Global financial crisis and its effects
Back to 25 years ago, the „Black Monday‟ in the October 19, 1987 was known as the largest one day drop in the history of the New York Stock Exchange Market. It caused the Wall Street to crash and then began the depression for the whole country. According to Michel (1997), in the 1987, 22.6 percent of the value of the American stock was decreased in the first hour in the Monday morning. It was also given a big effect on the European and Asian stock market through the financial system. Almost 10years later, July 2, 1997, the Asian financial crisis was started, Ed (2009) has concerned the Thai Baht was the first currency to experience problems. In that day, the exchange rate of Thai Baht for dollar decrease by17 percent, the foreign exchange and other financial markets got into a mess. In the following months, all the Asian countries‟ stock markets were shocked by the crisis. In the August 15, 1997, the Wall Street also suffered its worst day since 1987; the Dow Jones dropped 247 points. The tumble on August 15 also immediately spilled over to the world‟s stock markets, Hong Kong, Tokyo and European exchanges.
In the economic model, the GDP is usually measured as the real gross domestic product of the origin countries. So in this thesis I use the real GDP of these countries. Meanwhile, the export volumes from China to the ten export destination also will be the real export value of China. The data of real value of China to import countries are coming from the website of the Customs Bureau of Ministry of Commerce of China. And the real GDP and population of countries are all come from the website of The World Bank. The data of distance between two countries are coming from the website of Distance Calculation Org. The dependent variable in the current empirical study is the real value of Chinese export to its major export destinations. The data come from the website of the Customs Bureau of Ministry of Commerce of the China during the sample period of 2001 to 2010.
Unit root test
According to the principle of statistics, if the time series is not stationary, then the result will not reflect the real relationship between the dependent variables and the independent variable, and the regression will also become to spurious regression. Due to the data is the annually in the period from 2001 to 2010 in ten countries and I will use panel data model, avoiding getting the spurious regression, I need do the unit root test for my sample data. 1,2 Therefore, the unit root test was done for individual series variables. The results of the ADF test for unit root are shown in tables.
For the panel data models, there are two ways to estimate, the fixed effects and the random effects (Verbeek, 2007). In order to choose between the models, I use the Hausman Test, the general idea of Hausman test is to compare two estimators which one is consistent under the null (Ho:??? and ?? are uncorrelated) and an alternative hypothesis and the other is only consistent under the null hypothesis. If the difference between the two estimators is significant, then we can reject the null hypothesis. But for the gravity model, one of the short comings of the fixed effect model is that it cannot identify the impact of time invariant, such like distance between two countries. But the distance is an important variable in my paper. Penh (2008) in his study also stated the disadvantage for the fixed effect model, it cannot estimate coefficient for distance, common language and so on. So first I run the estimate by random effects in Eviews, and then do the Fixed/Random effects testing; Hausman Test of which result is the following.
Discussion of the Estimation Results
When estimating the regression model, all the time series variables use the first-differenced data, so the variable L?(???) means the real GDP growth rate of China, L?(???) means the real GDP growth rate of the export destination countries, L? (???) means the population growth rate of China and the L? (???) is the population growth rate of export destination countries. The coefficient estimates of the real GDP growth rate of Chinese export to destination economies and the dummy variable for the financial crisis. The R 2 of the linear regression function is 0.458787 meaning that 45.88% of the variance of the dependent variable about its mean can be explained by the regression model. The export of China is relatively affected by the real GDP growth rate of export destination countries and whether there have financial crisis or not.
Table of Contents :
- 1 Introduction
- 2 Chinese export trade overview
- 3 Theoretical Framework
- 4 Literature review
- 4.1 Export-led economic growth policy economics
- 4.2 Global Financial crisis and its effects
- 5 Empirical Frameworks
- 5.1 Modeling
- 5.2 Data
- 6 Empirical findings
- 6.1 Unit root test
- 6.2 Estimate results
- 6.3 Discussion of the Estimation Results
- 7 Conclusion
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Effects of global financial crisis on Chinese export: A gravity model study