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THE RELATIONSHIP BETWEEN GDPTOWARDS EMPLOYMENT AND
UNEMPLOYMENT
By: Bertilla Voyn Bj Bius (BG10110035)Kamsianah Bt Tajudin (BG10110164)Nursyuhada Binti Muhamad Wazir (BG10110388)
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INTRODUCTION
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THEORETICAL FRAMEWORK
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LITERATURE REVIEW
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METHODOLOGY AND DATA
Simple Regression
Multiple Regression
Log-linear Regression Model
Normality Distribution Heteroscedasticity test (4step)
Autocorrelation test
( 2 step )
The data is from
The data have three variable which are GDP as dependent,Employment and Unemployment as independent
The range is from 1959 to 2010 and it is quarterly.
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MULTIPLE REGRESSION
= -5674.722+0.125205X1-0.032600X2
Se = (185.2340) (0.002238) (0.022553)
t = (-30.63542) (55.94006) (-1.445458)
r2 = 0.962382p = (0.0000) (0.0000) (0.1499)
f-statistic= 2622.286 d = 0.011071
Equation above shows, the estimated value of B1 is -5674.72,B2 is 0.12 and B3 is -0.033. Thus, if the employment goes up
by one unit (1%), gross domestic product (GDP) is expected toincrease on the average by about 0.12 and when unemployment
increase by one unit (1%), gross domestic product (GDP) isexpected to decrease on the average by about -0.033. The
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NORMALITY TEST(HYPOTHESIS TESTING)
Step 1: Hypothesis Testing
H0: The error term is normally distributed
H1: The error term is not normally distributed
Step 2: Decision Rule
Reject the null hypothesis if probability is less than alpha;otherwise do not reject the null hypothesis.
Step 3 : Compute
Probability = 0.016572
alpha () = 0.05 (5%)
Step 4 : Decision
The probability0.016572 is less than alpha() 0.05. Therefore,we reject the null hypothesis.
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F-STATISTIC(HYPOTHESIS TESTING)
Step 1: Hypothesis Testing
H0: R2=0
H1: R20
Step 2: Decision Rule
Reject the null hypothesis when F-statistic is greater thancritical value (c.v)and it will shows that gdp has acorrelation between employed and unemployed, otherwise
do not reject. Step 3: Compute
R2=0.962382 F-statistic = 2262.26
Numerator (n) = 2 d.f = n-(k+1) = 208-(2+1) = 205
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RAMSEY TEST(HYPOTHESIS TESTING)
STEP 1:
H0: misspecification error
H1: not misspecification error
STEP 2:
The alternative hypothesis contains statement that istrying to prove the error term is not misspecification error.When probability chi-square is less than alpha(), reject
the null hypothesis and it shows that the error term ismisspecification error.
STEP 3:
Probability Chi-Square = 0.0000
=
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HETEROSCEDASTICITY(WHITE)
Step 1 : Hypothesis Testing
H0: The is no heteroskedasticity problem
H1: The is heteroskedasticity problem
Step 2: Decision Rule
Reject the null hypothesis if the probability is less thanalpha and this ; otherwise do not reject the nullhypothesis.
Step 3 : Compute
Error term(r2) = 149.3701 where the probability Chi-Square is 0.0000
alpha () = 0.05 (5%)
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HETEREOSCEDASTICITY(WHITE)
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AUTOCORRELATION(DURBIN-WATSON STATISTIC)
Step 1 : Hypothesis
TestingH0: There is no
autocorrelation problemH1: There is
autocorrelation problem
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AUTOCORRELATION(NEWEY TEST)
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DISCUSSIONANDCONCLUSION