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论文作者:www.51lunwen.org论文属性:硕士毕业论文 dissertation登出时间:2015-05-12编辑:felicia点击率:26307
论文字数:13668论文编号:org201505081941513653语种:英语 English地区:印度价格:免费论文
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摘要:这是一篇澳洲留学论文,主要分析印度经济的发展状况,浅析影响经济发展的重要因素。
Where;
The log of Real Industrial Production calculated for the period 1988-2008 (t).
Is a constant.
= The log of Real Exports calculated for the period 1988-2008 (t).
= The log of Real Gross Domestic Product calculated at 1990 prices.
= The log of Real Tariff rates calculated for the period 1988-2008.
Having defined the four economic indicators that will be used, the next step will be to create logs for each of these variables using the Eviews software. The reason for creating logarithms for each of the four data sets is to improve the precision and accuracy of capturing the gradual changes of the time series data. This is why both the dependant and independent variables illustrated in the equation above have the L symbol, which represents the logarithm. After the initial stage of uploading the raw data and creating logs, we perform the Augmented Dickey-Fuller (ADF) test in order to check for unit roots amongst the data sets. The purpose for carrying out unit root tests is to check if the set of data is stationary and more crucially to identify any patterns of co-integration between the variables. Alternatively, the unit root tests can also verify if the data used for each of the variables is spurious, which would mean that the figures are not useful for identifying patterns. Moreover, if we find that the data is not stationary, the order of integration can be applied using the ADF test in order to differentiate the data series a number of times to achieve stationary data. Hence, if the data set is integrated at order one I (1) demonstrate that the variable is stationary at the first difference. Additionally the data may be stationary at the integrated order two I (2), or sometimes at I (0) which means it does not need to be differentiated.
Results of ADF test
Having applied the ADF test to the selected data series, it is evident that the data does not seem to be stationary at I (0) or I (1), (Appendix A). However, when the data is integrated at order one I (2) the data is stationary and demonstrate co-integration, since the value of t-statistic is lower than the critical values hence we reject the null hypothesis, (Appendix A). Based on the ADF test, the r-squared values provides an explanation for the Cointegration of the dependant variable with regards to the independent variable. Moreover, the more closer the r-squared value is to 1, indicates a higher level of confidence between the data series. Thus, the r-squared value for ADF unit root test on the variables is 0.810145, which is significantly high and almost close to 1. This implies that the data variables are more likely to be cointegrated, since there is a high level confidence and stationarity between them. Another crucial indicator in the ADF test is the value of the F-Statistic, which helps to identify th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。