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    <title>topic Re: Different results estimated by SAS and STATA for Fixed and Random Effects in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/Different-results-estimated-by-SAS-and-STATA-for-Fixed-and/m-p/19976#M596</link>
    <description>What do you get if you use the ORTHOREG procedure instead of PROC REG to fit the fixed effect model in SAS?  If you get coefficients which differ from those produced by the REG procedure, then your data are probably ill conditioned, making it very difficult to get exact results.&lt;BR /&gt;
&lt;BR /&gt;
The ORTHOREG procedure is designed to produce estimates for a fixed efffect model with more accuracy than the estimates produced by the REG procedure.  There is no equivalent SAS procedure which produces highly accurate results for a mixed model.  I can't tell you what  option to use in STATA to produce highly accurate results.</description>
    <pubDate>Tue, 11 May 2010 21:38:39 GMT</pubDate>
    <dc:creator>Dale</dc:creator>
    <dc:date>2010-05-11T21:38:39Z</dc:date>
    <item>
      <title>Different results estimated by SAS and STATA for Fixed and Random Effects</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Different-results-estimated-by-SAS-and-STATA-for-Fixed-and/m-p/19975#M595</link>
      <description>Different results estimated by SAS and STATA for Fixed and Random Effects? The Hausman Test is also give different answers&lt;BR /&gt;
&lt;BR /&gt;
is there any differences in estimation method employed by these software? I will be very thankful for any comments&lt;BR /&gt;
&lt;BR /&gt;
These are the commands and Results I got from the two soft.&lt;BR /&gt;
&lt;BR /&gt;
&lt;BR /&gt;
a-	For Fixed Effect:&lt;BR /&gt;
SAS:&lt;BR /&gt;
proc reg data=chapter3.all_area;&lt;BR /&gt;
Model Ln_qdt = Ln_qdt2 Ln_vkm Ln_income Ln_F deregulation_dummy Time_Trend London_dummy Mets_ dummy Scotland_ dummy Wales_ dummy;&lt;BR /&gt;
test  London_dv = Mets_dv = Scotland_dv = Wales_dv = 0 ;&lt;BR /&gt;
run;&lt;BR /&gt;
&lt;BR /&gt;
STATA:&lt;BR /&gt;
regress   Ln_qdt Ln_qdt2 Ln_vkm Ln_income Ln_F deregulation_dummy Time_Trend London_dummy Mets_ dummy Scotland_ dummy Wales_ dummy&lt;BR /&gt;
&lt;BR /&gt;
&lt;BR /&gt;
&lt;BR /&gt;
&lt;BR /&gt;
b-	For Random Effect:&lt;BR /&gt;
SAS:&lt;BR /&gt;
&lt;BR /&gt;
proc panel data=chapter3.all_area;&lt;BR /&gt;
ID area year;&lt;BR /&gt;
Model Ln_qdt = Ln_qdt2 Ln_vkm Ln_income Ln_F deregulation_dummy Time_Trend / RANONE BP VCOMP=WK&lt;BR /&gt;
;&lt;BR /&gt;
run;&lt;BR /&gt;
&lt;BR /&gt;
&lt;BR /&gt;
                           &lt;BR /&gt;
&lt;BR /&gt;
STATA:&lt;BR /&gt;
iis area&lt;BR /&gt;
 xtreg qdt Ln_qdt2 Ln_vkm Ln_income Ln_F deregulation_dummy Time_Trend, re theta&lt;BR /&gt;
&lt;BR /&gt;
	SAS		                     STATA&lt;BR /&gt;
Model	FE	RE		FE (Using STATA)	RE (Using STATA)&lt;BR /&gt;
 	Coeff.	Coeff.		Coeff.	Coeff.&lt;BR /&gt;
 					&lt;BR /&gt;
Ln F	-.108	-0.09892		-.108	-0.06321&lt;BR /&gt;
Ln VKM	.114	0.135992		.115	0.082707&lt;BR /&gt;
Ln Income	-.560	-0.52503		-.566	-0.297&lt;BR /&gt;
Ln Qdt-1	.695	0.747298		.692	0.924061&lt;BR /&gt;
Der. DV	-.046	-0.04975		-.047	-0.05055&lt;BR /&gt;
TT	.011	0.010877		.011	0.00887&lt;BR /&gt;
 					&lt;BR /&gt;
Mets	.196			0.198	&lt;BR /&gt;
Scot	.153			0.154	&lt;BR /&gt;
Wales	-.023			-0.023	&lt;BR /&gt;
constant	5.999	 	 	5.908	 &lt;BR /&gt;
 					&lt;BR /&gt;
F	3624.282			3618.020	&lt;BR /&gt;
R2 (Adj.)	.997	0.973		0.9969	0.9967&lt;BR /&gt;
Durbin-Watson	1.703	 	 	 	 &lt;BR /&gt;
 					&lt;BR /&gt;
(Incremental) F	5.57 (0.0015)	5.57 (0.0015)	&lt;BR /&gt;
Breusch Pagan Test	0.00 (0.9781)	0.00 (0.9779)	&lt;BR /&gt;
Hausman Test	2.34 (0.8859)	20.16 (0.0026)</description>
      <pubDate>Tue, 11 May 2010 19:13:42 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Different-results-estimated-by-SAS-and-STATA-for-Fixed-and/m-p/19975#M595</guid>
      <dc:creator>deleted_user</dc:creator>
      <dc:date>2010-05-11T19:13:42Z</dc:date>
    </item>
    <item>
      <title>Re: Different results estimated by SAS and STATA for Fixed and Random Effects</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Different-results-estimated-by-SAS-and-STATA-for-Fixed-and/m-p/19976#M596</link>
      <description>What do you get if you use the ORTHOREG procedure instead of PROC REG to fit the fixed effect model in SAS?  If you get coefficients which differ from those produced by the REG procedure, then your data are probably ill conditioned, making it very difficult to get exact results.&lt;BR /&gt;
&lt;BR /&gt;
The ORTHOREG procedure is designed to produce estimates for a fixed efffect model with more accuracy than the estimates produced by the REG procedure.  There is no equivalent SAS procedure which produces highly accurate results for a mixed model.  I can't tell you what  option to use in STATA to produce highly accurate results.</description>
      <pubDate>Tue, 11 May 2010 21:38:39 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Different-results-estimated-by-SAS-and-STATA-for-Fixed-and/m-p/19976#M596</guid>
      <dc:creator>Dale</dc:creator>
      <dc:date>2010-05-11T21:38:39Z</dc:date>
    </item>
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