The Influencing Factors Analysis and Forecasting of Beijing's GDP Based on Eviews.doc

1、1The Influencing Factors Analysis and Forecasting of Beijings GDP Based on EviewsAbstract. GDP has important practical significance for the maintenance of national and local economic and political interests. In this paper, it uses Eviews software to verify the Beijing 1991-2010 GDP index and the fir

2、st, second and third industries index of the initial model tertiary four tests, and makes the 2011 Beijing GDP index prediction. Key words: Eviews software, Beijings GDP, Analysis, Forecast Introduction American economist Samuelson (Economics Nobel Prize winner) and Nordhaus in his book “Economics“

3、textbook mentioned that: GDP is the 20th centurys greatest inventions. GDP accounted for judging the macroeconomic situation, the development of sound macroeconomic policies and macro-economic management, which has important theoretical and practical significance. Similarly, GDP reflects a region wi

4、thin all residential units, production of the final product in a given period and the value of the sum of the provision of services. It is the 2best indicator of regional economic conditions. Thus, using a more accurate statistical method GDP index system research, analysis, forecasting can be bette

5、r able to guide the macroeconomic management departments to understand economic situation, the development of economic development strategies, annual plans, long-term planning and a variety of macroeconomic properly. It helps policy makers to guide economic development toward the main economic objec

6、tives, for the maintenance of national and local economic and political interests, has important practical significance. Beijings GDP Factors Affecting Analysis Based on Eviews By researching Beijing Statistical Yearbook, the above analysis and data integrity select the following related variables:

7、the Beijing 1991-2010 GDP index, the first index of industrial GDP, secondary industry GDP index, tertiary industry GDP index of 20 years, 60 data (see Table 1). The Establishment of Timing Diagrams and the Linear Regression Equation. Establishment of Beijings GDP index and the index of primary indu

8、stry, secondary industry GDP index, and the tertiary industry GDP index changes in the timing diagram (see Figure 1). Figure 1. Eviews Timing Diagram 3OLS estimation parameters are used to build the model - the linear regression equation (see Figure 2). Figure 2. Eviews linear regression Model - lin

9、ear regression equation: y = -2.121012 +0.176412 * X 1 +0.329767 * X 2 +0.380208 * X 3 Four Levels Test. It carried out to establish the initial model, which includes economic significance testing, statistical tests, econometric analysis, and forecasting. Economic significance test. Coefficients of

10、the explanatory variables X 1 are 1 = 0.176412, the coefficients of the explanatory variables X 2 are 2 = 0.329767, and the coefficients of the explanatory variables X 3 are 3 =0.380208. 1, 2, 3 are positive, in accordance with the dependent variable y with the explanatory variables X 1, X 2, X 3 po

11、sitive correlation between, in accordance with the explanatory variables X 1, X 2, X 3 growth to improve the growth of the dependent variable y economic reality, which are consistent with the reality of economic significance, the model through economic sense test. Statistical test. Inspection: seen

12、from Figure 2, Ie R-squared = 0.999922, Ie, Adjusted R-squared = 0.999907; goodness of fit is high visibility, close to 1, equation fitting degree 4is very good. Be seen from Figure 2, F statistic F-statist ic = 68390.05, and the F-test probability Prob (F-statistic) is small, the equation is signif

13、icant. Explanatory variables X 2, X 3 t-test coefficient accompanying probability Prob. Less than 5%, at the 5% significance level, X 2, X 3 coefficient significantly different from zero, by significant test. X 1 coefficient associated with the probability of the t - test probe. Greater than 5%, the

14、re may be multi-collinearity. Econometric analysis. In the multivariate case, choose to include cross terms (cross-terms) for white toast (see Figure 3). Figure 3. The white heteroscedasticity tests Where F is the auxiliary regression model F-statistic value F statistic. Take a significant level = 0

15、.05, since concomitant probability Prbo. F (9,10) = 0.007 5%, that there is heteroscedasticity. Heteroscedasticity uses the Heteroskedasticity consistent coefficient consistency coefficient, eliminating heteroscedasticity (see Figure 4). Figure 4. Linear regressions after eliminating the heterosceda

16、sticity 5Multi-collinearity test. Explanatory variables use the simple correlation coefficient matrix for multicollinearity test (see Figure 5). Figure 5. Multicollinearity tests As can be seen from the figure, the explanatory variables are paired correlation coefficients were above 80%, indicating

17、the presence of severe multicollinearity. Elimination of multicollinearity - Stepwise Regression. Separately with each of the explanatory variables are the explanatory variables doing simple regression (Figure 6,7,8), which determines the importance of the explanatory variables. Figure 6. X 1 to y i

18、s simple regression Regression equation: = -8231.954 +36.10241 X 1; = 0.706467, F = 43.32189 Figure 7. X 2 to y is simple regression Regression equation: = -115.3589 +1.478379 X 2; = 0.994968, F = 3559.342 Figure 8. X3 to y is simple regression Regression equation: = 86.33524 +490636 X 3; = 0.999490

19、, F = 35285.80 Thus, the variables and associated coefficient data (see Table 2) 6The above results show that adding of X 3 Max. Therefore, X3 is based, in order to join other variables (see 9,10,11). Figure 9. X1 and X3 return to y Regression equation: = -28.08854 +0.479861 X 1 +0.486045 X 3; = 0.9

20、99527, F = 17978.26 Figure 10. X2 and X3 return to y Regression equation: = 38.96269 +0.336748 X 2 +0.379593 X 3; = 0.999917, F = 102605.9 Thus, the variables and associated coefficient data (Table 3) The above results show that, after adding X 2, = 0.999917, most improved, and the variable paramete

21、r estimation is positive. Therefore, the amendment regression results (see Figure 11) Figure 11. Fixed regression results Fixed regression equation: = 38.96269 +0.336748 X 2 +0.379593 X 3 (6.847235) (9.361519) (31.86917) = 0.999917, = 0.999907, F = 102605.9 Factor analysis of various factors of the

22、Beijings GDP Fixed regression equation = 38.96269 +0.336748 X 2 +0.379593 X 3 illustrates, in the case of other factors 7constant, the secondary industry and tertiary industry index increased by 1, Beijings GDP index increased by an average 0.716341. Meanwhile, the coefficient of determination of 0.

23、999907, indicating high model goodness of fit, F value 102605.9, the whole equation significantly; slope coefficient t is 9.361519,31.86917, t test significance. Beijings GDP forecast Based on Eviews To Establish Predictive Models. Prediction model: y = -2.121012 +0.176412 * X 1 +0.329767 * X 2 +0.3

24、80208 * X 3 Using Eviews to Expand the Scope of the Sample Period. The sample in the period ranging from 1991-2010 to 1991-2011 expansion (see Figure 12) Figure 12. Forecast Line Chart Input 2011 variable data (see Figure 13) X 1 = 283.1, X 2, 1854.6, X 3 = 5272.3 Figure 13. 2011 variable data input

25、 Beijing forecast 2011 GDP index (see Figure 14). Figure 14. 2011 Beijing GDP Index Forecast y = 2663.975. And the actual value y (2011) = 2671.4 (Source: Beijing Statistical Yearbook), compared the error is about 0.278%. Conclusions 8Results can be seen from the above analysis, the secondary indust

26、ry and tertiary industry GDP index to Beijings GDP index has a significant effect, the tertiary industry relative to the second industry of Beijings GDP contribution to the larger index. From Beijings GDP index forecast perspective, in 2011 GDP index increased by 8% compared to 2010, in 2009 about 1

27、0% of the increase, although the decline, but still keep it running smoothly. Resource-based industries in the second industrial development for effective control, to further improve the existing industrial structure, increase, and accelerate the development of high-tech industries. Accelerate the p

28、ace of development of the tertiary industry, increase the production of tertiary industry accounted for the proportion of GDP; improve the market system, to create a favorable policy environment, play urban functional advantages; establishment of modern integrated metropolis, vigorously develop the

29、capitals cultural industry, to build the economic development of pillar industries; also emphasized the prominence of the tertiary industry and pulling twelve industrial development and to improve the level of economic development an important role. References 91 XuXianchun GDP accounting significan

30、ce and role of the National Bureau of Statistics, http:/ 2 Wang Feiyu, Beijing per capita GDP influencing factors J, business impact, February 2012 3 Qiu Jingnan, EVIEWS software econometric models in the process of establishing Empirical Analysis J, market research, 2007 4 Sun Jing water, Econometrics M, Tsinghua University Press, 2004 5 Beijing Municipal Bureau of Statistics, Beijing Statistical Yearbook http:/