金融学英语论文:全球股市 [9]
论文作者:英语论文论文属性:作业 Assignment登出时间:2014-09-05编辑:yangcheng点击率:18191
论文字数:6000论文编号:org201409022200296052语种:英语 English地区:美国价格:免费论文
关键词:全球股市飙升Stock MarketsEconomics Essay经济学英语论文
摘要:在20世纪90年代初,全球股票市场与新兴市场都有较大涨幅,股票在留学生的金融学研究中一直是热点,所以,本文就是一篇十分优秀的案例,综述了金融经济中的经济发展时代中股票对经济的影响力。
he lower bound and upper bound values, this will indicate inconclusive decision. Furthermore, if the F-statistic of equation (2) and (3) is below the lower bound value, this reveals that there is no co-integration relationship among the variables at the long run. In other words, the null hypothesis of no co-integration cannot be rejected.
The advantages using ARDL co-integration methods it does not impose a restrictive assumption that all the variables under study must be integrated of the same order. In other words, the ARDL approach can be applied regardless of whether the underlying regressors are integrated of order one [I(1)], order zero [I(0)] or fractionally integrated. Secondly, while other co-integration techniques are sensitive to the size of the sample, the ARDL test is suitable even if the sample size is small. Thirdly, the ARDL technique generally provides unbiased estimates of the long-run model and valid t-
statistics even when some of the regressors are endogenous (see also Harris and Sollis, 2003). The ARDL model used in this study can be expressed as follows:
Model 1 – Stock Market Capitalization and Economic Development
The ARDL approach uses two steps to estimate the long run relationship. First step is to determine whether a long run relationship exists between the variables in equations (2) and (3) by considering each of the variables as a dependent variable. Then use the F-test for testing the existence of the long-run relationship in equations (2) and (3). That is, the null hypothesis of no co-integration among variables in equation 2 is tested (i.e., 0) against the alternative hypothesis (i.e., ≠ ≠ 0) using the F-test for the joint significance of the lagged levels coefficient in equation (2). In equation (3), when the share prices is the dependent variable, the null hypothesis of no co-integration among variables is tested (i.e., 0) against the alternative hypothesis (i.e., ≠ ≠ 0) using the F-test for the joint significance of the lagged levels coefficient in equation (3). Second, if the long-run relationship is established between the variables, the long-run coefficients are estimated using the ARDL approach. In addition, for select the optimum lag of the orders of the ARDL model in the two variables using Akaike Information Criteria (AIC).
CHAPTER 4
FINDINGS
Co-integration Test: The ARDL approach
The co-integration test under the bounds framework involves the comparison of the F-statistics against the critical values, which are generated for specific sample sizes. In Table 1 the results of the bounds co-integration test show that the null hypothesis of against its alternative is easily rejected at the 5% significant level. The computed F statistic of I (1) = 6.350 is greater than the critical lower bound value of I (0) = 5.395, thus indicating the existence of long run relationship between GDP and share price. Therefore, Pesaran et al. (2001), stated that if the F statistic is in the between the lower bound and upper bound values, this will indicate inconclusive decision.
When GDP is the dependent variable for five selected ASEAN countries, the calculated F statistic F (GDP/SP) is majority lower bound critical values of 5.395 at the 5% level provided by Pesaran et al. (2001) for 1982-2011 periods. However, when using equation 3, where SP is the dependent variable over the same period as shown at
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