INTRODUCTION TO EVIEWS: OLS ESTIMATION AND INTERPRETATION
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E-Views is an application that is used for regression analysis (econometrics).
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Before you can use the app you must have constructed your desired model and get the right
data for your variable from the right sources
How to open a work file on E-Views
Know that creating a work file is the first thing to do before you can carry out any activity on the EViews. Steps involved:
File—new ---- work file
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Choose your frequency(default is annual)
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Input the start and end dates of the data you have collected
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Note; c-constant resid-error term
How to import data
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Make sure you have an open excel worksheet with your data
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Copy the data from excel; do not copy the years
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Go back to the E-Views
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Go to quick --- empty group(edit series)
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Always scroll up to the very first row then paste
Generating your equation
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Go to your work file---- click on the dependent variable first( anyone variable you click first is
assumed to be the dependent variable, so make sure you click your dependent variable first).
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When you do that, click on Ctrl press it down while you click on the other variables
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On the highlighted part right click then click on open----- as equation
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Do not click on the constant or the residual
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Before estimating your residual is unobservable, it is quiet till you estimate.
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The diagram below should look like your result
Dependent Variable: RGDP
Method: Least Squares
Date: 02/23/18 Time: 01:51
Sample: 1970 2011
Included observations: 42
Variable
Coefficient
Std. Error
t-Statistic
Prob.
OILP
INTR
GFCF
EXR
C
5087.663
7518.312
-0.591644
1467.617
-30153.42
680.2053
1681.375
0.348262
275.0485
38931.17
7.479599
4.471527
-1.698848
5.335846
-0.774532
0.0000
0.0001
0.0977
0.0000
0.4435
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.941973
0.935700
57951.66
1.24E+11
-517.5629
150.1585
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
281644.8
228538.7
24.88395
25.09081
24.95977
1.530247
Checking if your variables are significant
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After estimating your equation, the question is – is the result significant?
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You could use the p- value or the t- statistic
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If the absolute value of your t- stat is greater than two, your variable is statically significant
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If your p-value is less than 0.05, then it is statistically significant
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N.B; if more than 50% of your variables (exclude the constant) are insignificant then there
is a problem
Your summary Analyses
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T-stat is used for statistical significance, while the signs economic significance.
Normally we focus on 3 things;
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R squared and adjusted R squared
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Durbin- watson stats
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Probability of F-statistics
R-squared
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This measures the goodness of fit, it measures how much of the changes in your dependent
variable can be jointly explained by your explanatory variables.
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It measures how well your estimated regression line fits the data
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On the summary if r- square is greater than 0.6 then your estimated regression line is a good
fit vice-versa
F-stat
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This measures the joint significance of your explanatory variables
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If the probability of the f- stat is less than 0.05 then your explanatory variables are jointly
statistically significant
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For the Durbin- Watson stat, if it is less than 1.8 (which is very close to 2) or greater than 2,
then there might be a presence of positive and negative autocorrelation respectively, but if it
is 2 or approx. 2 (between 1.8 and 2) then it is fine
PRACTICE
Now let’s practice our analysis on the estimation output displayed above.
Economic significance:
OILP is economically significant because the positive sign that the coefficient takes agrees with a priori
expectation of there being a positive relationship between oil price and real GDP.
INTR is economically insignificant because its coefficient has a positive sign and this goes against the
a priori expectation that there is a negative relationship between real GDP and interest rate.
GFCF (gross fixed capital formation) is economically insignificant because its coefficient has a negative
sign which goes the a priori expectation of there being a positive relationship between capital stock
and real GDP.
EXR is economically significant because it is positively signed and therefore it is in agreement with the
a priori expectation is that there is a positive relationship between exchange rate and real GDP; as
exchange rate increases, exports become cheaper and hence the demand for them increases causing
GDP to rise ceteris paribus.
Statistical significance:
OILP is statistically significant because the p- value is less than 0.05 (absolute value of t-stat is greater
than 2)
INTR is statistically significant because the p-value is less than 0.05 (absolute value oft-stat is greater
than 2)
GFCF is statistically insignificant because the p-value is greater than 0.05 (absolute value of t-stat is
less than 2)
EXR is statistically significant because the p-value is less than 0.05 (absolute value of t-stat is greater
than 2)
Finally, since 75% of the explanatory variables are statistically significant (that is higher than 50%) we
can move on with our data analysis.
Summary statistics
The estimated regression line is a very good fit for the data with an R squared and adjusted R squared
of 94.12% and 93.57% respectively.
The explanatory variables are jointly significant at 150.16 because the probability of F-stat is less than
0.05.
Finally, from the Durbin- Watson statistic, there is an indication of positive autocorrelation because
the value is less than 1.8.