1. Applied Econometrics Second edition
Dimitrios Asteriou and Stephen G. Hall

2. Applied Econometrics
SIMPLE REGRESSION
1. Introduction to the Classical Linear Regression Model 2. The OLS Method of Estimation 3. The Overall Goodness of Fit 4. Hypothesis Testing 5. How to Estimate a Simple Regression in Eviews 6. Applications and Examples

3. Learning Objectives
Compute the equation of a simple regression line from a sample of data, and interpret the slope and intercept of the equation.
A full understanding of the simple OLS method of estimation and discussion of the properties of estimated coefficients.
Computation of a standard error of the estimate and interpretation of its meaning and its use in Hypothesis Testing.
Understanding and interpretation of the R2

4. Introduction
Regression analysis is the process of constructing a mathematical model or function that can be used to predict or determine one variable by another variable.
Key issue here is direction of causation of the two variables, or which variable depends on the other.
Therefore we have two cases of variables
dependent variables (usually denoted by Y)
independent or explanatory (usually denoted by X)

5. The Scatter Plot

6. Four Ways of Fitting a Line in the Data
By eye
Connecting the first with the last observation
Take the average of the first two and the average of the two last and connect
Apply Ordinary Least Squares

7. Regression Models Deterministic Regression Model: Y=0+1X
Probabilistic Regression Model:
Y=0+1X+u
0 and 1 are population parameters
0 and 1 are estimated by sample statistics b0 and b1

8. Equation of the Regression Line

9. Slope and Intercept of the Regression Line

10. Least Squares Analysis

11. Example: The Keynesian Consumption Function

12. Example: The Keynesian Consumption Function
C2=B2*A2
D2=B2*B2
A22=SUM(A2:A21)
B22=SUM(B2:B21)
and so on!

13. Excel Calculations
b0=(C22-(A22*B22)/20)/(D22-((B22ˆ2)/20))=
b1=AVERAGE(A2:A21)-G2*AVERAGE(B2:B21)=

14. Excel Calculations (the easy way!)
Step 1: go to the menu Tools/Data Analysis and choose the command regression.
Step 2: We are then asked to specify the Input Range, Output Range, and a choice of including or not labels in the first row.
Step 3: The Input Range is the columns that contain the data for Y and X
(i.e. we enter ‘$A$1:$B$21’ or simply select this area using the mouse).
Step 4: The Output Range can be either a different sheet (not recommended) or any empty cell in the current sheet (i.e. we might specify cell F5).
Step 5: Since we have chosen the labels in our selection we tick the box.
Step 6: By clicking <OK> we obtain the display shown in Table 4.4.

15. Excel Results
Applied Econometrics: A Modern Approach using Eviews and Microfit © Dr D Asteriou

16. The Regression Line

17. The Coefficient of Determination
The proportion of variability of the dependent variable accounted for or explained by the independent variable in a regression model.
It is called R2 and it takes values from 0-1.

18. Hypothesis Tests for the Slope of the Regression Model

19. Regression in Eviews (1) (1)
Step 1 Open EViews.
Step 2 Choose File/New/Workfile in order to create a new file.
Step 3 Choose Undated or Irregular and specify the number of observations (in this case 20). A new window appears which automatically contains a constant (c) and a residual (resid) series.

20. Regression in EViews (2)
Step 4 In the command line type:
genr x=0 (press enter)
genr y=0 (press enter)
which creates two new series named x and y that contain zeros for every observation.
Open x and y as a group by selecting them and double clicking with the mouse.
Step 5 Either type the data in EViews or copy/paste the data from Excel®. To edit the series press the edit +/− button. After finishing with editing the series press the edit +/− button again to lock or secure the data.