stata程序及运行结果.docx

1、Stata 程序及运行结果(前面整理合并数据部分省略，考试会直接给合并好的数据)（由于是 copy 的，所以会缺少表格线）use “C:Documents and SettingsAdministrator桌面 合并数据 .dta“, cleardrop if q=. leverage=. profit=. fratio=. lna=.(416 observations deleted). sum q, dqPercentiles Smallest1% .601852 -7.6692855% .849397 .25189310% .938017 .283239 Obs 1039725% 1.1

2、2204 .283675 Sum of Wgt. 1039750% 1.4441 Mean 4.98767Largest Std. Dev. 184.905575% 2.066348 955.415890% 3.189917 1736.165 Variance 34190.0395% 4.42306 11665.94 Skewness 72.1676899% 11.19809 14665.54 Kurtosis 5324.15. gsort - q. drop if q50(25 observations deleted). sum leverage, dleveragePercentiles

3、 Smallest1% .0378113 .00172535% .087829 .007079910% .1376331 .0075213 Obs 1037225% .2841151 .0108272 Sum of Wgt. 1037250% .4686987 Mean .5390607Largest Std. Dev. 1.67691175% .6367149 41.9393890% .7588252 41.93938 Variance 2.81203195% .8406378 96.95931 Skewness 42.3892699% 1.6956 96.95931 Kurtosis 22

4、25.629. drop if leverage1 leverage20(4 observations deleted). sum fratio, dfratioPercentiles Smallest1% .0277692 05% .1028079 010% .1602922 0 Obs 1013625% .2636119 0 Sum of Wgt. 1013650% .402213 Mean .4088882Largest Std. Dev. .192008375% .5483563 .955230890% .6696964 .9709213 Variance .036867295% .7

5、325709 .9745661 Skewness .153633499% .8396031 .9745661 Kurtosis 2.416593. drop if fratio F = 0.0000Residual 486.489226 10120 .048072058 R-squared = 0.0142Adj R-squared = 0.0142Total 493.52184 10121 .048762162 Root MSE = .21925leverage Coef. Std. Err. t Pt 95% Conf. Intervalq -.0171031 .001414 -12.10

6、 0.000 -.0198749 -.0143313_cons .4828087 .0033785 142.91 0.000 .4761862 .4894312. reg leverage q profit fratio lnaSource SS df MS Number of obs = 10122F( 4, 10117) = 1241.10Model 162.454363 4 40.6135909 Prob F = 0.0000Residual 331.067476 10117 .032723878 R-squared = 0.3292Adj R-squared = 0.3289Total

7、 493.52184 10121 .048762162 Root MSE = .1809leverage Coef. Std. Err. t Pt 95% Conf. Intervalq .0104224 .0012615 8.26 0.000 .0079497 .0128951profit -.4919057 .0226995 -21.67 0.000 -.5364012 -.4474102fratio .3803813 .0097427 39.04 0.000 .3612837 .399479lna .0646686 .0015325 42.20 0.000 .0616646 .06767

8、25_cons -1.097821 .033489 -32.78 0.000 -1.163466 -1.032175. test q( 1) q = 0F( 1, 10117) = 68.26Prob F = 0.0000. test q profit( 1) q = 0( 2) profit = 0F( 2, 10117) = 250.68Prob F = 0.0000. *test q=1. *test q=profit=1. *test q+profit=1. reg leverage q profit fratio lnaSource SS df MS Number of obs =

9、10122F( 4, 10117) = 1241.10Model 162.454363 4 40.6135909 Prob F = 0.0000Residual 331.067476 10117 .032723878 R-squared = 0.3292Adj R-squared = 0.3289Total 493.52184 10121 .048762162 Root MSE = .1809leverage Coef. Std. Err. t Pt 95% Conf. Intervalq .0104224 .0012615 8.26 0.000 .0079497 .0128951profit

10、 -.4919057 .0226995 -21.67 0.000 -.5364012 -.4474102fratio .3803813 .0097427 39.04 0.000 .3612837 .399479lna .0646686 .0015325 42.20 0.000 .0616646 .0676725_cons -1.097821 .033489 -32.78 0.000 -1.163466 -1.032175. ereturn listscalars:e(N) = 10122e(df_m) = 4e(df_r) = 10117e(F) = 1241.099558756532e(r2

11、) = .329173605634022e(rmse) = .1808974246986086e(mss) = 162.4543634900055e(rss) = 331.0674763826105e(r2_a) = .3289083782368228e(ll) = 2946.854676865168e(ll_0) = 926.2762267636834e(rank) = 5macros:e(cmdline) : “regress leverage q profit fratio lna“e(title) : “Linear regression“e(marginsok) : “XB defa

12、ult“e(vce) : “ols“e(depvar) : “leverage“e(cmd) : “regress“e(properties) : “b V“e(predict) : “regres_p“e(model) : “ols“e(estat_cmd) : “regress_estat“matrices:e(b) : 1 x 5e(V) : 5 x 5functions:e(sample) . gen r2ur=e(r2). reg leverage fratio lnaSource SS df MS Number of obs = 10122F( 2, 10119) = 2126.5

13、8Model 146.048038 2 73.0240189 Prob F = 0.0000Residual 347.473802 10119 .034338749 R-squared = 0.2959Adj R-squared = 0.2958Total 493.52184 10121 .048762162 Root MSE = .18531leverage Coef. Std. Err. t Pt 95% Conf. Intervalfratio .4065295 .0098893 41.11 0.000 .3871446 .4259144lna .0580092 .0014631 39.

14、65 0.000 .0551413 .0608771_cons -.9750239 .0311435 -31.31 0.000 -1.036071 -.9139764. ereturn listscalars:e(N) = 10122e(df_m) = 2e(df_r) = 10119e(F) = 2126.577725823851e(r2) = .29593024275006e(rmse) = .1853071749435181e(mss) = 146.0480378759594e(rss) = 347.4738019966565e(r2_a) = .2957910847784719e(ll

15、) = 2702.068978698881e(ll_0) = 926.2762267636834e(rank) = 3macros:e(cmdline) : “regress leverage fratio lna“e(title) : “Linear regression“e(marginsok) : “XB default“e(vce) : “ols“e(depvar) : “leverage“e(cmd) : “regress“e(properties) : “b V“e(predict) : “regres_p“e(model) : “ols“e(estat_cmd) : “regre

16、ss_estat“matrices:e(b) : 1 x 3e(V) : 3 x 3functions:e(sample) . gen r2r=e(r2). gen F=(r2ur-r2r)/2)/(1-r2ur)/(10122-4-1). . capture drop r2ur r2r F. reg leverage q profit fratio lnaSource SS df MS Number of obs = 10122F( 4, 10117) = 1241.10Model 162.454363 4 40.6135909 Prob F = 0.0000Residual 331.067

17、476 10117 .032723878 R-squared = 0.3292Adj R-squared = 0.3289Total 493.52184 10121 .048762162 Root MSE = .1809leverage Coef. Std. Err. t Pt 95% Conf. Intervalq .0104224 .0012615 8.26 0.000 .0079497 .0128951profit -.4919057 .0226995 -21.67 0.000 -.5364012 -.4474102fratio .3803813 .0097427 39.04 0.000

18、 .3612837 .399479lna .0646686 .0015325 42.20 0.000 .0616646 .0676725_cons -1.097821 .033489 -32.78 0.000 -1.163466 -1.032175. ereturn listscalars:e(N) = 10122e(df_m) = 4e(df_r) = 10117e(F) = 1241.099558756532e(r2) = .329173605634022e(rmse) = .1808974246986086e(mss) = 162.4543634900055e(rss) = 331.06

19、74763826105e(r2_a) = .3289083782368228e(ll) = 2946.854676865168e(ll_0) = 926.2762267636834e(rank) = 5macros:e(cmdline) : “regress leverage q profit fratio lna“e(title) : “Linear regression“e(marginsok) : “XB default“e(vce) : “ols“e(depvar) : “leverage“e(cmd) : “regress“e(properties) : “b V“e(predict

20、) : “regres_p“e(model) : “ols“e(estat_cmd) : “regress_estat“matrices:e(b) : 1 x 5e(V) : 5 x 5functions:e(sample) . gen r2ur=e(r2). reg leverage lnaSource SS df MS Number of obs = 10122F( 1, 10120) = 2196.68Model 88.0196216 1 88.0196216 Prob F = 0.0000Residual 405.502218 10120 .040069389 R-squared =

21、0.1784Adj R-squared = 0.1783Total 493.52184 10121 .048762162 Root MSE = .20017leverage Coef. Std. Err. t Pt 95% Conf. Intervallna .072032 .0015369 46.87 0.000 .0690194 .0750446_cons -1.113191 .0334455 -33.28 0.000 -1.178751 -1.047631. ereturn listscalars:e(N) = 10122e(df_m) = 1e(df_r) = 10120e(F) =

22、2196.679894874584e(r2) = .1783500028923137e(rmse) = .2001733977342292e(mss) = 88.01962156870104e(rss) = 405.5022183039149e(r2_a) = .1782688121811371e(ll) = 1920.46295803989e(ll_0) = 926.2762267636834e(rank) = 2macros:e(cmdline) : “regress leverage lna“e(title) : “Linear regression“e(marginsok) : “XB

23、 default“e(vce) : “ols“e(depvar) : “leverage“e(cmd) : “regress“e(properties) : “b V“e(predict) : “regres_p“e(model) : “ols“e(estat_cmd) : “regress_estat“matrices:e(b) : 1 x 2e(V) : 2 x 2functions:e(sample) . gen r2r=e(r2). gen F=(r2ur-r2r)/3)/(1-r2ur)/(10122-4-1). . drop r2ur r2r F. gen lnq=ln(q). g

24、en lnleverage=ln(leverage). gen lnprofit=ln(profit)(770 missing values generated). gen lnfratio=ln(fratio). reg leverage q profit fratio lna Source SS df MS Number of obs = 10122F( 4, 10117) = 1241.10Model 162.454363 4 40.6135909 Prob F = 0.0000Residual 331.067476 10117 .032723878 R-squared = 0.3292

25、Adj R-squared = 0.3289Total 493.52184 10121 .048762162 Root MSE = .1809leverage Coef. Std. Err. t Pt 95% Conf. Intervalq .0104224 .0012615 8.26 0.000 .0079497 .0128951profit -.4919057 .0226995 -21.67 0.000 -.5364012 -.4474102fratio .3803813 .0097427 39.04 0.000 .3612837 .399479lna .0646686 .0015325

26、42.20 0.000 .0616646 .0676725_cons -1.097821 .033489 -32.78 0.000 -1.163466 -1.032175. display .0104224*1.541244.01606346. reg lnleverage lnq lnprofit lnfratio lnaSource SS df MS Number of obs = 9352F( 4, 9347) = 1165.37Model 1549.75886 4 387.439715 Prob F = 0.0000Residual 3107.51249 9347 .332460949

27、 R-squared = 0.3328Adj R-squared = 0.3325Total 4657.27136 9351 .498050621 Root MSE = .57659lnleverage Coef. Std. Err. t Pt 95% Conf. Intervallnq .1182021 .0137478 8.60 0.000 .0912534 .1451508lnprofit -.1480528 .0081074 -18.26 0.000 -.163945 -.1321606lnfratio .3743693 .0091822 40.77 0.000 .3563702 .3

28、923683lna .2064762 .005279 39.11 0.000 .1961283 .2168242_cons -5.572398 .1249991 -44.58 0.000 -5.817423 -5.327372. . gen profit_2=profit2. reg leverage q profit profit_2 fratio lnaSource SS df MS Number of obs = 10122F( 5, 10116) = 1123.89Model 176.246892 5 35.2493785 Prob F = 0.0000Residual 317.274

29、948 10116 .031363676 R-squared = 0.3571Adj R-squared = 0.3568Total 493.52184 10121 .048762162 Root MSE = .1771leverage Coef. Std. Err. t Pt 95% Conf. Intervalq .0097995 .0012353 7.93 0.000 .007378 .012221profit -.8495927 .0280139 -30.33 0.000 -.9045055 -.7946799profit_2 .4402733 .0209949 20.97 0.000

30、 .3991192 .4814275fratio .3651988 .0095655 38.18 0.000 .3464485 .3839492lna .0673593 .0015058 44.73 0.000 .0644078 .0703109_cons -1.131486 .0328249 -34.47 0.000 -1.19583 -1.067143. sum profitVariable Obs Mean Std. Dev. Min Maxprofit 10122 .0613722 .0806199 -.6430355 2.645649. display (-.8495927+2*.4

31、402733*.0613722)*.0806199-.06413729. display -1*(-.8495927)/(2*.4402733).96484695. . gen q_profit=q*profit. reg leverage q profit q_profit fratio lnaSource SS df MS Number of obs = 10122F( 5, 10116) = 1024.71Model 165.922728 5 33.1845455 Prob F = 0.0000Residual 327.599112 10116 .032384254 R-squared

32、= 0.3362Adj R-squared = 0.3359Total 493.52184 10121 .048762162 Root MSE = .17996leverage Coef. Std. Err. t Pt 95% Conf. Intervalq .0086458 .0012666 6.83 0.000 .0061631 .0111286profit -.7199138 .0315488 -22.82 0.000 -.7817558 -.6580718q_profit .0531124 .0051322 10.35 0.000 .0430523 .0631725fratio .37

33、45416 .0097085 38.58 0.000 .3555111 .3935721lna .0654486 .0015264 42.88 0.000 .0624566 .0684405_cons -1.101763 .033317 -33.07 0.000 -1.167071 -1.036455. sum profitVariable Obs Mean Std. Dev. Min Maxprofit 10122 .0613722 .0806199 -.6430355 2.645649. sum qVariable Obs Mean Std. Dev. Min Maxq 10122 1.8

34、25711 1.541244 .283239 44.53. display (.0086458+.0531124*.0613722)*1.541244.01834916. . reg leverage q profit fratio lna Source SS df MS Number of obs = 10122F( 4, 10117) = 1241.10Model 162.454363 4 40.6135909 Prob F = 0.0000Residual 331.067476 10117 .032723878 R-squared = 0.3292Adj R-squared = 0.32

35、89Total 493.52184 10121 .048762162 Root MSE = .1809leverage Coef. Std. Err. t Pt 95% Conf. Intervalq .0104224 .0012615 8.26 0.000 .0079497 .0128951profit -.4919057 .0226995 -21.67 0.000 -.5364012 -.4474102fratio .3803813 .0097427 39.04 0.000 .3612837 .399479lna .0646686 .0015325 42.20 0.000 .0616646

36、 .0676725_cons -1.097821 .033489 -32.78 0.000 -1.163466 -1.032175. predict pre_leverage, xb. gen pre_leverage_2=pre_leverage2. reg leverage q profit fratio lna pre_leverage_2Source SS df MS Number of obs = 10122F( 5, 10116) = 1014.85Model 164.859349 5 32.9718698 Prob F = 0.0000Residual 328.662491 10

37、116 .032489372 R-squared = 0.3340Adj R-squared = 0.3337Total 493.52184 10121 .048762162 Root MSE = .18025leverage Coef. Std. Err. t Pt 95% Conf. Intervalq .0056752 .0013727 4.13 0.000 .0029844 .008366profit -.3891551 .0255773 -15.21 0.000 -.4392918 -.3390185fratio .217856 .0212386 10.26 0.000 .17622

38、41 .2594879lna .0359964 .0036657 9.82 0.000 .0288108 .0431819pre_levera2 .4584789 .0532885 8.60 0.000 .3540228 .562935_cons -.5069118 .0763578 -6.64 0.000 -.6565883 -.3572353. . *第六章. reg leverage q profit fratio lnaSource SS df MS Number of obs = 10122F( 4, 10117) = 1241.10Model 162.454363 4 40.613

39、5909 Prob F = 0.0000Residual 331.067476 10117 .032723878 R-squared = 0.3292Adj R-squared = 0.3289Total 493.52184 10121 .048762162 Root MSE = .1809leverage Coef. Std. Err. t Pt 95% Conf. Intervalq .0104224 .0012615 8.26 0.000 .0079497 .0128951profit -.4919057 .0226995 -21.67 0.000 -.5364012 -.4474102

40、fratio .3803813 .0097427 39.04 0.000 .3612837 .399479lna .0646686 .0015325 42.20 0.000 .0616646 .0676725_cons -1.097821 .033489 -32.78 0.000 -1.163466 -1.032175. reg leverage q profit fratioSource SS df MS Number of obs = 10122F( 3, 10118) = 902.47Model 104.181387 3 34.7271289 Prob F = 0.0000Residua

41、l 389.340453 10118 .038479982 R-squared = 0.2111Adj R-squared = 0.2109Total 493.52184 10121 .048762162 Root MSE = .19616leverage Coef. Std. Err. t Pt 95% Conf. Intervalq -.0085362 .0012782 -6.68 0.000 -.0110418 -.0060306profit -.3857851 .0244635 -15.77 0.000 -.4337385 -.3378317fratio .4707303 .01030

42、67 45.67 0.000 .4505272 .4909334_cons .2981022 .0056612 52.66 0.000 .2870051 .3091993. corr q profit fratio lna(obs=10122)q profit fratio lnaq 1.0000profit 0.1024 1.0000fratio -0.1111 -0.1206 1.0000lna -0.3615 0.0415 0.2332 1.0000. . reg leverage q profit fratio lnaSource SS df MS Number of obs = 10

43、122F( 4, 10117) = 1241.10Model 162.454363 4 40.6135909 Prob F = 0.0000Residual 331.067476 10117 .032723878 R-squared = 0.3292Adj R-squared = 0.3289Total 493.52184 10121 .048762162 Root MSE = .1809leverage Coef. Std. Err. t Pt 95% Conf. Intervalq .0104224 .0012615 8.26 0.000 .0079497 .0128951profit -

44、.4919057 .0226995 -21.67 0.000 -.5364012 -.4474102fratio .3803813 .0097427 39.04 0.000 .3612837 .399479lna .0646686 .0015325 42.20 0.000 .0616646 .0676725_cons -1.097821 .033489 -32.78 0.000 -1.163466 -1.032175. estat vifVariable VIF 1/VIF lna 1.22 0.821395q 1.17 0.855360fratio 1.08 0.928478profit 1.04 0.965439Mean VIF 1.12. gen lna_1=lna. reg leverage q profit fratio lna lna_1note: lna_1 omitted because of c