计量经济学stata英文论文资料.doc

-! 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 Table 1： Y X1 X2 X3 X4 1986 110371 963 107507 264.4 1054 1987 120191 1112 109300 276.6 1063 1988 112776 1366 111026 296.2 1075 1989 101339 1519 112704 377.9 1075 1990 93018 1644 114333 383.2 1075 1991 88128 1893 115823 352.2 1075 1992 94164 2311 117171 363.9 1053 1993 106771 2998 118517 420.1 1065 1994 127935 4044 119850 476.4 1080 1995 145443 5046 121121 519.6 1054 1996 163322 5846 122389 552.8 1032 1997 176353 6420 123626 576.8 1020 1998 198885 6796 124761 571 1022 1999 233513 7159 125786 575 1071 2000 301239 7858 126743 595 1041 2001 393256 8622 127627 681 1225 2002 500980 9398 128453 770 1396 2003 651260 10542 129227 800 1552 2004 819896 12336 129988 827 1731 2005 978610 14040 130756 839 1792 四、Model parameter estimation, inspection and correction 1. Model parameter estimation and its economic significance, statistical inference test twoway(scatter Y X2) twoway(scatter Y X3) twoway(scatter Y X4) graph twoway lfit y X1 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.计量经济学检验 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: Y = 570.3757921*X4 + 53.53863254*X1 - 12.18901747*X2 + 777507.8381 （46.57535） （6.618152） （2.747500） （336370.1） t = （12.24630） （8.089665） （-4.436403） （2.311466） Adjusted R-squared=0.994626 F=987.1753 Through analysis, we can find: add a new variable X2, X2 coefficient - 12.18901747, indicating a negative correlation between X2 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 = 700.5113451*X4 + 53.63805156*X1 - 597.614061*X3 - 534866.1749 （33.11564） （6.480707） （131.3478） （49101.16） t = （12.24630） （ 8.089665） （-4.436403） （2.311466） Adjusted R-squared=0.994626 F=987.1753 Similarly, adding a new variable X3, its parameter estimate is still negative, X3, represented by the number of unemployment in urban areas, the 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 X3 3.White test Final results of a series of inspection and correction： Y = -51055.44688 + 66.53070046*X1 + 382.1680346*X4 （9052.520）　　（9.443438）　　　　　　（78.77833） t = （-5.639916） （7.045178） （4.851182） Adjusted R-squared=0.921287 F=106.3395　　ＤＷ＝1.627477 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