国外商科研究生paper怎么写:paper范文 [10]
论文作者:英语论文论文属性:作业 Assignment登出时间:2014-09-09编辑:yangcheng点击率:14701
论文字数:6018论文编号:org201409072125055402语种:英语 English地区:加拿大价格:免费论文
关键词:商科paper留学生论文Economics paperpaper怎么写Inflation
摘要:本文是加拿大滑铁卢大学关于全球经济的优秀paper,主要研究的课题是石油价格对通货膨胀的影响,目的是用于确定石油价格、通货膨胀、汇率、货币供应和失业之间的关系。
narity
The data are transformed into the logarithmic form on the basis of preliminary analysis. This transformation reduces the variability of variance of the data. At the first step, the ADF and Philips Perron unit root tests is applied to all the variables to test for the stationarity of these variables. The test is applied to both the original series (in log form) and to the first differences. The results, indicate that all the series are non-stationary at their level, that is they are random walk series. They become stationary after employing difference operator of degree one. That is, these series are integrated of order one, I (1).
The ADF unit root test can be represented by the following equation
Where X is the any variable in the model to be tested for unit root and εt is the purely white noise error term.The test is based on the null hypothesis, (H0) : Xt is not I(0), If calculated ADF statistic is less than the critical values from Fuller’s table , then the null hypothesis (H0) is rejected and the series are stationary . Phillips and Perron (PP) unit root test results are also presented.
The results for the unit root tests are given below,
UNIT ROOT TESTS RESULTS
Variables
ADF
PP
Level
1st Diff.
Level
Inf
1.0000
0.9992**
1.0000
OP
1.0000
0.5651*
1.0000
Reer
0.5058
0.0325**
0.5039
MS
0.9998
0.9343*
1.0000
.
*and ** denote the rejection of the null hypothesis at 1% and 5 % level of significance.
All the variables are found to be stationary at the first difference.
The assumption of Ordinary Least square is that all the variables are found to be stationary. But in that model all the variables are non stationay, so to remove this problem we trake all the variables in the difference form.
RESULTS AND DISCUSSION
Dependent Variable: D(INF)
Method: Least Squares
Date: 07/09/12 Time: 12:55
Sample (adjusted): 1982 2008
Included observations: 27 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
C
-0.089070
0.597612
-0.149043
D(INF(-1))
0.531544
0.204866
2.594600
D(MS)
4.62E-06
3.32E-06
1.390175
D(ER)
0.465098
0.139466
3.334856
D(OP)
0.171200
0.077621
2.205569
D(UN)
-0.000499
0.001064
-0.468541
R-squared
0.906938
Mean dependent var
Adjusted R-squared
0.884780
S.D. dependent var
S.E. of regression
1.513682
Akaike info criterion
Sum squared resid
48.11588
Schwarz criterion
Log lik
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