage least squares. The analysis of the model is directed at eliminating multicollinearity, accor ding to D.E. Farrar's and R. Glauber's method. In practice, however, complete elimination of multicollinearity leads to an exclusion of certain relations which is not entirely satisfactory. Economic relations can be dealt with more fully by analyzing the variables involved in detail by factor analysis. In this study factor analysis is also a suitable method for a comparative analysis of different periods.

Econometric Models, Techniques, and Applications

Econometric Models, Techniques, and Applications
Author: Michael D. Intriligator
Publisher: Prentice Hall
Total Pages: 662
Release: 1978
Genre: Business & Economics
ISBN:

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Models and data; Single-equation estimation; Applications of single-equation estimation; Simultaneous equations; Applications of simultaneous-equations estimation; The uses of econometrics.

Multicollinearity in Regression Analysis

Multicollinearity in Regression Analysis
Author: Donald Eugene Farrar
Publisher: Createspace Independent Publishing Platform
Total Pages: 96
Release: 2017-08-08
Genre:
ISBN: 9781974353095

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To most economists the single equation least squares regression model, like an old friend, is tried and true. Its properties and limitations have been extensively studied, documented and are, for the most part, well known. Any good text in econometrics can lay out the assumptions on which the model is based and provide a reasonably coherent -- perhaps even a lucid --discussion of problems that arise as particular assumptions are violated. A short bibliography of definitive papers on such classical problems as non-normality, heteroscedasticity, serial correlation, feedback, etc., completes the job.

Basic Econometrics

Basic Econometrics
Author: Dr.M.Chitra
Publisher: Shanlax Publications
Total Pages: 115
Release:
Genre: Business & Economics
ISBN: 9395422769

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This book is a guide for students who are studying econometrics as a course in their programme, There are 5 modules or units in this study material. The first unit explicate from the origin of econometrics, meaning, definition, need econometrics as a separate discipline, the scope of econometrics, Methodology of Econometrics, Reasons for introduction of stochastic error term, the difference between economic and econometric model and limitation. The second unit depicts about the simple linear regression in the aspects of its assumptions, derivations of its estimation of parameter value, properties and its technical note, applications of simple linear regression with examples. The third unit is the extension of simple linear regression as multiple regression with basic input with examples for applying in reality. The fourth unit briefly explains the violations of assumptions such as multicollinearity, homesecdasticity, autocorrelation, and specification errors in the aspects of causes, consequences, way of diagnostic the presence or absence and the remedies to rectify the issues. The fifth module or unit introduces about the qualitative response models with dummies, distributed lag models with importance’s of lag, analysis of variance, analysis of covariance, comparison of analysis of variance and analysis of covariance. The last unit attempted to explain about the free open source software Gretel to apply in need of finding solution to an economic phenomenon. The Glossary is attached in the annexure for a better understanding the terminology of econometrics which will support to face multiple choice questions of any competitive examinations in national and state level. In Simple words, this book is a guide to get the knowledge of econometrics and apply the same into reality wherever necessary.

Econometric Models, Techniques, and Applications

Econometric Models, Techniques, and Applications
Author: Michael D. Intriligator
Publisher: Pearson
Total Pages: 684
Release: 1996
Genre: Business & Economics
ISBN:

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This book surveys the theories, techniques (model- building and data collection), and applications of econometrics. KEY TOPICS: It focuses on those aspects of econometrics that are of major importance to readers and researchers interested in performing, evaluating, or understanding econometric studies in a variety of areas. It reviews matrix notation and the use of multivariate statistics; discusses the specification of the model and the development of data for its estimation; covers recent developments in econometric models, techniques, and applications; explains the estimation of single-equation models; and provides case studies of the applications of econometrics to a wide array of areas -- including traditional areas such as the estimation of demand functions and production functions, and macroeconometric models.