Question

我试图检查Stata是否正在使用我之前reg中使用的模型NormalReg（样本模型）中的初始值。但是，在我看来，通过查看迭代0，它没有考虑我的初始值。任何有助于解决此问题的帮助都将受到高度赞赏。

set seed 123
set obs 1000
gen x = runiform()*2
gen u = rnormal()*5
gen y = 2 + 2*x + u
reg y x


      Source |       SS       df       MS              Number of obs =    1000
-------------+------------------------------           F(  1,   998) =   52.93
       Model |  1335.32339     1  1335.32339           Prob > F      =  0.0000
    Residual |   25177.012   998   25.227467           R-squared     =  0.0504
-------------+------------------------------           Adj R-squared =  0.0494
       Total |  26512.3354   999  26.5388743           Root MSE      =  5.0227

------------------------------------------------------------------------------
           y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           x |    1.99348   .2740031     7.28   0.000     1.455792    2.531168
       _cons |   2.036442   .3155685     6.45   0.000     1.417188    2.655695
------------------------------------------------------------------------------




cap program drop NormalReg
program define NormalReg
args lnlk xb sigma2
qui replace `lnlk' = -ln(sqrt(`sigma2'*2*_pi)) - ($ML_y-`xb')^2/(2*`sigma2')
end

ml model lf NormalReg (reg: y = x) (sigma2:)
ml init reg:x = `=_b[x]'
ml init reg:_cons = `=_b[_cons]'
ml max,iter(1) trace


 ml max,iter(1) trace

initial:       log likelihood =     -<inf>  (could not be evaluated)
searching for feasible values .+
feasible:      log likelihood =  -28110.03
rescaling entire vector .+.
rescale:       log likelihood = -14623.922
rescaling equations ...+++++.
rescaling equations ....
rescale eq:    log likelihood = -3080.0872
------------------------------------------------------------------------------
Iteration 0:
Parameter vector:
        reg:     reg:  sigma2:
          x    _cons    _cons
r1  3.98696        1       32

                                                   log likelihood = -3080.0872
------------------------------------------------------------------------------
Iteration 1:
Parameter vector:
         reg:      reg:   sigma2:
           x     _cons     _cons
r1  2.498536  1.773872  24.10726

                                                   log likelihood = -3035.3553
------------------------------------------------------------------------------
convergence not achieved

                                                  Number of obs   =       1000
                                                  Wald chi2(1)    =      86.45
Log likelihood = -3035.3553                       Prob > chi2     =     0.0000

------------------------------------------------------------------------------
           y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
reg          |
           x |   2.498536   .2687209     9.30   0.000     1.971853     3.02522
       _cons |   1.773872   .3086854     5.75   0.000      1.16886    2.378885
-------------+----------------------------------------------------------------
sigma2       |
       _cons |   24.10726   1.033172    23.33   0.000     22.08228    26.13224
------------------------------------------------------------------------------
Warning: convergence not achieved

Answer 1

显然，如果您希望ml在迭代0处评估指定初始值的可能性，则还必须为sigma2;提供值。将代码的最后一部分更改为：

matrix rmse = e(rmse)
scalar mse = rmse[1,1]^2

ml model lf NormalReg (reg: y = x) (sigma2:)
ml init reg:x = `=_b[x]'
ml init reg:_cons = `=_b[_cons]'
ml init sigma2:_cons = `=scalar(mse)'
ml maximize, trace

请注意，sigma ^ 2的ML估计值与均方根误差不同，因为ML不知道自由度。 n = 1,000 sigma2 =（998/1000）* rmse。

Answer 2

像这样的东西非常敏感。您相信上一次回归的结果仍然可以在定义program的确切位置看到。这可能会被几个不同的行动直接或间接地破坏。最好将要用作参数的参数视为在运行时使用程序选项提供给程序的参数。

在Stata中设置ML编程中的初始值

2 个答案: