stata程序及运行结果

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% . -7.5% . .10% . . Obs 1039725% 1.12204 . Sum of Wgt. 1039750% 1.4441 Mean 4.9

2、8767Largest Std. Dev. 184.905575% 2. 955.415890% 3. 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 Smallest1% . .5% . .10% . . Obs 1037225% . . Sum of Wgt. 103

3、7250% . Mean .Largest Std. Dev. 1.75% . 41.9393890% . 41.93938 Variance 2.95% . 96.95931 Skewness 42.3892699% 1.6956 96.95931 Kurtosis 2225.629. drop if leverage1 leverage0(232 observations deleted). sum profit, dprofitPercentiles Smallest1% -.1462 -2.5% -. -1.10% . -1. Obs 1014025% . -. Sum of Wgt.

4、 1014050% . Mean .Largest Std. Dev. .75% . 7.90% . 7. Variance .95% . 8. Skewness 60.5911999% . 22.00289 Kurtosis 4581.194. drop if profit20(4 observations deleted). sum fratio, dfratioPercentiles Smallest1% . 05% . 010% . 0 Obs 1013625% . 0 Sum of Wgt. 1013650% . Mean .Largest Std. Dev. .75% . .90%

5、 . . Variance .95% . . Skewness .99% . . Kurtosis 2. drop if fratio F = 0.0000Residual 486. 10120 . R-squared = 0.0142Adj R-squared = 0.0142Total 493.52184 10121 . Root MSE = .21925leverage Coef. Std. Err. t Pt 95% Conf. Intervalq -. . -12.10 0.000 -. -._cons . . 142.91 0.000 . . reg leverage q prof

6、it fratio lnaSource SS df MS Number of obs = 10122F( 4, 10117) = 1241.10Model 162. 4 40. Prob F = 0.0000Residual 331. 10117 . R-squared = 0.3292Adj R-squared = 0.3289Total 493.52184 10121 . Root MSE = .1809leverage Coef. Std. Err. t Pt 95% Conf. Intervalq . . 8.26 0.000 . .profit -. . -21.67 0.000 -

7、. -.fratio . . 39.04 0.000 . .lna . . 42.20 0.000 . ._cons -1. . -32.78 0.000 -1. -1. 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

8、 SS df MS Number of obs = 10122F( 4, 10117) = 1241.10Model 162. 4 40. Prob F = 0.0000Residual 331. 10117 . R-squared = 0.3292Adj R-squared = 0.3289Total 493.52184 10121 . Root MSE = .1809leverage Coef. Std. Err. t Pt 95% Conf. Intervalq . . 8.26 0.000 . .profit -. . -21.67 0.000 -. -.fratio . . 39.0

9、4 0.000 . .lna . . 42.20 0.000 . ._cons -1. . -32.78 0.000 -1. -1. ereturn listscalars:e(N) = 10122e(df_m) = 4e(df_r) = 10117e(F) = 1241.2e(r2) = .4022e(rmse) = .86086e(mss) = 162.55e(rss) = 331.05e(r2_a) = .68228e(ll) = 2946.8e(ll_0) = 926.34e(rank) = 5macros:e(cmdline) : regress leverage q profit

10、fratio lnae(title) : Linear regressione(marginsok) : XB defaulte(vce) : olse(depvar) : leveragee(cmd) : regresse(properties) : b Ve(predict) : regres_pe(model) : olse(estat_cmd) : regress_estatmatrices:e(b) : 1 x 5e(V) : 5 x 5functions:e(sample) . gen r2ur=e(r2). reg leverage fratio lnaSource SS df

11、MS Number of obs = 10122F( 2, 10119) = 2126.58Model 146. 2 73. Prob F = 0.0000Residual 347. 10119 . R-squared = 0.2959Adj R-squared = 0.2958Total 493.52184 10121 . Root MSE = .18531leverage Coef. Std. Err. t Pt 95% Conf. Intervalfratio . . 41.11 0.000 . .lna . . 39.65 0.000 . ._cons -. . -31.31 0.00

12、0 -1. -. ereturn listscalars:e(N) = 10122e(df_m) = 2e(df_r) = 10119e(F) = 2126.1e(r2) = .006e(rmse) = .35181e(mss) = 146.94e(rss) = 347.65e(r2_a) = .84719e(ll) = 2702.1e(ll_0) = 926.34e(rank) = 3macros:e(cmdline) : regress leverage fratio lnae(title) : Linear regressione(marginsok) : XB defaulte(vce

13、) : olse(depvar) : leveragee(cmd) : regresse(properties) : b Ve(predict) : regres_pe(model) : olse(estat_cmd) : regress_estatmatrices: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 lnaSour

14、ce SS df MS Number of obs = 10122F( 4, 10117) = 1241.10Model 162. 4 40. Prob F = 0.0000Residual 331. 10117 . R-squared = 0.3292Adj R-squared = 0.3289Total 493.52184 10121 . Root MSE = .1809leverage Coef. Std. Err. t Pt 95% Conf. Intervalq . . 8.26 0.000 . .profit -. . -21.67 0.000 -. -.fratio . . 39

15、.04 0.000 . .lna . . 42.20 0.000 . ._cons -1. . -32.78 0.000 -1. -1. ereturn listscalars:e(N) = 10122e(df_m) = 4e(df_r) = 10117e(F) = 1241.2e(r2) = .4022e(rmse) = .86086e(mss) = 162.55e(rss) = 331.05e(r2_a) = .68228e(ll) = 2946.8e(ll_0) = 926.34e(rank) = 5macros:e(cmdline) : regress leverage q profi

16、t fratio lnae(title) : Linear regressione(marginsok) : XB defaulte(vce) : olse(depvar) : leveragee(cmd) : regresse(properties) : b Ve(predict) : regres_pe(model) : olse(estat_cmd) : regress_estatmatrices:e(b) : 1 x 5e(V) : 5 x 5functions:e(sample) . gen r2ur=e(r2). reg leverage lnaSource SS df MS Nu

17、mber of obs = 10122F( 1, 10120) = 2196.68Model 88. 1 88. Prob F = 0.0000Residual 405. 10120 . R-squared = 0.1784Adj R-squared = 0.1783Total 493.52184 10121 . Root MSE = .20017leverage Coef. Std. Err. t Pt 95% Conf. Intervallna . . 46.87 0.000 . ._cons -1. . -33.28 0.000 -1. -1. ereturn listscalars:e

18、(N) = 10122e(df_m) = 1e(df_r) = 10120e(F) = 2196.4e(r2) = .23137e(rmse) = .42292e(mss) = 88.104e(rss) = 405.49e(r2_a) = .11371e(ll) = 1920.e(ll_0) = 926.34e(rank) = 2macros:e(cmdline) : regress leverage lnae(title) : Linear regressione(marginsok) : XB defaulte(vce) : olse(depvar) : leveragee(cmd) :

19、regresse(properties) : b Ve(predict) : regres_pe(model) : olse(estat_cmd) : regress_estatmatrices: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). gen lnleverage=ln(leverage). gen lnprofit=ln(profit)(770 missing

20、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. 4 40. Prob F = 0.0000Residual 331. 10117 . R-squared = 0.3292Adj R-squared = 0.3289Total 493.52184 10121 . Root MSE = .1809leverage Coef. Std. Err. t Pt

21、95% Conf. Intervalq . . 8.26 0.000 . .profit -. . -21.67 0.000 -. -.fratio . . 39.04 0.000 . .lna . . 42.20 0.000 . ._cons -1. . -32.78 0.000 -1. -1. display .*1. reg lnleverage lnq lnprofit lnfratio lnaSource SS df MS Number of obs = 9352F( 4, 9347) = 1165.37Model 1549.75886 4 387. Prob F = 0.0000R

22、esidual 3107.51249 9347 . R-squared = 0.3328Adj R-squared = 0.3325Total 4657.27136 9351 . Root MSE = .57659lnleverage Coef. Std. Err. t Pt 95% Conf. Intervallnq . . 8.60 0.000 . .lnprofit -. . -18.26 0.000 -. -.lnfratio . . 40.77 0.000 . .lna . . 39.11 0.000 . ._cons -5. . -44.58 0.000 -5. -5. . gen

23、 profit_2=profit2. reg leverage q profit profit_2 fratio lnaSource SS df MS Number of obs = 10122F( 5, 10116) = 1123.89Model 176. 5 35. Prob F = 0.0000Residual 317. 10116 . R-squared = 0.3571Adj R-squared = 0.3568Total 493.52184 10121 . Root MSE = .1771leverage Coef. Std. Err. t Pt 95% Conf. Interva

24、lq . . 7.93 0.000 . .profit -. . -30.33 0.000 -. -.profit_2 . . 20.97 0.000 . .fratio . . 38.18 0.000 . .lna . . 44.73 0.000 . ._cons -1. . -34.47 0.000 -1.19583 -1. sum profitVariable Obs Mean Std. Dev. Min Maxprofit 10122 . . -. 2. display (-.+2*.*.)*.-. display -1*(-.)/(2*.). . gen q_profit=q*pro

25、fit. reg leverage q profit q_profit fratio lnaSource SS df MS Number of obs = 10122F( 5, 10116) = 1024.71Model 165. 5 33. Prob F = 0.0000Residual 327. 10116 . R-squared = 0.3362Adj R-squared = 0.3359Total 493.52184 10121 . Root MSE = .17996leverage Coef. Std. Err. t Pt 95% Conf. Intervalq . . 6.83 0

26、.000 . .profit -. . -22.82 0.000 -. -.q_profit . . 10.35 0.000 . .fratio . . 38.58 0.000 . .lna . . 42.88 0.000 . ._cons -1. . -33.07 0.000 -1. -1. sum profitVariable Obs Mean Std. Dev. Min Maxprofit 10122 . . -. 2. sum qVariable Obs Mean Std. Dev. Min Maxq 10122 1. 1. . 44.53. display (.+.*.)*1. .

27、reg leverage q profit fratio lna Source SS df MS Number of obs = 10122F( 4, 10117) = 1241.10Model 162. 4 40. Prob F = 0.0000Residual 331. 10117 . R-squared = 0.3292Adj R-squared = 0.3289Total 493.52184 10121 . Root MSE = .1809leverage Coef. Std. Err. t Pt 95% Conf. Intervalq . . 8.26 0.000 . .profit

28、 -. . -21.67 0.000 -. -.fratio . . 39.04 0.000 . .lna . . 42.20 0.000 . ._cons -1. . -32.78 0.000 -1. -1. 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. 5 32. Prob F = 0.

29、0000Residual 328. 10116 . R-squared = 0.3340Adj R-squared = 0.3337Total 493.52184 10121 . Root MSE = .18025leverage Coef. Std. Err. t Pt 95% Conf. Intervalq . . 4.13 0.000 . .profit -. . -15.21 0.000 -. -.fratio . . 10.26 0.000 . .lna . . 9.82 0.000 . .pre_levera2 . . 8.60 0.000 . ._cons -. . -6.64

30、0.000 -. -. . *第六章. reg leverage q profit fratio lnaSource SS df MS Number of obs = 10122F( 4, 10117) = 1241.10Model 162. 4 40. Prob F = 0.0000Residual 331. 10117 . R-squared = 0.3292Adj R-squared = 0.3289Total 493.52184 10121 . Root MSE = .1809leverage Coef. Std. Err. t Pt 95% Conf. Intervalq . . 8

31、.26 0.000 . .profit -. . -21.67 0.000 -. -.fratio . . 39.04 0.000 . .lna . . 42.20 0.000 . ._cons -1. . -32.78 0.000 -1. -1. reg leverage q profit fratioSource SS df MS Number of obs = 10122F( 3, 10118) = 902.47Model 104. 3 34. Prob F = 0.0000Residual 389. 10118 . R-squared = 0.2111Adj R-squared = 0

32、.2109Total 493.52184 10121 . Root MSE = .19616leverage Coef. Std. Err. t Pt 95% Conf. Intervalq -. . -6.68 0.000 -. -.profit -. . -15.77 0.000 -. -.fratio . . 45.67 0.000 . ._cons . . 52.66 0.000 . . corr q profit fratio lna(obs=10122)q profit fratio lnaq 1.0000profit 0.1024 1.0000fratio -0.1111 -0.

33、1206 1.0000lna -0.3615 0.0415 0.2332 1.0000. . reg leverage q profit fratio lnaSource SS df MS Number of obs = 10122F( 4, 10117) = 1241.10Model 162. 4 40. Prob F = 0.0000Residual 331. 10117 . R-squared = 0.3292Adj R-squared = 0.3289Total 493.52184 10121 . Root MSE = .1809leverage Coef. Std. Err. t P

34、t 95% Conf. Intervalq . . 8.26 0.000 . .profit -. . -21.67 0.000 -. -.fratio . . 39.04 0.000 . .lna . . 42.20 0.000 . ._cons -1. . -32.78 0.000 -1. -1. estat vifVariable VIF 1/VIF lna 1.22 0.q 1.17 0.fratio 1.08 0.profit 1.04 0.Mean VIF 1.12. gen lna_1=lna. reg leverage q profit fratio lna lna_1note

35、: lna_1 omitted because of collinearitySource SS df MS Number of obs = 10122F( 4, 10117) = 1241.10Model 162. 4 40. Prob F = 0.0000Residual 331. 10117 . R-squared = 0.3292Adj R-squared = 0.3289Total 493.52184 10121 . Root MSE = .1809leverage Coef. Std. Err. t Pt 95% Conf. Intervalq . . 8.26 0.000 . .

36、profit -. . -21.67 0.000 -. -.fratio . . 39.04 0.000 . .lna . . 42.20 0.000 . .lna_1 (omitted)_cons -1. . -32.78 0.000 -1. -1. replace lna_1=0 in 1/5(5 real changes made). reg leverage q profit fratio lna lna_1Source SS df MS Number of obs = 10122F( 5, 10116) = 992.93Model 162. 5 32. Prob F = 0.0000

37、Residual 331. 10116 . R-squared = 0.3292Adj R-squared = 0.3289Total 493.52184 10121 . Root MSE = .1809leverage Coef. Std. Err. t Pt 95% Conf. Intervalq . . 6.94 0.000 . .profit -. . -21.52 0.000 -. -.fratio . . 39.05 0.000 . .lna . . 12.64 0.000 . .lna_1 -. . -0.71 0.475 -. ._cons -1. . -32.33 0.000 -1.16065 -1.02794. estat vifVariable VIF 1/VIF lna_1 16.93 0.lna 15.16 0.q 1.51 0.fratio 1.08 0.profit 1.04 0.Mean VIF 7.14. . reg lna_1 q profit fratio lnaSource SS df MS Number of obs = 10122F( 4, 10117) =40285.00Model 18079.0202 4 4519.