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The Determinants Of Money Of Selected Asian Pacific Countries

3401 words - 14 pages


We will discuss in Chapter Five about the results and findings and would be transformed the finding from Eviews software into tables and figures shown in the form of tables so that can be simplified in form of figures and tables in the hope that easier and in order to understand. We don't take all of finding that have been done after we regress the data, but we just only take the important value that answers the objectives of the study. Therefore, in this chapter also would be shown step by step process that consist four tables to explain the results and findings. In Table 1 and 2 show a panel unit root test that conducted before we proceed ...view middle of the document...

Consequently, when we don’t run Augmented Dickey- Fuller (ADF) test or the data are still in non-stationary therefore the result will be facing from the spurious regression problem outcome and make the results and finding become incorrect conclusions, meaningless and also biased. Detailed examination by Granjer and Newbold (1974) noted that in the time series analysis, the new time series properties such as order of integration or unit root have to be applied into account before proceeding the next step methods to predict a regression model. The reasons for this prediction result may be lead to bias or spurious if the predicted variables are non-cointegrated or non-stationary. Therefore, when the data that is a test of the unit root test in statistical software, there consist of the level, at first differencing and the second differencing. So, to know and ensure either the each variable are stationary or non-stationary, we must look and compare the ADF and PP statistics with the level of critical value. The result of the ADF and PP test statistics is less than level of critical value so that indicates and shown that the variable is stationary. The result will lead us to

reject the null of non-stationary based on hypothesis study. So, based on the results that by conducting e-views software. Table 1 will show the simplified form of table in hope for easier understanding.
Variable Level 1st Difference Determination

(0.9817) 68.5211
(0.0000)*** Stationary at 1st Difference
(0.9892) 47.8365
(0.0000)*** Stationary at 1st Difference
(0.0004)*** 59.5327
(0.0000)*** Stationary at Level
(0.8548) 54.2971
(0.0000)*** Stationary at 1st Difference
DLNREER 12.5185
(0.4050) 39.8524
(0.0001)*** Stationary at 1st Difference
DUSRR 3.03721
(0.9953) 28.4858
(0.0047)*** Stationary at1st Difference
*10% of significant level, **5% of significant level, ***1% of significant level
Table 1: Augmented Dickey- Fuller (ADF) test

The variables such as DLNMoneyDD, DLNIncome, INTSPREAD, DLNGDPdeflator,DLNREER and DUSRR are stationary at first difference except interest rate spread which is stationary at level. Some variables need to be logged (GDP) and first differencing before run for unit root in order to avoid spurious and make the data stationary. So, as a result we failed to reject H0 because ADF is larger than the level of critical value. Table 1 shows that ADF is 68.5211 and probability is 0.0000 for DLNMoneyDD, while 47.8365 for ADF and probability is 0.0000 for DLNIncome. For INTSPREAD,it shown 35.3969 for ADF and probability is 0.0004. Besides, the ADF is 54.2971 and probability is 0.0000 for DLNGDPdeflator. Next is for DLNREER shown that ADF is 39.8524 and probability is 0.0001. Lastly, finding reveals that the ADF is 28.4858 and 0.0047 for its probability for DUSRR.
Variable Level 1st Difference Determination

(0.6667) 87.7925
(0.0000)*** Stationary at...

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