National open university of nigeria introduction to econometrics I eco 355

 
 
4.0 
CONCLUSION 
Classical linear regression model statistical-tool used in predicting future values of a 
target (dependent) variable on the basis of the behavior of a set of explanatory factors
(independent variables). A type of regression analysis model, it assumes the target 
variable is predictable, not chaotic or random. 
5.0 
SUMMARY 
In this unit, we have vividly discuss the classical linear regression model and its
assumptions and various calculation of 
̂
. So i think from this unit, you should be able to
work through the calculation of CLRM and the assumptions. 
6.0 
Tutor Marked Assessment Exercise
. 
1. 
Discuss extensively the assumption of classical linear regressions model. 
7.0 
REFERENCES/Further Reading 
Dimitrios, A & Stephen, G., (2011). Applied Econometrics, second edition 2011, first
edition 2006 and revised edition 2007. 
 

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UNIT 4
PROPERTIES OF THE ORDINARY LEAST SQUARE
ESTIMATORS
 
CONTENTS 
 
1.0.
Introduction 
2.0.
Objectives 
3.0.
Main content 
3.1. Properties of the OLS Estimators 
4.0 
Conclusion 
5.0 
Summary 
6.0
Tutor-Marked Assignment 
7.0 
References/Further Readings 
 
 
 
 
1.0 
INTRODUCTION 
The ordinary least square (OLS) estimator is consistent when the regressors are 
exogenous and there is no perfect multicollinearity, and optimal in the class of linear 
unbiased estimators when the errors are homoscedastic and serially uncorrelated. Under 
these conditions, the method of OLS provides minimum-variance mean-unbiased 
estimation when the errors have finite variances. Under the additional assumption that the 
errors be normally distributed, OLS is the maximum likelihood estimator. OLS is used in 
economics (econometrics), political science and electrical engineering (control theory and 
signal processing), among many areas of application. The Multi-fractional order 
estimator is an expanded version of OLS. 

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