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3.Multicorlinearity

1.Normality test,Stationary test,3.Multicorlinearity

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- 1. Applied economics
- 2. Diagnostic test 1.Norm ality test 2.Stationary test 3.M ulticorlinearity
- 3. Diagnostic Test:- Diagnostic Tests carried out on the data included the normality test, stationary test, Multicolinearity test, serial correlation test and the structural stability test.
- 4. Normality Test:- Normality is a condition in which the used variables follow the standard normal distribution. A normally distributed data set has a probability density. Test use for normality:- Jarque-Bera test of normality .this test first computes the skewness(s) and kurtosis(k) and uses the following statistics; JB = N [S2 /6 + (K-3)2 /24] The Jarque-Bera test is based on the sample skewness and sample kurtosis. with S, K, and N denoting the sample skewness, the sample kurtosis, and the sample size, respectively.
- 5. Method 0f Normality test:- Check the series normal distribute we apply Jarque-bera test The following Steps are adopted in E-view Step no.1 Open E-view create a new work file and open any data file like (1979-2010) Paste the question in empty group. Step 2 Go to option view (where paste the data) and select Descriptive stat than common sample and press ok. We will get the result.
- 6. Table show the result that data is normally distribute or not LPGDP LCPI LINV Mean 4.146104 1.781789 0.969416 Median 4.145150 1.801283 0.968722 Maximum 4.961491 2.352240 1.196225 Minimum 3.392548 1.238794 0.699115 Std. Dev. 0.455947 0.319540 0.115272 Skewness 0.082936 0.001676 0.013718 Kurtosis 1.840198 1.810464 3.081233 Jarque-Bera 1.830206 1.886676 0.009802 Probability 0.400475 0.389326 0.995111 Sum 132.6753 57.01725 31.02132 Sum Sq. Dev. 6.444524 3.165285 0.411919 Observations 32 32 32
- 7. Make hypothesis Ho=series is normal distributed. H1=series is not normal distributed. NOTE: If probability of normative statistic is less than 0.05, it is not normal distribution. If probability of normative statistic is greater than 0.05, it is normal distribution. Like (p ≤ 0.05), (p ≥ 0.05) SO:-we accepted the Ho that shows lpgdp , lcpi., linv,is normally distributed.
- 8. Stationarity Test Definition 1:- stationary process reverts around a contsant long –term mean and has a constant variance independent of time. Definition 2:- A stationary process has the property that the mean , variance and auto-correlation structure don’t change over the time. The stationary series :- If a time-series is stationary its mean, variance and auto co-variance remains the same no matter what point we measure them i.e. they are time invariant.
- 9. Test used for Stationary Unit-root test:- Standard inference procedures do not apply to regressions which contain an integrated dependent variable or integrated regressors. Therefore, it is important to check whether a series is stationary or not before using it in a regression. The formal method to test the Stationarity of a series is the unit root test. 1:The Dickey-Fuller (DF) Test 2:The Augmented Dickey-Fuller Test (ADF) 3:The Phillips-Peron (PP) Test
- 10. Non-stationary Definition :- A non stationary time series will have a time-varying mean and time-varying variance or both. Example: Non-stationary behavior can be trends , cycles, random walks.
- 11. Check the series is Stationary or Non- Stationary The following steps are adopted in Eviews. Step 1: Open Eviews and create a new work file . Go to quick option select empty group. Open any data file like(male , female, salary and spending). And paste in empty group in Eviews. Step 2 : Reopen back file and select any variable and double click on it and see new file. Go to view of this file click the unit-root test press OK. Step 3: New window will open and select level and also trend & intercept press
- 12. Check the Results Null Hypothesis: FEMALES has a unit root Exogenous: Constant, Linear Trend Lag Length: 0 (Automatic based on SIC, MAXLAG=5) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -0.403705 0.9809 Test critical values: 1% level -4.416345 5% level -3.622033 10% level -3.248592 *MacKinnon (1996) one-sided p-values.
- 13. Continue Augmented Dickey-Fuller Test Equation Dependent Variable: D(FEMALES) Method: Least Squares Date: 05/24/14 Time: 14:06 Sample (adjusted): 1968 1990 Included observations: 23 after adjustments Variable Coefficient Std. Error t-Statistic Prob. FEMALES(-1) -0.035447 0.087804 -0.403705 0.6907 C 13.84571 40.77848 0.339535 0.7377 @TREND(1967) 0.142634 0.121177 1.177076 0.2530 R-squared 0.154234 Mean dependent var -0.521739 Adjusted R-squared 0.069657 S.D. dependent var 3.072896 S.E. of regression 2.963939 Akaike info criterion 5.132023 Sum squared resid 175.6987 Schwarz criterion 5.280131 Log likelihood -56.01826 Hannan-Quinn criter. 5.169272 F-statistic 1.823602 Durbin-Watson stat 1.675355
- 14. Results Note :- If answer are negative, it means stationary is right. If answer are positive, it means stationary is wrong. If wrong then go to 1st difference, also wrong then go to 2nd difference. So the female series is stationary.
- 15. Multicolinearity Definition:- Multicollinearity is a problem in regression analysis that occurs when two independent variables are highly correlated, e.g. r = 0.90, or higher. In other words : This problem occurs when the explanatory variables are very highly correlated with each other.
- 16. Check the multicolinearity’s problem Step 1: i.Open E-view and also open question (1972- 2010). ii.In E-view go option file ͢ work file ͢ enter start and end date ͢ press ok. iii.Go to option Quick empty file paste the data.͢ ͢ Step 2: i.Write command on command bar genr dINF=INF-INF(-1) and press ok. i.Double click on back window (dinf) select option view descriptive statistic stat͢ ͢ ͢ histogram. New diagram’s window will open. ii.Go option Quick group statistic correlation new window will open. In this window͢ ͢ write variables and press ok.
- 17. Results INF GDPDEF GDPG INF 1 0.9141421348686689 -0.079202822826937 GDPDEF 0.9141421348686689 1 -0.1731063518611149 GDPG -0.079202822826937 -0.1731063518611149 1
- 18. Result NOTE: If 0.05 less ͢ normality is not. If 0.05 above normality is exist.͢ If 50% above then there is high multucolinarity. If 1% then there is prefect multicolinarity. If 50% less then less multicolinarity. So, there is perfect Multicolinearity exist among variables.

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