計量經(jīng)濟學(xué)英文論文
計量經(jīng)濟學(xué)是以一定的'經(jīng)濟理論和統(tǒng)計資料為基礎(chǔ)，運用數(shù)學(xué)、統(tǒng)計學(xué)方法與電腦技術(shù)，以建立經(jīng)濟計量模型為主要手段，定量分析研究具有隨機性特性的經(jīng)濟變量關(guān)系的一門經(jīng)濟學(xué)學(xué)科。下面是小編為你整理的關(guān)于計量經(jīng)濟學(xué)的英文論文,請你欣賞!
Graduates to apply for the quantitative analysis of changes in number of graduate
students
一、Topics raised
In this paper, the total number of students from graduate students (variable) multivariate analysis (see below) specific analysis, and collect relevant data, model building, this quantitative analysis. The number of relations between the school the total number of graduate students with the major factors, according to the size of the various factors in the coefficient in the model equations, analyze the importance of various factors, exactly what factors in changes in the number of graduate students aspects play a key role in and changes in the trend for future graduate students to our proposal.
The main factors affect changes in the total number of graduate students for students are as follows：
Per capita GDP - which is affecting an important factor to the total number of students in the graduate students (graduate school is not a small cost, and only have a certain economic base have more opportunities for post-graduate)
The total population - it will affect the total number of students in graduate students is an important factor (it can be said to affect it is based on source)
The number of unemployed persons - this is the impact of a direct factor of the total number of students in the graduate students (it is precisely because of the high unemployment rate, will more people choose Kaoyan will be their own employment weights) Number of colleges and universities - which is to influence precisely because of the emergence of more institutions of higher learning in the school the total number of graduate students is not a small factor (to allow more people to participate in Kaoyan)
二、Establish Model
Y=α+β1X1+β2X2+β3X3+β4X4 +u Among them, the
Y-in the total number of graduate students (variable) X1 - per capita GDP (explanatory variables) X2 - the total population (explanatory variables)
X3 - the number of unemployed persons (explanatory variables) X4 - the number of colleges and universities (explanatory variables)
三、Data collection
1. date Explain
Here, using the same area (ie, China) time-series data were fitted
2. Data collection
Time series data from 1986 to 2005, the specific circumstances are shown in Table 1
四、Model parameter estimation, inspection and correction
1. Model parameter estimation and its economic significance, statistical inference test
twoway(scatter Y X1)
twoway(scatter Y X2)
twoway(scatter Y X3)
twoway(scatter Y X4)
twoway(scatter Y X4)
graph twoway lfit y X2
graph twoway lfit y X3
graph twoway lfit y X4
Y = 59.22454816*X1- 7.158602346*X2- 366.8774279*X3+621.3347694*X4
（6.352288） （3.257541） （157.9402） （46.72256） t= （9.323341） （-2.197548） （-2.322889） （13.29839） + 270775.151 （369252.8） （0.733306）
R2=0.996048 Adjusted R-squared =0.994994 F=945.1415
DW=1.596173
Visible, X1, X2, X3, X4 t values are significant, indicating that the per capita GDP, the total population of registered urban unemployed population, the number of colleges and universities are the main factors affecting the total number of graduate students in school. Model coefficient of determination for 0.996048 amendments coefficient of determination of 0.994994, was relatively large, indicating high degree of model fit, while the F value of 945.1415, indicating that the model overall is significant。
In addition, the coefficient of X1, X4, in line with economic significance, but the coefficient of X2, X3, does not meet the economic significance, because from an economic sense, with the increase in the total population (X2), the total number of graduate students should be increased, and due to the increase in the number of unemployed, there will be more and more people choose graduate school, so that the total number of unemployed and graduate students should be positively correlated. X2, X3 coefficient sign contrary to expectations, which may indicate the existence of severe multicollinearity.
2.計量經(jīng)濟學(xué)檢驗
The above table can be seen to explain the positive correlation between the height of the variable X1 and X2, X3, X4, X2, X1, X3, between the highly positively correlated, showing that there is serious multicollinearity. Following amendment stepwise regression：
Y = 60.21976901*X1 - 61096.25048
（6.311944） （42959.23）
t = (9.540606) (-1.422191) Adjusted R-squared=0.825725 F=91.02316
Y = 27.05878289*X2 - 2993786.354
( 5.622791) (680596.9) t = (4.812340) (-4.398766) R-squared=0.562668 F=23.15862
Y = 1231.659997*X3 - 371863.6509
(161.9045) (90051.37) t = (7.607324) (-4.129461)
Adjusted R-squared=0.749576 F=57.87138
Y = 1053.519847*X4 - 964699.7964 (65.85948) (79072.71)
t = (15.99648) (-12.20016)
Adjusted R-squared=0.930628 F=255.8874
The analysis shows that the four simple regression model, the total number of graduate students for the linear relationship between Y college x4, goodness of fit： Y = 1053.519847*X4 - 964699.7964 (65.85948) (79072.71)
t = (15.99648) (-12.20016)
Adjusted R-squared=0.930628 F=255.887
Y = 714.1694264*X4 + 25.58237739*X1 - 708247.7381 （48.45708） （2.930053） （45496.23） t = （14.73818） （8.731029） （-15.56718）
Adjusted R-squared=0.986606 F=700.7988
Y = 886.3583756*X4 + 8.974091045*X2 - 1852246.686
�。�55.52670） （1.837722） （189180.7） t = （15.96274） （4.883269） （-9.790886）
Adjusted R-squared=0.969430 F=302.2581
Y = 791.519267*X4 + 436.7502136*X3 - 885870.134
�。�69.64253） （90.10899） （55171.66） t = （11.36546） （4.846910） （-16.05662） Adjusted R-squared=0.969163 F=299.5666
By the data analysis, comparison, per capita GDP of the new entrants to the X1 equation of the Adjusted R-squared = .986606
, The largest improvement, and each parameter, T-test significant, so I chose to retain the X1
Then add the other new variables to the stepwise regression:
五、Analysis and conclusions of the model
It can be seen from the model:
(1) model: significantly correlated only with colleges and universities total and per capita GDP in the total number of graduate students.
(2) X1, X4 is in line with economic significance of the test. Economic sense, the total number of graduate students with the increase in per capita GDP increases, the increase with the increase in the total number of universities. And universities is the total impact of the total number of the most important factor in the graduate students.
(3) the amendment of the model coefficient of determination and F values are very high goodness of fit of the model is good
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