VIF
3.9761 43
. regress lprice lnox ldist rooms stratio
Source | SS df MS Number of obs = 506 ---+--- F(4, 501) = 175.86 Model | 49.3987735 4 12.3496934 Prob > F = 0.0000 Residual | 35.1834974 501 .070226542 R-squared = 0.5840 ---+--- Adj R-squared = 0.5807 Total | 84.5822709 505 .167489645 Root MSE = .265 lprice | Coef. Std. Err. t P>|t| [95% Conf. Interval]
lnox | -.95354 .1167418 -8.17 0.000 -1.182904 -.7241762 ldist | -.1343401 .0431032 -3.12 0.002 -.2190255 -.0496548 rooms | .2545271 .0185303 13.74 0.000 .2181203 .2909338 stratio | -.0524512 .0058971 -8.89 0.000 -.0640373 -.0408651 _cons | 11.08387 .3181115 34.84 0.000 10.45887 11.70886 ---F test
. test lnox ( 1) lnox = 0
F( 1, 501) = 66.72 Prob > F = 0.0000 test lnox ldist
( 1) lnox = 0 ( 2) ldist = 0
F( 2, 501) = 58.95 Prob > F = 0.0000 . test lnox = ldist
( 1) lnox - ldist = 0
F( 1, 501) = 96.40 Prob > F = 0.0000
. test lnox + ldist = 1 ( 1) lnox + ldist = 1
F( 1, 501) = 181.55 Prob > F = 0.0000 . testparm lnox
( 1) lnox = 0
F( 1, 501) = 66.72 Prob > F = 0.0000 . testparm lnox ldist
( 1) lnox = 0 ( 2) ldist = 0
F( 2, 501) = 58.95 Prob > F = 0.0000 . test lnox = 2*ldist
( 1) lnox - 2*ldist = 0 F( 1, 501) = 116.96 Prob > F = 0.0000 Pengujian Hetero
predict ehat, resid
. twoway (scatter ehat lprice)
-1-.50.511.5Residuals
8.5 9 9.5 10 10.5 11
log(price)
. hettest
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance
Variables: fitted values of lprice chi2(1) = 127.21
Prob > chi2 = 0.0000 . hettest lnox ldist rooms stratio
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance
Variables: lnox ldist rooms stratio chi2(4) = 236.55
Prob > chi2 = 0.0000 . hettest lprice
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance
Variables: lprice chi2(1) = 65.52 Prob > chi2 = 0.0000
. imtest
Cameron & Trivedi's decomposition of IM-test
Source | chi2 df p Heteroskedasticity | 143.98 14 0.0000 Skewness | 16.99 4 0.0019 Kurtosis | 11.30 1 0.0008 Total | 172.26 19 0.0000 ---. imtest, white
White's test for Ho: homoskedasticity
against Ha: unrestricted heteroskedasticity chi2(14) = 143.98
Prob > chi2 = 0.0000
Cameron & Trivedi's decomposition of IM-test
Source | chi2 df p Heteroskedasticity | 143.98 14 0.0000 Skewness | 16.99 4 0.0019 Kurtosis | 11.30 1 0.0008 Total | 172.26 19 0.0000 ---.
. regress lprice lnox ldist rooms stratio [weight =lprice]
(analytic weights assumed) (sum of wgt is 5.0302e+03)
Source | SS df MS Number of obs = 506 ---+--- F(4, 501) = 178.20 Model | 48.9305968 4 12.2326492 Prob > F = 0.0000 Residual | 34.3918667 501 .06864644 R-squared = 0.5872 ---+--- Adj R-squared = 0.5839 Total | 83.3224635 505 .164994977 Root MSE = .262 lprice | Coef. Std. Err. t P>|t| [95% Conf. Interval]
lnox | -.9627375 .1156753 -8.32 0.000 -1.190006 -.7354691 ldist | -.1503326 .0425837 -3.53 0.000 -.2339971 -.066668 rooms | .2567455 .0182026 14.10 0.000 .2209828 .2925083 stratio | -.0511989 .0058156 -8.80 0.000 -.0626249 -.0397729 _cons | 11.0885 .3151686 35.18 0.000 10.46928 11.70771
---Regresi dengan pembatas
. constraint def 1 lnox ldist + rooms + stratio = 0 . cnsreg lprice lnox ldist rooms stratio, constraint(1)
Constrained linear regression Number of obs = 506 F( 3, 502) = 187.67 Prob > F = 0.0000 Root MSE = 0.2818 ( 1) lnox = 0
lprice | Coef. Std. Err. t P>|t| [95% Conf. Interval]
lnox | 0 (omitted)
ldist | .1638875 .024361 6.73 0.000 .1160255 .2117495 rooms | .2792103 .0194421 14.36 0.000 .2410124 .3174082 stratio | -.052383 .0062712 -8.35 0.000 -.0647041 -.040062 _cons | 8.958702 .1946402 46.03 0.000 8.576293 9.341112
---Pernyataan batas
. constraint def 2 lnox + ldist + rooms + stratio = 1 .
. . cnsreg lprice lnox ldist rooms stratio, constraint(2)
Constrained linear regression Number of obs = 506 Root MSE = 0.2996 ( 1) lnox + ldist + rooms + stratio = 1
lprice | Coef. Std. Err. t P>|t| [95% Conf. Interval]
lnox | .4080539 .0241287 16.91 0.000 .3606483 .4554595 ldist | .3263141 .0211547 15.43 0.000 .2847514 .3678767 rooms | .3112691 .0202413 15.38 0.000 .2715011 .3510371 stratio | -.0456371 .0066358 -6.88 0.000 -.0586745 -.0325997 _cons | 7.748845 .1683279 46.03 0.000 7.418131 8.079559 ---. constraint def 3 lnox + ldist + rooms + stratio = 2
.
. . cnsreg lprice lnox ldist rooms stratio, constraint(3)
Constrained linear regression Number of obs = 506 Root MSE = 0.3409 ( 1) lnox + ldist + rooms + stratio = 2
lprice | Coef. Std. Err. t P>|t| [95% Conf. Interval]
lnox | 1.130077 .02745 41.17 0.000 1.076146 1.184008 ldist | .5705888 .0240667 23.71 0.000 .5233049 .6178726 rooms | .3413582 .0230275 14.82 0.000 .296116 .3866003 stratio | -.0420238 .0075493 -5.57 0.000 -.0568558 -.0271918 _cons | 5.980358 .1914985 31.23 0.000 5.604121 6.356596 ---Pengujian nonnested models
. regres lprice lnox ldist rooms stratio
Source | SS df MS Number of obs = 506 ---+--- F(4, 501) = 175.86 Model | 49.3987735 4 12.3496934 Prob > F = 0.0000 Residual | 35.1834974 501 .070226542 R-squared = 0.5840 ---+--- Adj R-squared = 0.5807 Total | 84.5822709 505 .167489645 Root MSE = .265 lprice | Coef. Std. Err. t P>|t| [95% Conf. Interval]
lnox | -.95354 .1167418 -8.17 0.000 -1.182904 -.7241762 ldist | -.1343401 .0431032 -3.12 0.002 -.2190255 -.0496548 rooms | .2545271 .0185303 13.74 0.000 .2181203 .2909338 stratio | -.0524512 .0058971 -8.89 0.000 -.0640373 -.0408651 _cons | 11.08387 .3181115 34.84 0.000 10.45887 11.70886 ---. regress lprice crime proptax ldist rooms stratio
Source | SS df MS Number of obs = 506 ---+--- F(5, 500) = 165.33 Model | 52.7041403 5 10.5408281 Prob > F = 0.0000 Residual | 31.8781307 500 .063756261 R-squared = 0.6231 ---+--- Adj R-squared = 0.6193 Total | 84.5822709 505 .167489645 Root MSE = .2525 lprice | Coef. Std. Err. t P>|t| [95% Conf. Interval]
crime | -.012582 .001634 -7.70 0.000 -.0157924 -.0093716 proptax | -.0046291 .0010044 -4.61 0.000 -.0066025 -.0026557 ldist | .004446 .0271272 0.16 0.870 -.0488514 .0577433 rooms | .2663099 .0174591 15.25 0.000 .2320076 .3006122 stratio | -.0324209 .0060618 -5.35 0.000 -.0443307 -.0205111 _cons | 9.095153 .1756747 51.77 0.000 8.750002 9.440305 ---. nnest lprice lnox ldist rooms stratio (crime proptax ldist rooms stratio) Competing Models
M1 : Y = [lprice]
X = [crime proptax ldist rooms stratio]
M2 : Y = [lprice]
Z = [lprice lnox ldist rooms stratio crime proptax ldist rooms stratio]
J test for non-nested models Dist Stat P>|Stat|
H0:M1 / H1:M2 t(499) . . H0:M2 / H1:M1 t(498) . . Cox-Pesaran test for non-nested models Dist Stat P>|Stat|
H0:M1 / H1:M2 N(0,1) . . H0:M2 / H1:M1 N(0,1) . . ---.Misspecification of the functional form
Ramsey’s RESET . estat ovtest
Ramsey RESET test using powers of the fitted values of lprice Ho: model has no omitted variables
F(3, 497) = 30.10 Prob > F = 0.0000 . estat ovtest, rhs
Ramsey RESET test using powers of the independent variables Ho: model has no omitted variables
F(15, 485) = 16.65 Prob > F = 0.0000
Pengaruh interaksi
. generate taxschl = lproptax * stratio
. regress lprice lnox ldist proptax stratio taxschl
Source | SS df MS Number of obs = 506 ---+--- F(5, 500) = 81.48 Model | 37.9756099 5 7.59512199 Prob > F = 0.0000 Residual | 46.606661 500 .093213322 R-squared = 0.4490 ---+--- Adj R-squared = 0.4435 Total | 84.5822709 505 .167489645 Root MSE = .30531 lprice | Coef. Std. Err. t P>|t| [95% Conf. Interval]
lnox | -.9863103 .1430775 -6.89 0.000 -1.267418 -.7052031 ldist | -.169819 .0498389 -3.41 0.001 -.2677384 -.0718995 proptax | .0000827 .0071648 0.01 0.991 -.0139941 .0141594 stratio | .0002104 .0857639 0.00 0.998 -.1682917 .1687124 taxschl | -.0114064 .0150355 -0.76 0.448 -.040947 .0181342 _cons | 13.05851 .3762466 34.71 0.000 12.31929 13.79773 ---Testing serial correlation
. use "C:\Users\Windows 10 Gamer.PURWANTO\Desktop\Using Stata\imeus\ukrates.dta", clear
. summarize rs r20 month
Variable | Obs Mean Std. Dev. Min Max rs | 526 7.651513 3.553109 1.561667 16.18 r20 | 526 8.863726 3.224372 3.35 17.18 month | 526 168.5 151.9874 -94 431 . list rs D.rs r20 L.r20 LD.r20 in 1/10
+---+
| D. L. LD.|
| rs rs r20 r20 r20 | |---|
1. | 2.365042 . 4.33 . . | 2. | 2.3175 -.0475416 4.23 4.33 . | 3. | 2.350833 .0333333 4.36 4.23 -.0999999 | 4. | 2.451833 .1009998 4.57 4.36 .1300001 | 5. | 2.466167 .0143335 4.36 4.57 .21 | |---|
6. | 2.468417 .00225 4.11 4.36 -.21 | 7. | 2.485583 .0171666 4.2 4.11 -.25 | 8. | 2.415125 -.0704584 4.19 4.2 .0899997 | 9. | 2.389875 -.02525 4.15 4.19 -.0099998 | 10. | 2.418167 .0282917 4.22 4.15 -.04 | +---+
. regress D.rs LD.r20
Source | SS df MS Number of obs = 524 ---+--- F(1, 522) = 52.88 Model | 13.8769739 1 13.8769739 Prob > F = 0.0000 Residual | 136.988471 522 .262430021 R-squared = 0.0920 ---+--- Adj R-squared = 0.0902 Total | 150.865445 523 .288461654 Root MSE = .51228 D.rs | Coef. Std. Err. t P>|t| [95% Conf. Interval]
r20 |
LD. | .4882883 .0671484 7.27 0.000 .356374 .6202027 |
_cons | .0040183 .022384 0.18 0.858 -.0399555 .0479921 ---. predict ehat, residual
(2 missing values generated) . wntestq ehat
Portmanteau test for white noise
Portmanteau (Q) statistic = 82.3882 Prob > chi2(40) = 0.0001
. use "C:\Users\Windows 10 Gamer.PURWANTO\Desktop\Using Stata\imeus\ukrates.dta", clear
. regress D.rs LD.r20
Source | SS df MS Number of obs = 524 ---+--- F(1, 522) = 52.88 Model | 13.8769739 1 13.8769739 Prob > F = 0.0000 Residual | 136.988471 522 .262430021 R-squared = 0.0920 ---+--- Adj R-squared = 0.0902 Total | 150.865445 523 .288461654 Root MSE = .51228 D.rs | Coef. Std. Err. t P>|t| [95% Conf. Interval]
r20 |
LD. | .4882883 .0671484 7.27 0.000 .356374 .6202027 |
_cons | .0040183 .022384 0.18 0.858 -.0399555 .0479921 ---. predict ehat, residual
(2 missing values generated) . wntestq ehat
Portmanteau test for white noise
Portmanteau (Q) statistic = 82.3882 Prob > chi2(40) = 0.0001 . prais D.rs LD.r20
Iteration 0: rho = 0.0000
Iteration 1: rho = 0.1488 Iteration 2: rho = 0.1803 Iteration 3: rho = 0.1874 Iteration 4: rho = 0.1891 Iteration 5: rho = 0.1894 Iteration 6: rho = 0.1895 Iteration 7: rho = 0.1895 Iteration 8: rho = 0.1895 Iteration 9: rho = 0.1895
Prais-Winsten AR(1) regression -- iterated estimates
Source | SS df MS Number of obs = 524 ---+--- F(1, 522) = 25.73 Model | 6.56420242 1 6.56420242 Prob > F = 0.0000 Residual | 133.146932 522 .25507075 R-squared = 0.0470 ---+--- Adj R-squared = 0.0452 Total | 139.711134 523 .2671341 Root MSE = .50505 D.rs | Coef. Std. Err. t P>|t| [95% Conf. Interval]
r20 |
LD. | .3495857 .068912 5.07 0.000 .2142067 .4849647 |
_cons | .0049985 .0272145 0.18 0.854 -.0484649 .0584619 rho | .1895324
---Durbin-Watson statistic (original) 1.702273
Durbin-Watson statistic (transformed) 2.007414 .
. mean dpipc, over(state)
Mean estimation Number of obs = 120 CT: state = CT
MA: state = MA ME: state = ME NH: state = NH RI: state = RI VT: state = VT
Over | Mean Std. Err. [95% Conf. Interval]
---+---dpipc |
CT | 22.32587 1.413766 19.52647 25.12527 MA | 19.77681 1.298507 17.20564 22.34798 ME | 15.17391 .9571251 13.27871 17.06911 NH | 18.66835 1.193137 16.30582 21.03088 RI | 17.26529 1.045117 15.19586 19.33473 VT | 15.73786 1.020159 13.71784 17.75788 ---. tabulate state, generate(NE)
state | Freq. Percent Cum.
CT | 20 16.67 16.67 MA | 20 16.67 33.33 ME | 20 16.67 50.00
NH | 20 16.67 66.67 RI | 20 16.67 83.33 VT | 20 16.67 100.00 Total | 120 100.00
. tabulate year, generate(Tahun)
year | Freq. Percent Cum.
1981 | 6 5.00 5.00 1982 | 6 5.00 10.00 1983 | 6 5.00 15.00 1984 | 6 5.00 20.00 1985 | 6 5.00 25.00 1986 | 6 5.00 30.00 1987 | 6 5.00 35.00 1988 | 6 5.00 40.00 1989 | 6 5.00 45.00 1990 | 6 5.00 50.00 1991 | 6 5.00 55.00 1992 | 6 5.00 60.00 1993 | 6 5.00 65.00 1994 | 6 5.00 70.00 1995 | 6 5.00 75.00 1996 | 6 5.00 80.00 1997 | 6 5.00 85.00 1998 | 6 5.00 90.00 1999 | 6 5.00 95.00 2000 | 6 5.00 100.00 Total | 120 100.00
. regress dpipc NE2-NE6
Source | SS df MS Number of obs = 120 ---+--- F(5, 114) = 5.27 Model | 716.218512 5 143.243702 Prob > F = 0.0002 Residual | 3099.85511 114 27.1917115 R-squared = 0.1877 ---+--- Adj R-squared = 0.1521 Total | 3816.07362 119 32.0678456 Root MSE = 5.2146 dpipc | Coef. Std. Err. t P>|t| [95% Conf. Interval]
NE2 | -2.549057 1.648991 -1.55 0.125 -5.815695 .7175814 NE3 | -7.151959 1.648991 -4.34 0.000 -10.4186 -3.88532 NE4 | -3.65752 1.648991 -2.22 0.029 -6.924158 -.3908815 NE5 | -5.060575 1.648991 -3.07 0.003 -8.327214 -1.793937 NE6 | -6.588007 1.648991 -4.00 0.000 -9.854646 -3.321369 _cons | 22.32587 1.166013 19.15 0.000 20.01601 24.63573 ---. mean dpipc, over(NE1 - NE6)
Mean estimation Number of obs = 120 Over: NE1 NE2 NE3 NE4 NE5 NE6
_subpop_1: 0 0 0 0 0 1 _subpop_2: 0 0 0 0 1 0 _subpop_3: 0 0 0 1 0 0 _subpop_4: 0 0 1 0 0 0 _subpop_5: 0 1 0 0 0 0
_subpop_6: 1 0 0 0 0 0
Over | Mean Std. Err. [95% Conf. Interval]
---+---dpipc |
_subpop_1 | 15.73786 1.020159 13.71784 17.75788 _subpop_2 | 17.26529 1.045117 15.19586 19.33473 _subpop_3 | 18.66835 1.193137 16.30582 21.03088 _subpop_4 | 15.17391 .9571251 13.27871 17.06911 _subpop_5 | 19.77681 1.298507 17.20564 22.34798 _subpop_6 | 22.32587 1.413766 19.52647 25.12527
---. use "C:\Users\Windows 10 Gamer---.PURWANTO\Desktop\Using Stata\imeus\nlsw88---.dta", clear (NLSW, 1988 extract)
. summarize
Variable | Obs Mean Std. Dev. Min Max
idcode | 2,246 2612.654 1480.864 1 5159
age | 2,246 39.15316 3.060002 34 46
race | 2,246 1.282725 .4754413 1 3
married | 2,246 .6420303 .4795099 0 1
never_marr~d | 2,246 .1041852 .3055687 0 1
grade | 2,244 13.09893 2.521246 0 18
collgrad | 2,246 .2368655 .4252538 0 1
south | 2,246 .4194123 .4935728 0 1
smsa | 2,246 .7039181 .4566292 0 1
c_city | 2,246 .2916296 .4546139 0 1
industry | 2,232 8.189516 3.010875 1 12
occupation | 2,237 4.642825 3.408897 1 13
union | 1,878 .2454739 .4304825 0 1
wage | 2,246 7.766949 5.755523 1.004952 40.74659 hours | 2,242 37.21811 10.50914 1 80
ttl_exp | 2,246 12.53498 4.610208 .1153846 28.88461 tenure | 2,231 5.97785 5.510331 0 25.91667 . summarize wage race union tenure Variable | Obs Mean Std. Dev. Min Max wage | 2,246 7.766949 5.755523 1.004952 40.74659 race | 2,246 1.282725 .4754413 1 3
union | 1,878 .2454739 .4304825 0 1
tenure | 2,231 5.97785 5.510331 0 25.91667 . summarize wage race union tenure, sep(0) Variable | Obs Mean Std. Dev. Min Max wage | 2,246 7.766949 5.755523 1.004952 40.74659 race | 2,246 1.282725 .4754413 1 3
union | 1,878 .2454739 .4304825 0 1 tenure | 2,231 5.97785 5.510331 0 25.91667 .
. tabulate race, generate(R)
race | Freq. Percent Cum.
white | 1,637 72.89 72.89 black | 583 25.96 98.84 other | 26 1.16 100.00 Total | 2,246 100.00
. generate lwage =ln(wage) . regress lwage R1 R2 union
Source | SS df MS Number of obs = 1,878 ---+--- F(3, 1874) = 38.73 Model | 29.3349228 3 9.77830761 Prob > F = 0.0000 Residual | 473.119209 1,874 .252464893 R-squared = 0.0584 ---+--- Adj R-squared = 0.0569 Total | 502.454132 1,877 .267690001 Root MSE = .50246 lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
R1 | -.0349326 .1035125 -0.34 0.736 -.2379444 .1680793 R2 | -.2133924 .1049954 -2.03 0.042 -.4193126 -.0074721 union | .239083 .0270353 8.84 0.000 .1860606 .2921054 _cons | 1.913178 .1029591 18.58 0.000 1.711252 2.115105 ---. test R1 R2 union
( 1) R1 = 0 ( 2) R2 = 0 ( 3) union = 0
F( 3, 1874) = 38.73 Prob > F = 0.0000
. . use "C:\Users\Windows 10 Gamer.PURWANTO\Desktop\Using Stata\Data POT\phillips_aus.dta", clear
. generate date = tq(1987q1) +_n-1 . format %tq date
. tsset date
time variable: date, 1987q1 to 2009q3 delta: 1 quarter
. . use "C:\Users\Windows 10 Gamer.PURWANTO\Desktop\Using Stata\Data POT\phillips_aus.dta", clear
. generate date = tq(1987q1) +_n-1 . format %tq date
. tsset date
time variable: date, 1987q1 to 2009q3 delta: 1 quarter
. regress inf D.u
Source | SS df MS Number of obs = 90 ---+--- F(1, 88) = 5.29 Model | 2.04834633 1 2.04834633 Prob > F = 0.0238 Residual | 34.0445426 88 .386869802 R-squared = 0.0568 ---+--- Adj R-squared = 0.0460 Total | 36.0928889 89 .405538077 Root MSE = .62199 inf | Coef. Std. Err. t P>|t| [95% Conf. Interval]
u |
D1. | -.5278638 .2294049 -2.30 0.024 -.9837578 -.0719699 |
_cons | .7776213 .0658249 11.81 0.000 .646808 .9084345 ---. predict ehat, res
(1 missing value generated) . ac ehat, lags(12) generate(rk) . list rk in 1/10
+---+
| rk | |---|
1. | .54865864 | 2. | .45573248 | 3. | .43321579 | 4. | .42049358 | 5. | .33903419 | |---|
6. | .27097344 | 7. | .1912208 | 8. | .25069401 | 9. | .15340864 | 10. | .05000152 | +---+
.
-0.40-0.200.000.200.400.60Autocorrelations of ehat
0 5 10 15
Lag
Bartlett's formula for MA(q) 95% confidence bands
. corrgram ehat, lags(10)
-1 0 1 -1 0 1 LAG AC PAC Q Prob>Q [Autocorrelation] [Partial Autocor]
---1 0.5487 0.5498 28.006 0.0000 |---- |---- 2 0.4557 0.2297 47.548 0.0000 |--- |- 3 0.4332 0.1926 65.409 0.0000 |--- |- 4 0.4205 0.1637 82.433 0.0000 |--- |- 5 0.3390 0.0234 93.63 0.0000 |-- | 6 0.2710 -0.0256 100.87 0.0000 |-- | 7 0.1912 -0.0771 104.52 0.0000 |- | 8 0.2507 0.1211 110.86 0.0000 |-- | 9 0.1534 -0.0464 113.27 0.0000 |- | 10 0.0500 -0.1047 113.53 0.0000 | |
Pengujian auto
. estat bgodfrey, lags(1)
Breusch-Godfrey LM test for autocorrelation
lags(p) | chi2 df Prob > chi2 1 | 27.592 1 0.0000 H0: no serial correlation
. estat bgodfrey, lags(5)
Breusch-Godfrey LM test for autocorrelation
lags(p) | chi2 df Prob > chi2 5 | 36.871 5 0.0000 H0: no serial correlation
. newey inf D.u, lag(4)
Regression with Newey-West standard errors Number of obs = 90 maximum lag: 4 F( 1, 88) = 2.76 Prob > F = 0.1001 | Newey-West
inf | Coef. Std. Err. t P>|t| [95% Conf. Interval]
u |
D1. | -.5278638 .3176735 -1.66 0.100 -1.159173 .1034454 |
_cons | .7776213 .1116107 6.97 0.000 .5558184 .9994242
---. estat bgodfrey, lags(1 2 3 4 5)
Breusch-Godfrey LM test for autocorrelation
lags(p) | chi2 df Prob > chi2 1 | 6.483 1 0.0109 2 | 11.993 2 0.0025 3 | 15.136 3 0.0017 4 | 15.151 4 0.0044 5 | 15.485 5 0.0085 H0: no serial correlation
. estat bgodfrey, lags(1 2 3 4 5 6 7 8 9 10) Breusch-Godfrey LM test for autocorrelation
lags(p) | chi2 df Prob > chi2 1 | 6.483 1 0.0109 2 | 11.993 2 0.0025 3 | 15.136 3 0.0017 4 | 15.151 4 0.0044 5 | 15.485 5 0.0085 6 | 16.105 6 0.0132 7 | 16.311 7 0.0224 8 | 17.428 8 0.0260 9 | 17.783 9 0.0378 10 | 17.817 10 0.0581 H0: no serial correlation
. .
forvalues q=1/3 { forvalues p=0/2 {
regress L(0/`p’).D.u L(0/`q’).g if date>=tq(1986q1) display “p=`p’ q=`q”
modelsel }
}