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es imports.

Relevant data regarding the variables for the last 30 years starting from 1985 to 2010 will be obtained through the websites of State Bank of Pakistan, World Bank Indicators, OECD.

Data

Year

Pakistan - import value index Import value index (2000 = 100)

Fuel imports (% of merchandise imports)

Commercial service imports (% of merchandise imports)

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(X2,2)

Method: Least Squares

Date: 07/20/12 Time: 01:11

Sample (adjusted): 1983 2010

Included observations: 28 after adjustments

Variable

Coefficient

Std. Error

t-Statistic

D(X2(-1))

-0.939354

0.198065

-4.742653

C

0.041996

0.472734

0.088836

R-squared

0.463837

Mean dependent var

Adjusted R-squared

0.443216

S.D. dependent var

S.E. of regression

2.495747

Akaike info criterion

Sum squared resid

161.9476

Schwarz criterion

Log likelihood

-64.30124

F-statistic

Durbin-Watson stat

2.008990

Prob(F-statistic)

Graphical Analysis

Interpretation

it is positive so there exists positive relation between X1 and Y. if X1 increases by 1 unit, Y will increase by 10.66 units and vice versa.

= 12.25 it is positive so there exists positive relation between X2 and Y. If X2 increases by 1 unit, Y will increase by 12.25 units and vice versa.

, it is the value of imports in the absence of independent variables.

R2 tells that what percentage of variations in dependent variables is coming from explained variables. It also tells that either a model is a good fit or not. If R2 is greater than 30%, model is a proof fit and if R2 is less than 30%, model is not a good fit.

R2 = 0.43, it means there is 43.28% variations in dependent variable due to the explained variables and remaining due to the error term. As R2 is greater than 30%, the model is a good fit.

T-Test tells whether an independent variable is significant or not. If T-Test value is greater than 2, variable is significant and if T-Test value is less than 2, variable is insignificant.

The T-Test value for X1 is 4.09, it is greater than 2 so X1 is significant.

T-Test value for X2 is 3.24, it is greater than 2, so X2 is also significant.

F-Test tells us that either the overall model is significant or not. If value of F-Test is greater than 10, the model is significant, if F-Test value is less than 10, model is insignificant.

As the value of F-test for this model is 10.299 which is greater than 10, so the overall model is significant.

Durbin-Watson test tells that either the auto correlation is present in the model or not. If Durbin-Watson test value is less than 2, there is positive auto correlation. If value of Durbin –Watson test is greate本论文由英语论文网提供整理，提供论文代写，英语论文代写，代写论文，代写英语论文，代写留学生论文，代写英文论文，留学生论文代写相关核心关键词搜索。