計(jì)量經(jīng)濟(jì)學(xué)stata英文論文終稿

1、.Graduates to apply for the quantitative analysis of changes in number of graduate students一Topics raisedIn this paper, the total number of students from graduate students multivariate analysis specific analysis, and collect relevant data, model building, this quantitative analysis. The number of re

2、lations 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

3、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 Th

4、e total population - it will affect the total number of students in graduate students is an important factor The number of unemployed persons - this is the impact of a direct factor of the total number of students in the graduate students Number of colleges and universities - which is to influence p

5、recisely because of the emergence of more institutions of higher learning in the school the total number of graduate students is not a small factor 二 Establish ModelY=+1X1+2X2+3X3+4X4 +uAmong them, theY-in the total number of graduate students X1 - per capita GDP X2 - the total population X3 - the n

6、umber of unemployed persons X4 - the number of colleges and universities 三、Data collectiondate ExplainHere, using the same area time-series data were fittedData collectionTime series data from 1986 to 2005, the specific circumstances are shown in Table 1Table 1：YX1X2X3X41986110371963107507264.410541

7、9871201911112109300276.6106319881127761366111026296.2107519891013391519112704377.910751990930181644114333383.210751991881281893115823352.210751992941642311117171363.9105319931067712998118517420.1106519941279354044119850476.4108019951454435046121121519.6105419961633225846122389552.8103219971763536420

8、123626576.81020199819888567961247615711022199923351371591257865751071200030123978581267435951041200139325686221276276811225200250098093981284537701396200365126010542129227800155220048198961233612998882717312005978610140401307568391792四、Model parameter estimation, inspection and correction1. Model pa

9、rameter estimation and its economic significance, statistical inference testtwowaytwowaytwowaygraph twoway lfit y X1graph twoway lfit y X2graph twoway lfit y X3graph twoway lfit y X4Y = 59.22454816*X1- 7.158602346*X2- 366.8774279*X3+621.3347694*X4 6.3522883.257541157.940246.72256 t= 9.323341-2.19754

10、8-2.32288913.29839+ 270775.151369252.80.733306R2=0.996048 Adjusted R-squared=0.994994F=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 af

11、fecting 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 a

12、ddition, 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 , the total number of graduate students should be increased, and due to the increa

13、se 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(jì)

14、量經(jīng)濟(jì)學(xué)檢驗(yàn)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 - 61096.250486

15、.311944 42959.23 t = Adjusted R-squared=0.825725 F=91.02316Y = 27.05878289*X2 - 2993786.354 t = R-squared=0.562668 F=23.15862Y = 1231.659997*X3 - 371863.6509 t = Adjusted R-squared=0.749576 F=57.87138Y = 1053.519847*X4 - 964699.7964 t = Adjusted R-squared=0.930628 F=255.8874The analysis shows that t

16、he 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 t = Adjusted R-squared=0.930628 F=255.887Y = 714.1694264*X4 + 25.58237739*X1 - 708247.738148.457082.93005345496.23t = 14.73818 8.7

17、31029 -15.56718Adjusted R-squared=0.986606 FY = 886.3583756*X4 + 8.974091045*X2 - 1852246.68655.526701.837722189180.7t = 15.96274 4.883269 -9.790886Adjusted R-squared=0.969430 F=302.2581Y = 791.519267*X4 + 436.7502136*X3 - 885870.13469.6425390.1089955171.66t = 11.36546 4.846910 -16.05662Adjusted R-s

18、quared=0.969163 F=299.5666By 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 X1Then add the other new variables to the stepwise regressi

19、on:Y = 570.3757921*X4 + 53.53863254*X1 - 12.18901747*X2 + 777507.838146.575356.6181522.747500336370.1t = 12.24630 8.089665 -4.436403 2.311466Adjusted R-squared=0.994626 F=Through analysis, we can find: add a new variable X2, X2 coefficient - 12.18901747, indicating a negative correlation between X2

20、and Y, but in the real economic significance, X2 total population, and Y number of graduate studentsa positive correlation between the more general economic significance of the total population, the absolute amount of the number of graduate student will be more. So, X2, should be removed.Y = 700.511

21、3451*X4 + 53.63805156*X1 - 597.614061*X3 - 534866.174933.115646.480707131.347849101.16t = 12.24630 8.089665 -4.436403 2.311466Adjusted R-squared=0.994626 F=Similarly, adding a new variable X3, its parameter estimate is still negative, X3, represented by the number of unemployment in urban areas, the

22、 economic significance, the more unemployment in urban areas, will encourage more and more people go to PubMed in order to achieveimprove their own quality, employability and opportunities. So, in reality, the two should be positively correlated, it should be removed X33.White testFinal results of a series of inspection and correction：Y = -51055.44688 + 66.53070046*X1 + 382.1680346*X49052.5209.44