Introduction to econometrics II eco 356 faculty of social sciences course guide course Developers: Dr. Adesina-Uthman
Download 1.75 Mb. View original pdf
|
- Navigate this page:
- INTRODUCTION TO ECONOMETRICS II ECO 306 NOUN 42 random factor known as the disturbance term
| 2.1.2.0 OBJECTIVE The main objective of this unit is to acquaint students with the rudiments of identifying and differentiating simple equation model from multiple regression. 2.1.3.0 MAIN CONTENTS 2.1.3.1 Simple Regression Analyses The correlation coefficient may indicate that two variables (bivariate regression model) are associated with one another, but it does not give any idea of the kind of relationship involved. While regression predicts the value of the dependent variable based on the known value of the independent variable. In this module further step is taken for cases which we are willing to hypothesize on, than one variable dependence on another. It must be statedimmediately that one would not expect to find an exact relationship between any two economic variables unless it is true as a matter of definition. In textbook expositions of economic theory, the usual way of dealing with this awkward fact is to write down the relationship as if it were exact and to warn the reader that it is only an approximation. However, in statistical analysis, one acknowledges the fact that the relationship is not exact by explicitly including in it a INTRODUCTION TO ECONOMETRICS II ECO 306 NOUN 42 random factor known as the disturbance term. We shall start with the simplest possible model ...[2.01] , the value of the dependent variable in observation i, has two components (1) the nonrandom (deterministic term) component, being described as the explanatory (or independent/descriptive) variable and the fixed quantities and as the parameters of the equation, and (2) the disturbance (stochastic term, Figure 2.0 illustrates how these two components combine to determine Y. X 1 , X 2 , X 3 , and X 4 , which are four hypothetical values of the explanatory variable. If the relationship between Y and X were exact, the corresponding values of Y would be represented by the points Q 1 – Q 4 on the line. The disturbance term causes the actual values of Y to be different. In the diagram, the disturbance term has been assumed to be positive in the first and fourth observations and negative in the other two, with the result that, if one plots the actual values of Y against the values of X, one obtains the points P 1 – P 4 Figure Illustration of independent component combination to give a dependent variable In practice, the P points are all not what can be seen in Figure 2.0. The actual values of and and hence the location of the Q points, are unknown, as these are the values of the disturbance term in the observations. The task of regression analysis is to |