رشد اقتصادی و توسعه مالی در کشورهای آسیایی:تجزیه و تحلیل علی پانل گرنجر بوت استراپ
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|12834||2013||8 صفحه PDF||سفارش دهید|
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|شرح||تعرفه ترجمه||زمان تحویل||جمع هزینه|
|ترجمه تخصصی - سرعت عادی||هر کلمه 90 تومان||11 روز بعد از پرداخت||673,920 تومان|
|ترجمه تخصصی - سرعت فوری||هر کلمه 180 تومان||6 روز بعد از پرداخت||1,347,840 تومان|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Economic Modelling, Volume 32, May 2013, Pages 294–301
This study using Kónya (2006) [Kónya, L. (2006). Exports and growth: Granger causality analysis on OECD countries with a panel data approach. Economic Modelling 23, 978–992.] method of bootstrap panel Granger causality analysis, which considers the issues of cross-sectional dependency and slope heterogeneity among countries investigated simultaneously, analyzes the causality between financial development and economic growth among ten Asian countries surveyed during period 1980 to 2007. We find that the direction of causality between financial development and economic growth is sensitive to the financial development variables used in the ten Asian countries examined in this work. Moreover, our findings support the supply-leading hypothesis, as many financial development variables lead economic growth in some of the ten Asian countries surveyed, especially in China.
The aim of this paper is to investigate the causality between financial development and economic growth. Among the many questions that arise about economic growth, two common ones are “why do different phenomena occur during the economic development of different countries?”, and “what are the major causes of these various phenomena?” Many theories and empirical studies suggest that the development of financial markets is a key factor in this, as such markets can make a country's economic environment more efficient (Levine, 1997). From a theoretical perspective, a more financially liberal environment enables investors to more easily reduce risks via financial markets, thus lowering the cost of capital, raising the desire to invest, and ultimately leading to economic growth (Bekaert and Harvey, 2000, Bekaert et al., 2001, Bekaert et al., 2002 and Bekaert et al., 2005). However, some papers have different opinions, such as Robinson (1952), which claims that financial development has no effect on economic growth. Moreover, economic growth may encourage the financial industry to provide better services, and thus economic growth can cause financial development, rather than the other way around. Patrick (1966) refers to these two different views as the supply-leading and demand-following hypotheses,1 respectively. In addition, some papers indicate that there is no significant relationship between financial development and economic growth, or that any relationship that exists is a negative one. Khan and Senhadji (2003) found that financial development affects economic growth in an insignificant manner, although there may be a nonlinear relationship between them. They also found that while financial development may progress slowly in some countries, economic growth may be much faster, and thus that indicators used to measure the latter cannot be used to reflect the former.
نتیجه گیری انگلیسی
The present study uses Kónya's (2006) method of bootstrap panel Granger causality to analyze the causal relations among ten Asian countries over the period of 1980 to 2007. To reveal the different impacts of financial development, it uses four financial development indicators. This method simultaneously considers the issues of cross-sectional dependency and slope heterogeneity among countries examined. The choice of the optimal lag is very important before using Kónya's method of bootstrap panel Granger causality analysis, as stated before. Therefore, we choose the criterion of minimizing SBC (Schwartz Bayesian information criterion) to decide the lag period, and the largest lag period is set at 3. In Table 6, we list the value of SBC for every SUR process, in which it is the explained variable before the arrow sign. We can see that the optimal lag is 1 for each regression. The results are shown in Table 6.