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7/31/2019 GDP INF EXPO PG 1
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IMPACT OF EXPORT POPULATION GROWTH AND INFLATION ON GDP
1
ARM TERM REPORT
IMPACT OF EXPORT POPULATION GROWTH AND INFLATION ON GDP
(MBA EVENING SUMMER 2012)
TERM REPORT OF RESEARCH METHODS
Submitted to:
Sir Tehseen Jawed
Presented By
Faiz Ullah Khan (3052)
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This report is based on detail summary to run E-views software for research based analysis. In this reportI will judge my econometric model with the help of different analytical technique to predict my model.Data is collected from state bank of Pakistan, handbook of statistics 2005.
My analysis is based on casual effect. So I am using Regression Analysis to estimate my econometricmodel. Variables of model are INFLATION , EXPORT, GROSS DOMESTIC PRODUCT ANDPOPULATION GROWTH.
DATA IS COLLECTED FROM 1981 -2010 (30 OBSERVATIONS)
YEARS INF Expo GDPG PG1981 11.949 2,957.5 6.4 3.35
1982 5.862 2,489.2 7.6 3.37
1983 6.446 2,710.6 6.8 3.38
1984 6.056 2,769.1 43.36
1985 5.564 2,504.1 8.7 3.32
1986 3.467 3,072.8 6.4 3.28
1987 4.692 3,687.8 5.8 3.24
1988 8.835 4,457.2 6.4 3.15
1989 7.882 4,693.2 4.8 3.03
1990 9.051 4,964.7 4.6 2.88
1991 12.628 6,167.0 5.6 2.72
1992 4.851 6,912.2 7.7 2.58
1993 9.825 6,819.3 2.3 2.49
1994 11.272 6,812.8 4.5 2.491995 13.022 8,137.2 4.1 2.53
1996 10.789 8,707.1 6.6 2.59
1997 11.803 8,320.3 1.7 2.62
1998 7.812 8,627.7 3.5 2.58
1999 5.736 7,779.3 4.2 2.44
2000 3.584 8,568.6 3.9 2.26
2001 4.41 9,201.6 2 2.06
2002 2.504 9,134.6 3.1 1.89
2003 3.102 11,160.2 4.7 1.78
2004 4.568 12,313.3 7.5 1.752005 9.276 14,391.1 9 1.76
2006 7.921 16,451.2 5.8 1.78
2007 7.771 16,976.2 6.8 1.78
2008 11.998 19,052.3 5.8 1.79
2009 20.775 17,688.0 5.8 1.79
2010 11.73 19,290.0 5 1.78
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Here I am using Regression analysis in our Econometric model. Data is time series collected from Handbook of Statistics 2005. Published by state bank of Pakistan (SBP).
Our Model of the above variables is
REGRESSION ANALYSIS
A statistical technique use to determine the strength of the relationship between one dependent variable(usually expressed as Y) and more than one independent variables (predictors). Regressions are of twotypes Linear and multiple.
Linear Regression (one Dependent Variable and One Independent Variable)
Multiple Regressions (One DV and more than one IVs) model for multiple regressions can be written as
Y=a +b1X1 +b2X2+b3X3..+BtXt + u
Our model is
Where:
Gdp: variable we are trying to predict
Inf : Predictor of gdp
Pg: predictor of gdp
Expo:predictor of gdp
= Intercept (Constant)
= slope of model (define how much predictor effect on Dependent Variable)
= regression residual (er ror term)
To Run Regression model in e-views first we need to create new work file to enter data. Steps of runningregression are mention below:
FileNewWorkfile enter (now select Annual data write yours starting date and ending date)
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In white space write Variables (GDP INF EXPO PG) Press Enter .Fill the data variable vise from excelsheet and run Regression Analysis by the following equation.
Quick Estimate Equatio n gdp c pg expo inf and press ENTER from keyboard
To run Multiple regression analysis we usually used ordinary least square method to minimize error term.
OLS method to run equation Dependent Variable: GDP
Method: Least SquaresDate: 07/28/12 Time: 22:45Sample: 1981 2010Included observations: 30
Variable Coefficient Std. Error t-Statistic Prob.
EXPO 0.000199 6.40E-05 3.104753 0.0044INF -0.081291 0.095864 -0.847983 0.4039PG 1.723572 0.253442 6.800663 0.0000
R-squared 0.121354 Mean dependent var 5.370000Adjusted R-squared 0.056269 S.D. dependent var 1.865873S.E. of regression 1.812617 Akaike info criterion 4.122060Sum squared resid 88.71070 Schwarz criterion 4.262180Log likelihood -58.83090 Hannan-Quinn criter. 4.166886Durbin-Watson stat 1.598787
Results shows that the value of Durbin-watson stat is 1.5. If it is nearer to 2 or equal to 2 it means there isno auto correlation. To confirm this we have to run Serial correlation LM Test for our hypothesis.
H: - Auto correlation does not existHa:- Auto Correlation exits:
Where is 5% significant level
Result explained that our Independent variables inflation have insignificant effect on GDP (DependentVariable) whereas Export and Population Growth have significant impact on GDP.
TEST OF MULTICOLLINEARITY:
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Case of multiple regression in which the predictor variables are themselves highly correlated. When thereis multicollinearity exits chances of error are higher significant value become insignificant.
DETECTION OF MULTICOLLINEARITY If value of Independent Variables is greater than 0.7 (correlation exist) VIF(variance inflation factor) is less than 10 or 5 (means no auto correlation) When theories go against each other (i.e universal truth become change)
REMOVAL OF MULTICOLLINEARITY Remove that VIF which has high VIF VIF is high but significant do not exclude Change variables with their ratio which show high correlation i.e. if we have taken FDI as
percentage of GDP.
Fetch excel file in spss and Run the following Procedure ANALLYZEREGRESSIONLINEAR click on STATISTICS mark on COLLINEARITY
DIAGNOSTICS .Now check value of VIF (variance inflation factor).
Coefficients a
Model
UnstandardizedCoefficients
StandardizedCoefficients
t Sig.
CollinearityStatistics
B Std. Error Beta Tolerance VIF
1 (Constant) -5.414 5.004 -1.082 .289
INF -.122 .103 -.260 -1.190 .245 .676 1.478
Expo .000 .000 1.084 2.080 .048 .119 8.394
PG 3.345 1.520 1.081 2.200 .037 .134 7.453
a. Dependent Variable: GDPG
Serial Correlation LM test
Go to View Residual T estLM test
Breusch-Godfrey Serial Correlation LM Test:
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F-statistic 0.988030 Prob. F(1,26) 0.3294Obs*R-squared 1.092685 Prob. Chi-Square(1) 0.2959
COCHRAN ORCKUT METHOD
It is a proposed theory by which we can easily remove auto correlation from our model without addingany variable. To run this test we first create Residual series (error) to run for that we take lag(t-1) of error. Purpose of taking this is to create transpose variable to remove trend from auto correlation. Becauseone reason of existence of auto correlation is that it creates trend in error term which create baseness inour model. Now we run our procedure from auto correlation in e-views by Cochran Orckut Method.
Go to Proc click make residual series click OrdinaryER/resid Press EnterNow error series will generate on workfile page . Now we will do our next step, we find out value of byestimate error series.
Chochran test
It is a method of removal of auto correlation. There are three ways to remove auto correlation.
Add variable which can remove auto correlation Using Cochran Orkut method for creating residual error Using AR (1) Method.
Here we enter ER er (-1) method. For this we:
Go to Quick click Estimate equation write er er(-1) Press enter from keyboard
Dependent Variable: ERMethod: Least SquaresDate: 07/28/12 Time: 23:02Sample (adjusted): 1982 2010Included observations: 29 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
ER(-1) 0.191727 0.185350 1.034409 0.3098
R-squared 0.035675 Mean dependent var -0.059592Adjusted R-squared 0.035675 S.D. dependent var 1.768667S.E. of regression 1.736831 Akaike info criterion 3.975876Sum squared resid 84.46432 Schwarz criterion 4.023024Log likelihood -56.65020 Hannan-Quinn criter. 3.990642Durbin-Watson stat 2.023470
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From the above output we can see coefficient of error(-1) which is 0.191727 ,will use this to maketranspose of variables. Now we will generate series for all the variables of our model and again runregression on these transpose variables.
Quick click Generate series write tgdp=gdp-(0.191727)*gdp(-1) (all other variables will begenerate in a similar manner ) press Enter
Dependent Variable: TGDPMethod: Least SquaresDate: 07/28/12 Time: 23:10Sample (adjusted): 1982 2010Included observations: 29 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
TEXP 80.93919 62.29243 1.299342 0.2052
TINF -0.010484 0.048306 -0.217033 0.8299TPG -17.79209 35.50261 -0.501149 0.6205
R-squared 0.013371 Mean dependent var 32.45069Adjusted R-squared -0.062524 S.D. dependent var 20.47351S.E. of regression 21.10385 Akaike info criterion 9.034485Sum squared resid 11579.68 Schwarz criterion 9.175930Log likelihood -128.0000 Hannan-Quinn criter. 9.078784Durbin-Watson stat 1.515994
Now we see value of DW is still looking suspicious though it is more than 1.5 consider nearer to 2. But
we check again our same hypothesis by LM test.
Go to View click Residual test click LMtest
Breusch-Godfrey Serial Correlation LM Test:
F-statistic 1.523314 Prob. F(1,25) 0.2286Obs*R-squared 1.665558 Prob. Chi-Square(1) 0.1969
AR (1) METHOD
We can also use AR1 method to remove auto correlation in this method we do not need to add new valueof error term. We simply run equation in a OLS manner by using Lag 1.
Quick click Estimate equation write gdp c expo pg inf AR(1) press Enter
Dependent Variable: GDPMethod: Least SquaresDate: 07/28/12 Time: 23:14Sample: 1981 2010
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Included observations: 30
Variable Coefficient Std. Error t-Statistic Prob.
C -5.497098 4.992753 -1.101016 0.2810EXPO 0.000392 0.000187 2.099839 0.0456
INF -0.123065 0.102751 -1.197702 0.2418PG 3.369884 1.516428 2.222251 0.0352
R-squared 0.160496 Mean dependent var 5.370000Adjusted R-squared 0.063630 S.D. dependent var 1.865873S.E. of regression 1.805535 Akaike info criterion 4.143157Sum squared resid 84.75887 Schwarz criterion 4.329983Log likelihood -58.14735 Hannan-Quinn criter. 4.202924F-statistic 1.656886 Durbin-Watson stat 1.719396Prob(F-statistic) 0.200699
Heteroskedasticity Test: White
Heteroskedasticity Test: White
F-statistic 1.408901 Prob. F(6,23) 0.2537Obs*R-squared 8.062789 Prob. Chi-Square(6) 0.2335Scaled explained SS 5.393309 Prob. Chi-Square(6) 0.4944
DUMMY VARIABLE
We use dummy variables when we cannot quantify our data in econometric model. We randomly assign 1to the any year where we want to see the effect.
How to create dummy variable in e-views
Go to Data dy(variable for which you want to create dummy) click Quick click estimate equationwrite gdp c expo inf pg dy Press enter
Dependent Variable: GDPMethod: Least SquaresDate: 07/28/12 Time: 23:58Sample: 1981 2010Included observations: 30
Variable Coefficient Std. Error t-Statistic Prob.
C -3.828800 6.647008 -0.576019 0.5698INF -0.122479 0.104482 -1.172258 0.2521
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PG 2.898483 1.961470 1.477710 0.1520EXPO 0.000348 0.000221 1.577176 0.1273
DY -0.399318 1.027111 -0.388778 0.7007
R-squared 0.165541 Mean dependent var 5.370000Adjusted R-squared 0.032027 S.D. dependent var 1.865873
S.E. of regression 1.835751 Akaike info criterion 4.203796Sum squared resid 84.24950 Schwarz criterion 4.437329Log likelihood -58.05694 Hannan-Quinn criter. 4.278505F-statistic 1.239881 Durbin-Watson stat 1.687795Prob(F-statistic) 0.319593
Unit root test
Unit root test is used to remove the trend in time series data. First we need to check trend in the data. If trend exist then we will take 1 st difference to remove the trend from the data. If trend did not removedthen we have to take 2 nd difference as well. Data is of two types in unit root test.
STATIONARY : Data in which trend doesn t exist. Non-STATIONARY : Data in which trend exist.
Null Hypothesis: GDP has a unit rootExogenous: ConstantLag Length: 0 (Automatic based on SIC, MAXLAG=1)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -3.924160 0.0055Test critical values: 1% level -3.679322
5% level -2.96776710% level -2.622989
*MacKinnon (1996) one-sided p-values.
Null Hypothesis: EXPO has a unit rootExogenous: ConstantLag Length: 0 (Automatic based on SIC, MAXLAG=1)
t-Statistic Prob.*
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Augmented Dickey-Fuller test statistic 1.461211 0.9987Test critical values: 1% level -3.679322
5% level -2.96776710% level -2.622989
*MacKinnon (1996) one-sided p-values.
Null Hypothesis: INF has a unit rootExogenous: ConstantLag Length: 0 (Automatic based on SIC, MAXLAG=1)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -2.843202 0.0647Test critical values: 1% level -3.679322
5% level -2.967767
10% level -2.622989*MacKinnon (1996) one-sided p-values.
Null Hypothesis: PG has a unit rootExogenous: ConstantLag Length: 1 (Automatic based on SIC, MAXLAG=1)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -2.051717 0.2645Test critical values: 1% level -3.689194
5% level -2.97185310% level -2.625121
*MacKinnon (1996) one-sided p-values.
AUGMENTED DICKEY FULLER AT 1 ST DIFFERENCE:
Null Hypothesis: D(GDP) has a unit rootExogenous: ConstantLag Length: 0 (Automatic based on SIC, MAXLAG=1)
Augmented Dickey-Fuller test statistic -8.381068 0.0000Test critical values: 1% level -3.689194
5% level -2.97185310% level -2.625121
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*MacKinnon (1996) one-sided p-values.
Null Hypothesis: D(EXPO) has a unit rootExogenous: ConstantLag Length: 1 (Automatic based on SIC, MAXLAG=1)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -2.525988 0.1207Test critical values: 1% level -3.699871
5% level -2.97626310% level -2.627420
*MacKinnon (1996) one-sided p-values.
Null Hypothesis: D(INF) has a unit rootExogenous: ConstantLag Length: 0 (Automatic based on SIC, MAXLAG=1)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -6.601268 0.0000Test critical values: 1% level -3.689194
5% level -2.97185310% level -2.625121
*MacKinnon (1996) one-sided p-values.
Null Hypothesis: D(PG) has a unit rootExogenous: ConstantLag Length: 1 (Automatic based on SIC, MAXLAG=1)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -5.306489 0.0002Test critical values: 1% level -3.699871
5% level -2.97626310% level -2.627420
*MacKinnon (1996) one-sided p-values.
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Now we can see at 1 st difference trend is remove from GDP .In a similar way we will show you 1 St difference of all the variable to confirm that trend doesnt exist at 1 st difference also and our data isStationary and best fit to run OLS( spurious regression).
COINTEGRATION TEST
This test is use to predict long term relationship among variables when data is Time series.To run testthere are two ways to run cointegration in e-views
1. Quick Group statistics gdp c inf expo pg summary lag 1 1Enter 2. Open variables as group than go to View Cointegration test summary lag 1 1Enter
Date: 07/29/12 Time: 00:13Sample: 1981 2010Included observations: 28Series: GDP INF PG EXPOLags interval: 1 to 1
Selected(0.05 level*)Number of
Cointegrating Relationsby Model
Data Trend: None None Linear Linear QuadraticTest Type No Intercept Intercept Intercept Intercept Intercept
No Trend No Trend No Trend Trend TrendTrace 1 1 1 2 2
Max-Eig 0 1 1 2 2
*Critical values based on MacKinnon-Haug-Michelis (1999)
InformationCriteria byRank and
Model
Data Trend: None None Linear Linear QuadraticRank or No Intercept Intercept Intercept Intercept Intercept
No. of CEs No Trend No Trend No Trend Trend Trend
LogLikelihood
by Rank (rows) and
Model(columns)
0 -306.6708 -306.6708 -300.1670 -300.1670 -298.9645
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1 -294.7700 -285.0019 -278.9830 -274.9595 -273.99472 -288.2346 -276.1614 -273.1853 -259.3250 -258.68033 -285.1221 -270.4189 -268.2365 -254.2864 -253.96704 -284.9403 -268.1617 -268.1617 -251.7777 -251.7777
Akaike
InformationCriteria byRank (rows)and Model(columns)
0 23.04792 23.04792 22.86907 22.86907 23.068891 22.76929 22.14300 21.92736 21.71139 21.856772 22.87390 22.15439 22.08466 21.23750* 21.334313 23.22301 22.38706 22.30260 21.52046 21.569074 23.78145 22.86869 22.86869 21.98412 21.98412
SchwarzCriteria by
Rank (rows)and Model(columns)
0 23.80918 23.80918 23.82065 23.82065 24.210781 23.91118 23.33246 23.25956 23.09118 23.379292 24.39642 23.77206 23.79750 23.04549* 23.237463 25.12616 24.43295 24.39607 23.75666 23.852854 26.06523 25.34279 25.34279 24.64853 24.64853
Granger Causality Tests
This test is used to check the causal effects of variable.
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Quick click Group statistics write gdp c inf expo pg click summary write lag 1 Press Enter
Pairwise Granger Causality TestsDate: 07/29/12 Time: 00:23Sample: 1981 2010Lags: 5
Null Hypothesis: Obs F-Statistic Prob.
INF does not Granger Cause GDP 25 2.06361 0.1312GDP does not Granger Cause INF 0.36433 0.8645
PG does not Granger Cause GDP 25 0.57561 0.7180GDP does not Granger Cause PG 1.73074 0.1923
EXPO does not Granger Cause GDP 25 0.75964 0.5934GDP does not Granger Cause EXPO 0.55349 0.7336
PG does not Granger Cause INF 25 1.05696 0.4241INF does not Granger Cause PG 1.67334 0.2056
EXPO does not Granger Cause INF 25 0.47452 0.7893INF does not Granger Cause EXPO 2.14127 0.1203
EXPO does not Granger Cause PG 25 0.46824 0.7937PG does not Granger Cause EXPO 1.13597 0.3868
Granger causality tests are used to check the cause and effect in the data.
FORECASTING
In simple forecasting means prediction of future value with the help of trend or available of seasonal orannual data. Forecasting which I am going to predict in my model is of two types.
IN SAMPLE FORECASTINGIn in-sample forecasting we compare few samples from our available data with remaining samples. Wesimply reduce our data size and then forecast and compare our result with actual. In sample forecastingwe forecast our Dependent Variable..If forecasting is done through Dependent variable is known astrend forecasting. If we predicting in tine series data than R 2 Should be greater for better prediction.
TO RUN IN SAMPLE FORECASTING
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Go to Quick click Estimate equationEnter Forecast(on tab window)Forecaste varaible(GDPF )Enter
Now we have the output we see value of Theil Inequality to predict either our model is fit for in sampleforecasting or not. Values( 0 to 1) if it is nearer to 0 we say our model is good for forecasting here youcan see value of Theil Inequality is 0.04. Our model is good for forecasting.
OUT SAMPLE FORECASTING
Dependent Variable: GDPMethod: Least SquaresDate: 07/29/12 Time: 01:37Sample (adjusted): 1981 2005Included observations: 25 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
INF -0.112292 0.129049 -0.870155 0.3936EXPO 0.000186 9.62E-05 1.935329 0.0659
PG 1.825652 0.362685 5.033711 0.0000
R-squared 0.126916 Mean dependent var 5.276000Adjusted R-squared 0.047545 S.D. dependent var 2.020784S.E. of regression 1.972160 Akaike info criterion 4.308302Sum squared resid 85.56711 Schwarz criterion 4.454567
0
2
4
6
8
10
12
82 84 86 88 90 92 94 96 98 00 02 04
GDPF 2 S.E.
Forecast: GDPF Actual: GDPForecast sample: 1981 2005Included observations: 25
Root Mean Squared Error 1.850050Mean Absolute Error 1.520412Mean Abs. Percent Error 38.85766Theil Inequality Coefficient 0.168828
Bias Proportion 0.000190Variance Proportion 0.659586Covariance Proportion 0.340224
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Log likelihood -50.85378 Hannan-Quinn criter. 4.348870Durbin-Watson stat 1.400444
PANELED /POOLED DATA
FIXED EFFECT MODEL
Dependent Variable: INFMethod: Panel Least SquaresDate: 07/29/12 Time: 04:54Sample: 1981 2000Periods included: 20Cross-sections included: 2Total panel (balanced) observations: 40
Variable Coefficient Std. Error t-Statistic Prob.
C 10.66587 1.316850 8.099534 0.0000GDP -0.000443 0.000290 -1.524657 0.1447
Effects Specification
Cross-section fixed (dummy variables)Period fixed (dummy variables)
R-squared 0.650929 Mean dependent var 8.736000Adjusted R-squared 0.243680 S.D. dependent var 2.641298S.E. of regression 2.297048 Akaike info criterion 4.802619
0
2
4
6
8
10
12
82 84 86 88 90 92 94 96 98 00 02 04
GDPF 2 S.E.
Forecast: GDPF Actual: GDPForecast sample: 1981 2015
Adjusted sample: 1981 2005Included observations: 25
Root Mean Squared Error 1.850050Mean Absolute Error 1.520412Mean Abs. Percent Error 38.85766Theil Inequality Coefficient 0.168828
Bias Proportion 0.000190Variance Proportion 0.659586Covariance Proportion 0.340224
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Sum squared resid 94.97576 Schwarz criterion 5.731503Log likelihood -74.05239 Hannan-Quinn criter. 5.138474F-statistic 1.598357 Durbin-Watson stat 2.217281Prob(F-statistic) 0.159380
RANDOM EFFECT MODEL
Dependent Variable: INFMethod: Panel EGLS (Two-way random effects)Date: 07/29/12 Time: 05:39Sample: 1971 2000Periods included: 30
Cross-sections included: 3Total panel (balanced) observations: 90Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
C 8.056929 1.245254 6.470110 0.0000GDP 0.000256 0.000197 1.300911 0.1967
Effects SpecificationS.D. Rho
Cross-section random 1.389398 0.0674Period random 3.826692 0.5111Idiosyncratic random 3.475606 0.4216
hausman test
Correlated Random Effects - Hausman TestEquation: EQ03Test cross-section and period random effects
Test SummaryChi-Sq.Statistic Chi-Sq. d.f. Prob.
Cross-section random 0.988866 1 0.3200Period random 3.840737 1 0.0500Cross-section and period random 3.398207 1 0.0653
Cross-section random effects test comparisons:
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Variable Fixed Random Var(Diff.) Prob.
GDP 0.000419 0.000256 0.000000 0.3200
Factor Analysis
Factor analysis is an interdependency technique whose primary objective is to define the undelyingstructure among the variables in the analysis. There are two types of Factor analysis
Exploretry factor Analysis : It is the type of data in which variables are not defined and extracted afterdata reduction.
Confirmatory factor Analysis : It is type of data analysis in which variables are defined .
Now we will run Factor analysis on SPSS
Collinearity Diagnosticsa
ModelDimension Eigenvalue
ConditionIndex
Variance Proportions
(Constant) Work Supervision Co-workers Promotion
1 1 4.731 1.000 .00 .00 .00 .00 .00
2 .134 5.949 .01 .32 .00 .32 .03
3 .055 9.237 .17 .63 .18 .41 .04
4 .045 10.253 .63 .04 .06 .15 .44
5 .034 11.720 .18 .00 .76 .11 .49
a. Dependent Variable: Pay
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KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .662
Bartlett's Test of Sphericity Approx. Chi-Square 60.522
df 10
Sig. .000
Now you can see value of KMO and Barletts is significant it means data is reliable and we can Run
factor on it.
Communalities
Initial Extraction
pay 1.000 .467
work 1.000 .653
supervision 1.000 .656
Coworkers 1.000 .826
Promotion 1.000 .799
Extraction Method: PrincipalComponent Analysis.
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Component Matrix a
Component
1 2
Promotion .857
supervision .798
Coworkers .673 -.611
pay .581 .360
work .480 .650
Extraction Method: Principal
Component Analysis.
a. 2 components extracted.
Rotated Component Matrix a
Total Variance Explained
Component
Initial EigenvaluesExtraction Sums of Squared
LoadingsRotation Sums of Squared
Loadings
Total % of Variance
Cumulative % Total
% of Variance
Cumulative % Total
% of Variance
Cumulative %
1 2.392 47.832 47.832 2.392 47.832 47.832 1.828 36.560 36.560
2 1.010 20.203 68.035 1.010 20.203 68.035 1.574 31.475 68.035
3 .814 16.290 84.325
4 .479 9.588 93.912
5 .304 6.088 100.000
Extraction Method: Principal Component Analysis.
7/31/2019 GDP INF EXPO PG 1
21/21
IMPACT OF EXPORT POPULATION GROWTH AND INFLATION ON GDP
21
Component
1 2
Coworkers .908
Promotion .822 .351
work .807
pay .648
supervision .527 .615
Extraction Method: Principal
Component Analysis.Rotation Method: Varimax with
Kaiser Normalization.
a. Rotation converged in 3
iterations.
Component Transformation
Matrix
Component 1 2
1 .769 .639
2 -.639 .769
Extraction Method: PrincipalComponent Analysis.
Rotation Method: Varimaxwith Kaiser Normalization.