我有2000-2016的数据，我正在尝试使用交互式假人估计以下回归。
xtset id
xtreg lnp i.year i.year#fp, fe vce(robust)
但是当我这样做时，由于共线性，Stata省略了2008：有没有办法指定哪一年被省略？
答案 0 :(得分：1)
通常，您可以指定因子变量的省略级别（即
基本））（使用ib运算符（另请参见help fvvarlist）。
以下是使用Stata玩具数据集nlswork的可重现示例：
webuse nlswork, clear
xtset idcode
使用77作为基准年：
xtreg ln_wage ib77.year age, fe vce(robust)
Fixed-effects (within) regression Number of obs = 28,510
Group variable: idcode Number of groups = 4,710
R-sq: Obs per group:
within = 0.1060 min = 1
between = 0.0914 avg = 6.1
overall = 0.0805 max = 15
F(15,4709) = 69.49
corr(u_i, Xb) = 0.0467 Prob > F = 0.0000
(Std. Err. adjusted for 4,710 clusters in idcode)
------------------------------------------------------------------------------
| Robust
ln_wage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
year |
68 | -.108365 .1111117 -0.98 0.329 -.3261959 .1094659
69 | -.0335029 .0995142 -0.34 0.736 -.2285973 .1615915
70 | -.0604953 .0867605 -0.70 0.486 -.2305866 .1095959
71 | -.0218073 .0742761 -0.29 0.769 -.1674232 .1238087
72 | -.0226893 .0622792 -0.36 0.716 -.1447857 .0994071
73 | -.0203581 .049851 -0.41 0.683 -.1180894 .0773732
75 | -.0305043 .0259707 -1.17 0.240 -.081419 .0204104
78 | .0225868 .0147272 1.53 0.125 -.0062854 .0514591
80 | .0058999 .0381391 0.15 0.877 -.0688706 .0806704
82 | .0006801 .0622403 0.01 0.991 -.1213399 .1227001
83 | .0127622 .074435 0.17 0.864 -.1331653 .1586897
85 | .0381987 .0989316 0.39 0.699 -.1557535 .2321508
87 | .0298993 .1237839 0.24 0.809 -.2127751 .2725736
88 | .0716091 .1397635 0.51 0.608 -.2023927 .345611
|
age | .0125992 .0123091 1.02 0.306 -.0115323 .0367308
_cons | 1.312096 .3453967 3.80 0.000 .6349571 1.989235
-------------+----------------------------------------------------------------
sigma_u | .4058746
sigma_e | .30300411
rho | .64212421 (fraction of variance due to u_i)
------------------------------------------------------------------------------
使用80作为基准年：
xtreg ln_wage ib80.year age, fe vce(robust)
Fixed-effects (within) regression Number of obs = 28,510
Group variable: idcode Number of groups = 4,710
R-sq: Obs per group:
within = 0.1060 min = 1
between = 0.0914 avg = 6.1
overall = 0.0805 max = 15
F(15,4709) = 69.49
corr(u_i, Xb) = 0.0467 Prob > F = 0.0000
(Std. Err. adjusted for 4,710 clusters in idcode)
------------------------------------------------------------------------------
| Robust
ln_wage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
year |
68 | -.1142649 .1480678 -0.77 0.440 -.4045471 .1760172
69 | -.0394028 .136462 -0.29 0.773 -.3069323 .2281266
70 | -.0663953 .1237179 -0.54 0.592 -.3089402 .1761497
71 | -.0277072 .1112026 -0.25 0.803 -.2457164 .190302
72 | -.0285892 .0991208 -0.29 0.773 -.2229124 .165734
73 | -.026258 .0866489 -0.30 0.762 -.1961303 .1436142
75 | -.0364042 .0625743 -0.58 0.561 -.1590791 .0862706
77 | -.0058999 .0381391 -0.15 0.877 -.0806704 .0688706
78 | .0166869 .0258678 0.65 0.519 -.0340261 .0673999
82 | -.0052198 .0257713 -0.20 0.840 -.0557437 .0453041
83 | .0068623 .0378166 0.18 0.856 -.0672759 .0810005
85 | .0322987 .0620538 0.52 0.603 -.0893558 .1539533
87 | .0239993 .0868397 0.28 0.782 -.1462471 .1942457
88 | .0657092 .1028815 0.64 0.523 -.1359868 .2674052
|
age | .0125992 .0123091 1.02 0.306 -.0115323 .0367308
_cons | 1.317996 .3824809 3.45 0.001 .5681546 2.067838
-------------+----------------------------------------------------------------
sigma_u | .4058746
sigma_e | .30300411
rho | .64212421 (fraction of variance due to u_i)
------------------------------------------------------------------------------