Phụ lục 5
Source | SS | df | MS |
Model | .316497733 | 6 | .052749622 |
Residual | .466940033 | 268 | .001742314 |
Total | .783437765 | 274 | .002859262 |
Có thể bạn quan tâm!
-
Elsas, R. Hackethal Et Al (2010), The Anatomy Of Bank Diversification, Journal Of Banking And Finance, 3496), Pp, 1274-1287. -
Nguyễn Văn Tiến (2010), “Quản Trị Rủi Ro Trong Kinh Doanh Ngân Hàng”. Nhà Xuất Bản Thống Kê. -
Ảnh hưởng của thu nhập phi truyền thống đến khả năng sinh lời và rủi ro của các ngân hàng ở Việt Nam trong giai đoạn 2005-2013 - 13
Xem toàn bộ 118 trang tài liệu này.
Mô hình hồi quy theo biến SDROE Mô hình Pooling OLS
Number of obs = 275
F( 6, 268) = 30.28
Prob > F = 0.0000
= | 0.4040 | |
Adj R-squared | = | 0.3906 |
Root MSE | = | .04174 |
Coef. | Std. Err. | t | P>|t| | [95% Conf. | Interval] | |
lnsize | .003758 | .0020701 | 1.82 | 0.071 | -.0003179 | .0078338 |
nim | 2.007018 | .192024 | 10.45 | 0.000 | 1.628951 | 2.385086 |
lta | 1.530474 | .9986223 | 1.53 | 0.127 | -.4356684 | 3.496617 |
eta | 8.543595 | 2.787823 | 3.06 | 0.002 | 3.054776 | 14.03241 |
cir | -.5494936 | .3569046 | -1.54 | 0.125 | -1.252187 | .1531999 |
gdp | 97.00734 | 22.51403 | 4.31 | 0.000 | 52.68047 | 141.3342 |
_cons | .1139788 | .046323 | 2.46 | 0.015 | .0227756 | .205182 |
Random-effects GLS regression | Number of obs | = | 275 |
Group vari able: code | Number of groups | = | 40 |
R-sq: within = 0.3088 | Obs per group: min | = | 2 |
between = 0.4794 | avg | = | 6.9 |
overall = 0.3973 | max | = | 9 |
Wald chi2(6) | = | 139.18 | |
corr(u_i, X) = 0 (assumed) | Prob > chi2 | = | 0.0000 |
Coef. | Std. Err. | z P>|z| | [95% Conf. | Interval] | |||
lnsize | .0013964 | .002547 | 0.55 0.583 | -.0035955 | .0063884 | ||
nim | 1.815699 | .2028142 | 8.95 0.000 | 1.418191 | 2.213208 | ||
lta | 2.505335 | 1.076542 | 2.33 0.020 | .3953509 | 4.615319 | ||
eta | 6.86291 | 2.823835 | 2.43 0.015 | 1.328295 | 12.39753 | ||
cir | -.2801694 | .4418543 | -0.63 0.526 | -1.146188 | .5858491 | ||
gdp | 96.92326 | 22.6625 | 4.28 0.000 | 52.50558 | 141.3409 | ||
_cons | .1599452 | .0558197 | 2.87 0.004 | .0505406 | .2693498 | ||
sigma_u | .02122615 | ||||||
sigma_e | .03700614 | ||||||
rho | .24755422 | (fraction | of | variance due | to | u_i) |
Kiểm định LM
Breusch and Pagan Lagrangian multiplier test for random effects
sdroe[code,t] = Xb + u[code] + e[code,t]
Estimated results:
Var sd = sqrt(Var)
sdroe .0028593 .0534721
e .0013695 .0370061
u .0004505 .0212262
Test: Var(u) = 0
chibar2(01) = 18.85
Prob > chibar2 = 0.0000
Fixed-effects (within) regression | Number of obs = | 275 |
Group variable: code | Number of groups = | 40 |
R-sq: within = 0.3153 | Obs per group: min = | 2 |
between = 0.4018 | avg = | 6.9 |
overall = 0.3717 | max = | 9 |
F(6,229) = | 17.58 | |
corr(u_i, Xb) = 0.1075 | Prob > F = | 0.0000 |
Coef. | Std. Err. | t P>|t| | [95% Conf. | Interval] | |||
lnsize | -.0009884 | .0033188 | -0.30 0.766 | -.0075277 | .0055509 | ||
nim | 1.635686 | .2256972 | 7.25 0.000 | 1.190977 | 2.080395 | ||
lta | 3.532307 | 1.223668 | 2.89 0.004 | 1.12122 | 5.943393 | ||
eta | 5.713021 | 3.015948 | 1.89 0.059 | -.2295342 | 11.65558 | ||
cir | .0859825 | .565351 | 0.15 0.879 | -1.027972 | 1.199937 | ||
gdp | 95.10219 | 26.27628 | 3.62 0.000 | 43.328 | 146.8764 | ||
_cons | .2059991 | .0724786 | 2.84 0.005 | .063189 | .3488093 | ||
sigma_u | .02893278 | ||||||
sigma_e | .03700614 | ||||||
rho | .37937139 | (fraction | of | variance due | to | u_i) |
F test that all u_i=0: F(39, 229) = 2.87 Prob > F = 0.0000
more
Kiểm định Hausman
Note: the rank of the differenced variance matrix (5) does not equal the number of coefficients being tested (6); be sure this is what you expect, or there may be problems computing the test. Examine the output of your estimators for anything unexpected and possibly consider scaling your variables so that the coefficients are on a similar scale.
Coefficients
(b) fixed | (B) random | (b-B) Difference | sqrt(diag(V_b-V_B)) S.E. | |
lnsize | -.0009884 | .0013964 | -.0023848 | .0021278 |
nim | 1.635686 | 1.815699 | -.180013 | .0990235 |
lta | 3.532307 | 2.505335 | 1.026971 | .5817379 |
eta | 5.713021 | 6.86291 | -1.149889 | 1.059196 |
cir | .0859825 | -.2801694 | .3661519 | .3526847 |
gdp | 95.10219 | 96.92326 | -1.821073 | 13.29865 |
b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test: Ho: difference in coefficients not systematic
chi2(5) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= 10.75
Prob>chi2 = 0.0567
.
Kiểm định đa cộng tuyến
Collinearity Diagnostics
SQRT R-
Variable VIF VIF Tolerance Squared
---------------------------------------------------- lnsize 2.02 1.42 0.4938 0.5062
nim 1.20 1.10 0.8308 0.1692
lta 1.14 1.07 0.8748 0.1252
eta 1.71 1.31 0.5857 0.4143
cir 1.04 1.02 0.9633 0.0367
gdp 1.30 1.14 0.7676 0.2324
---------------------------------------------------- Mean VIF 1.40
Cond
Eigenval Index
---------------------------------
5.0552 | 1.0000 | |
2 | 0.9893 | 2.2604 |
3 | 0.5302 | 3.0878 |
4 | 0.2645 | 4.3721 |
5 | 0.1334 | 6.1564 |
6 | 0.0256 | 14.0543 |
7 | 0.0019 | 51.9155 |
---------------------------------
Condition Number 51.9155
Eigenvalues & Cond Index computed from scaled raw sscp (w/ intercept) Det(correlation matrix) 0.3712
.
Kiểm định tự tương quan của phần dư
Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation
F( 1, 38) = 1.420
Prob > F = 0.2407
Breusch and Pagan Lagrangian multiplier test for random effects
sdroe[code,t] = Xb + u[code] + e[code,t]
Var sd = sqrt(Var | ||
sdroe | .0028593 | .0534721 |
e | .0013695 | .0370061 |
u | .0004505 | .0212262 |
Estimated results:
)
Test: Var(u) = 0
chibar2(01) = 18.85
Prob > chibar2 = 0.0000