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st: xtscc and xtreg - t ratios on time trends


From   Alexander Nervedi <alexnerdy@hotmail.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: xtscc and xtreg - t ratios on time trends
Date   Thu, 5 Jun 2008 04:33:06 +0000

Hi Stata gooroos!

I am trying to figure out what is it that xtreg does differently from xtscc that affects estimation of t-ratios for time variables. I have a data set on firms over time observing their inventory sizes (in days). Now if I put in a time fixed effect it doesn't report t-stats in xtscc while it does so for xtreg. I am hoping someone can help

*summary of the data*
. sum avginventorydays yr1-yr14

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
avginvento~s |       812    71.52417    42.35176       4.75      345.3
         yr1 |       812    .0714286    .2576981          0          1
         yr2 |       812    .0714286    .2576981          0          1
         yr3 |       812    .0714286    .2576981          0          1
         yr4 |       812    .0714286    .2576981          0          1
-------------+--------------------------------------------------------
         yr5 |       812    .0714286    .2576981          0          1
         yr6 |       812    .0714286    .2576981          0          1
         yr7 |       812    .0714286    .2576981          0          1
         yr8 |       812    .0714286    .2576981          0          1
         yr9 |       812    .0714286    .2576981          0          1
-------------+--------------------------------------------------------
        yr10 |       812    .0714286    .2576981          0          1
        yr11 |       812    .0714286    .2576981          0          1
        yr12 |       812    .0714286    .2576981          0          1
        yr13 |       812    .0714286    .2576981          0          1
        yr14 |       812    .0714286    .2576981          0          1

*xtreg regression*

. xtreg  avginventorydays yr1-yr13, fe

Fixed-effects (within) regression               Number of obs      =       812
Group variable: n_company                       Number of groups   =        58

R-sq:  within  = 0.2527                         Obs per group: min =        14
       between = 0.0000                                        avg =      14.0
       overall = 0.0963                                        max =        14

                                                F(13,741)          =     19.27
corr(u_i, Xb)  = 0.0000                         Prob> F           =    0.0000

------------------------------------------------------------------------------
avginvento~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         yr1 |   44.38966   4.390504    10.11   0.000     35.77035    53.00896
         yr2 |   43.56345   4.390504     9.92   0.000     34.94414    52.18276
         yr3 |   35.44948   4.390504     8.07   0.000     26.83017    44.06879
         yr4 |   25.79155   4.390504     5.87   0.000     17.17224    34.41086
         yr5 |   22.10862   4.390504     5.04   0.000     13.48931    30.72793
         yr6 |   18.50586   4.390504     4.21   0.000     9.886554    27.12517
         yr7 |    27.1569   4.390504     6.19   0.000     18.53759    35.77621
         yr8 |    20.4119   4.390504     4.65   0.000     11.79259    29.03121
         yr9 |   16.31241   4.390504     3.72   0.000     7.693105    24.93172
        yr10 |   16.02845   4.390504     3.65   0.000     7.409139    24.64776
        yr11 |   16.16276   4.390504     3.68   0.000      7.54345    24.78207
        yr12 |   8.121896   4.390504     1.85   0.065    -.4974127    16.74121
        yr13 |   .8931034   4.390504     0.20   0.839    -7.726206    9.512412
       _cons |   50.46017   3.104555    16.25   0.000      44.3654    56.55494
-------------+----------------------------------------------------------------
     sigma_u |  33.590036
     sigma_e |   23.64359
         rho |  .66869195   (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0:     F(57, 741) =    28.26             Prob> F = 0.0000


*xtscc*

 xtscc  avginventorydays yr1-yr13, fe

Regression with Driscoll-Kraay standard errors   Number of obs     =       812
Method: Fixed-effects regression                 Number of groups  =        58
Group variable (i): n_company                    F( 13,    57)     =         .
maximum lag: 2                                   Prob> F          =         .
                                                 within R-squared  =    0.2527

------------------------------------------------------------------------------
             |             Drisc/Kraay
avginvento~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         yr1 |   44.38966   4.19e-12        .   0.000     44.38966    44.38966
         yr2 |   43.56345   4.19e-12        .   0.000     43.56345    43.56345
         yr3 |   35.44948   4.19e-12        .   0.000     35.44948    35.44948
         yr4 |   25.79155   4.19e-12        .   0.000     25.79155    25.79155
         yr5 |   22.10862   4.19e-12        .   0.000     22.10862    22.10862
         yr6 |   18.50586   4.19e-12        .   0.000     18.50586    18.50586
         yr7 |    27.1569   4.19e-12        .   0.000      27.1569     27.1569
         yr8 |    20.4119   4.19e-12        .   0.000      20.4119     20.4119
         yr9 |   16.31241   4.19e-12        .   0.000     16.31241    16.31241
        yr10 |   16.02845   4.19e-12        .   0.000     16.02845    16.02845
        yr11 |   16.16276   4.20e-12        .   0.000     16.16276    16.16276
        yr12 |   8.121896   4.22e-12        .   0.000     8.121896    8.121896
        yr13 |   .8931034   4.35e-12        .   0.000     .8931034    .8931034
       _cons |   50.46017   4.19e-12        .   0.000     50.46017    50.46017
------------------------------------------------------------------------------

I'd like to know what to infer from this ... how is it that the std. errors have shrunk so much! Any help would be great.

Alex

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