National open university of nigeria introduction to econometrics II eco 356



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Introduction to Econometrics ECO 356 Course Guide and Course Material
Introduction to Econometrics ECO 356 Course Guide and Course Material
UNIT
3
MULTIPLE
REGRESSION
ANALYSIS
AND
MULTICOLLINEARITY
CONTENTS
2.3.1.0 Introduction
2.3.2.0 Objectives
2.3.3.0 Main Content
2.3.3.1 Multiple Regression Coefficients Interpretation
2.3.3.2 Properties of the Multiple Regression Coefficients
2.3.3.3 t Tests and Confidence Intervals
2.3.3.4 Consistency
2.3.4.0 Multicollinearity
2.3.4.1 Multicollinearity in Models with More Than Two Explanatory Variables
2.3.4.2 Ways to alleviate multicollinearity problems
2.3.5.0 Summary


INTRODUCTION TO ECONOMETRICS II

ECO 306

NOUN
74 2.3.6.0 Conclusion
2.3.7.0 Tutor-Marked Assignment
2.3.8.0 References/Further Reading
2.3.1.0 INTRODUCTION
The multiple regression analysis is an extension of simple regression analysis. It covers cases in which the dependent variable is hypothesized to depend on more than one descriptive variable. Most of the multiple regression analysis is a direct extension of the simple regression model but hasonly two new dimensions. First, when evaluating the influence of a given descriptive variable on the dependent variable, we would now have to face the problem of discriminating between its effects and the effects of the other descriptive variables. Second, we shall have to tackle the problem of model specification. Often some variables might bethought to influence the behaviour of the dependent variable though, they might be unconnected. We shall have to decide which should be included in the regression equation and which should be omitted. However, the arrangement of flow for the multiple regression analysis is to firstly, carryout derivation of formula, then estimation procedures using values, followed by presentation of results and lastly interpretations. As an extension of unit
2, we shall discuss multicollinearity being a problem associated with CLRM.

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