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雷达卡
黃河泉 发表于 2017-8-20 10:57
请把指令发出来！你到底要问什么？
xthreg lncs lninc, rx(lnhw) qx(lnyue) thnum(1) bs(300) trim(0.01) grid(10
> 0)
Estimating the threshold parameters: 1st ...... Done
Boostrap for single threshold
.................................................. + 50
.................................................. + 100
.................................................. + 150
.................................................. + 200
.................................................. + 250
.................................................. + 300
Threshold estimator (level = 95):
-----------------------------------------------------
model | Threshold Lower Upper
-----------+-----------------------------------------
Th-1 | 0.6553 0.6303 0.6643
-----------------------------------------------------
Threshold effect test (bootstrap = 300):
-----------------------------------------------------------------------
Threshold | RSS MSE Fstat Prob Crit10 Crit5
-----------+-----------------------------------------------------------
Single | 2.2006 0.0050 29.97 0.0033 17.6358 21.5110
-----------------------------------------------------------------------
--------------------
Threshold | Crit1
-----------+--------
Single | 25.4068
--------------------
Fixed-effects (within) regression Number of obs =
> 455
Group variable: city Number of groups =
> 35
R-sq: within = 0.9420 Obs per group: min =
> 13
between = 0.6695 avg = 1
> 3.0
overall = 0.8208 max =
> 13
F(3,417) = 2259
> .27
corr(u_i, Xb) = -0.4265 Prob > F = 0.0
> 000
---------------------------------------------------------------------------
> ---
lncs | Coef. Std. Err. t P>|t| [95% Conf. Interv
> al]
-------------+-------------------------------------------------------------
> ---
lninc | .6587557 .0282209 23.34 0.000 .6032827 .7142
> 287
|
_cat#c.lnhw |
0 | .1990422 .0249937 7.96 0.000 .1499129 .2481
> 714
1 | .2052844 .0252257 8.14 0.000 .1556989 .2548
> 699
|
_cons | 1.295394 .118624 10.92 0.000 1.062218 1.528
> 569
-------------+-------------------------------------------------------------
> ---
sigma_u | .14364899
sigma_e | .07254307
rho | .79679538 (fraction of variance due to u_i)
---------------------------------------------------------------------------
> ---
F test that all u_i=0: F(34, 417) = 35.40 Prob > F = 0.0
> 000
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