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st: FW: comparing xtregar coefficients across models


From   Lopa Chakraborti <lchakraborti@arec.umd.edu>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   st: FW: comparing xtregar coefficients across models
Date   Fri, 25 Feb 2011 14:02:57 -0500

Not sure how to let statalist know that this error has been resolved.
sincerely
Lopa
________________________________________
From: Lopa Chakraborti
Sent: Friday, February 25, 2011 10:44 AM
To: statalist@hsphsun2.harvard.edu
Subject: comparing xtregar coefficients across models

I need some help on how to compare regression coefficients between models using xtregar. In the results below, I am trying to compare the coefficient on lagseaavgwqfoia04avg3 (first model below) with that of pastyearseaavgwq (second model, further below) by calculating the t statistics. The test seems to fail and gives error message "Constraint 1 dropped".
any advice would be appreciated

model 1:

. xtregar lseaavglcavfoia04avg2   lagseaavgwqfoia04avg3 lagseaavgflowfoia04avg3 elec food mill paper chem petro rubber leather metal transp secu just rnwhite mhhi carpl manuf popt popu MD PA

RE GLS regression with AR(1) disturbances       Number of obs      =       352
Group variable (i): npid                        Number of groups   =        81

R-sq:  within  = 0.0679                         Obs per group: min =         2
       between = 0.3513                                        avg =       4.3
       overall = 0.2204                                        max =        10

                                                Wald chi2(23)      =     63.36
corr(u_i, Xb)      = 0 (assumed)                Prob > chi2        =    0.0000

------------------- theta --------------------
  min      5%       median        95%      max
0.5865   0.5865     0.6942     0.7785   0.8009

------------------------------------------------------------------------------
lseaavglca~2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
lagseaavgw~3 |   .0243223   .0049348     4.93   0.000     .0146504    .0339943
lagseaavgf~3 |   .0000264   .0002437     0.11   0.914    -.0004511     .000504
        elec |   .0167549   .2884746     0.06   0.954     -.548645    .5821547
        food |   .4841683   .4780733     1.01   0.311    -.4528381    1.421175
        mill |  -.2253284   .3069892    -0.73   0.463    -.8270161    .3763594
       paper |   .2761943   .2659313     1.04   0.299    -.2450215    .7974102
        chem |    .416034   .2515593     1.65   0.098    -.0770132    .9090812
       petro |    .777429   .3445641     2.26   0.024     .1020958    1.452762
      rubber |   .2154333   .4328246     0.50   0.619    -.6328873    1.063754
     leather |   1.157704   .3244032     3.57   0.000     .5218859    1.793523
       metal |  -.0522007   .4622447    -0.11   0.910    -.9581837    .8537823
      transp |   .5580452   .4799053     1.16   0.245     -.382552    1.498642
        secu |  -.3943754   .3002096    -1.31   0.189    -.9827755    .1940247
        just |   .2919446    .431559     0.68   0.499    -.5538955    1.137785
     rnwhite |   .0041641    .003601     1.16   0.248    -.0028936    .0112218
        mhhi |   -.007618   .0064241    -1.19   0.236    -.0202091    .0049731
       carpl |   .0046771   .0116036     0.40   0.687    -.0180656    .0274198
       manuf |   .0023217    .005092     0.46   0.648    -.0076585    .0123019
        popt |  -.0057607   .0042432    -1.36   0.175    -.0140771    .0025557
        popu |  -.0009174   .0015892    -0.58   0.564    -.0040323    .0021974
          MD |    .294147   .1364115     2.16   0.031     .0267853    .5615087
          PA |   .0404563   .2087747     0.19   0.846    -.3687346    .4496472
       _cons |   2.964972   .3077018     9.64   0.000     2.361888    3.568056
-------------+----------------------------------------------------------------
      rho_ar |    .426524   (estimated autocorrelation coefficient)
     sigma_u |  .37549867
     sigma_e |  .18262772
     rho_fov |  .80870389   (fraction of variance due to u_i)
------------------------------------------------------------------------------

. scalar t_lagseaavgwqfoia04avg3=_b[lagseaavgwqfoia04avg3]/_se[lagseaavgwqfoia04avg3]


model 2:

. xtregar lseaavglcavfoia04avg2  pastyearseaavgwq lagseaavgflowfoia04avg3 elec food mill paper chem petro rubber leather metal transp secu just rnwhite mhhi carpl manuf popt popu MD PA

RE GLS regression with AR(1) disturbances       Number of obs      =       346
Group variable (i): npid                        Number of groups   =        80

R-sq:  within  = 0.0892                         Obs per group: min =         2
       between = 0.3846                                        avg =       4.3
       overall = 0.2400                                        max =         8

                                                Wald chi2(23)      =     73.20
corr(u_i, Xb)      = 0 (assumed)                Prob > chi2        =    0.0000

------------------- theta --------------------
  min      5%       median        95%      max
0.5795   0.5795     0.6885     0.7742   0.7742

------------------------------------------------------------------------------
lseaavglca~2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
pastyearse~q |   .0262475   .0048356     5.43   0.000     .0167699     .035725
lagseaavgf~3 |   .0000391   .0002365     0.17   0.869    -.0004244    .0005026
        elec |  -.0045798   .2800818    -0.02   0.987    -.5535301    .5443705
        food |   .2973297   .4741468     0.63   0.531    -.6319809     1.22664
        mill |  -.2210648   .2980757    -0.74   0.458    -.8052824    .3631529
       paper |   .5726535   .2985217     1.92   0.055    -.0124382    1.157745
        chem |   .2940331   .2513263     1.17   0.242    -.1985574    .7866236
       petro |   .6126716   .3433223     1.78   0.074    -.0602278    1.285571
      rubber |    .246944   .4207964     0.59   0.557    -.5778019     1.07169
     leather |   1.055009   .3183348     3.31   0.001     .4310839    1.678933
       metal |   .0795689   .4540158     0.18   0.861    -.8102857    .9694236
      transp |   .3573744   .4766374     0.75   0.453    -.5768177    1.291567
        secu |  -.3947636   .2912529    -1.36   0.175    -.9656088    .1760816
        just |   .2870395   .4187941     0.69   0.493    -.5337818    1.107861
     rnwhite |   .0040544   .0035024     1.16   0.247    -.0028101     .010919
        mhhi |  -.0049846   .0063327    -0.79   0.431    -.0173965    .0074272
       carpl |   .0006704   .0114049     0.06   0.953    -.0216828    .0230235
       manuf |   .0034781   .0049695     0.70   0.484     -.006262    .0132181
        popt |  -.0047142   .0041523    -1.14   0.256    -.0128526    .0034242
        popu |  -.0016382   .0015744    -1.04   0.298    -.0047241    .0014476
          MD |   .3072598   .1324144     2.32   0.020     .0477324    .5667872
          PA |   .2284754   .2213065     1.03   0.302    -.2052773    .6622282
       _cons |   2.910289   .2990276     9.73   0.000     2.324206    3.496372
-------------+----------------------------------------------------------------
      rho_ar |  .41767391   (estimated autocorrelation coefficient)
     sigma_u |  .36306501
     sigma_e |  .18159331
     rho_fov |  .79989281   (fraction of variance due to u_i)
------------------------------------------------------------------------------

. scalar t_pastyearseaavgwq=_b[pastyearseaavgwq]/_se[pastyearseaavgwq]

. test t_lagseaavgwqfoia04avg3=t_pastyearseaavgwq

 ( 1) = .4992289
       Constraint 1 dropped

           chi2(  0) =       .
         Prob > chi2 =         .

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