1.Normality test,Stationary test,3.Multicorlinearity

2. DIAGNOSTIC TESTS

3. Types of Diagnostic Test

4. Normality
Normality is a condition in which the used
variables follow the standard normal
distribution.

5. Normality Test
Histogram of residuals
Normal probability plot (NPP)
 The Jarque – Bera test

6. The Jarque Bera Test
The JB test of normality is a large-sample test. It is
also based on the OLS residuals.
We make hypothesis in this test.

7. How study Result of JB
If the computed value of P > 0.05, then we
said that residuals are normally distributed.
If the computed value of P < 0.05, then we said that
residuals are not normally distributed.

8. Example
Variable ( LINV )
H0 : Residuals are normally distributed
H1 : Residuals are not normally distributed
Critical Region: 0.05
Results: p = 0.99
0.99 > 0.05
Conclusion : Ho Accepted

9. 10
8
6
4
2
0
0.7 0.8 0.9 1.0 1.1 1.2
Series: LINV
Sample 1979 2010
Observations 32
Mean 0.969416
Median 0.968722
Maximum 1.196225
Minimum 0.699115
Std. Dev. 0.115272
Skewness 0.013718
Kurtosis 3.081233
Jarque-Bera 0.009802
Probability 0.995111

10. Stationarity
Time series Yt is said to be stationary,
if its mean, its variance and its covariance
remain constant over time.

11. Stationarity Tests
 Graphical Analysis
 Autocorrelation Function (ACF)
 Differencing
 The Unit Root Test
a) Dickey Fuller Test (DF)
b) Augmented Dickey Fuller Test (ADF)
c) Phillips Perron Test

12. Graphical Analysis

13. Differencing
If we wants to remove the trend component
from a (time) series entirely to render it
stationary.
we need to apply differencing, i.e. compute
absolute changes from one period to the next.

14. Symbolically
First order Differencing
ΔYt =Yt – Yt -1
If differenced series still exhibits a trend, it needs to be differenced again
(one or more times) to render it stationary. Thus we have
Second order Differencing

15. Multicolinearity
Multicolinearity is the undesirable situation where the
correlations among the independent variables are
strong. We have perfect muticolinearity if , the value of
correlation of two independent variables is between +1
to -1

16. Example

17. Interpretation of Result
1.0 Perfect Multicolinearity
above 0.5 Strong Multicolinearity
below 0.5 No Multicolinearity

18. REFERENCES
 Applied Econometrics
by Asteriou and Stephen
Basic Econometrics
by Damodar N. Gujarati
Normality Tests for Statistical Analysis: A Guide
for Non-Statisticians
by Ghasemi , Zahediasl