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From |
Bersant Hobdari <bh.cees@cbs.dk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: Test coefficients across equations |

Date |
Fri, 27 Jun 2003 10:52:03 +0100 |

Hi Everyone, Many thanks to Scott Merryman, David Moore and Mike Hollis for their comments on my original message. I however have still some doubts regarding the way I should follow. I will explain it in more detail taking also into account comments of Scott, David and Mike. I am estimating the same specification on different samples and want to test coefficients equality across the samples. The main gist of Scott, Davis and Mike argument was that I introduce ownership dummies and respective interactions in the pooled sample and then apply Chow type tests on individual equations. This however, as they themselves stress, needs sub-samples to be independent. I have serious doubts that my sub-samples are independent. More specifically, I divide the big sample into smaller ones based on majority ownership. Given that ownership changes over time a given firm might be present at different sub-samples over time. I assume this is enough to state that sub-samples are not independent. The reason for sample separation is the endogeneity of ownership structures to the left hand side variable, labor productivity in this case. If I ignore this problem I will be in a bigger problem that the one I am trying to solve. Given non-independence then what is the way to test coefficient equality across equations. One suggestion would be to use Zellner's SUR method, suggested by Scott. To my knowledge however SUR estimates equation by equation OLS accounting for cross equation correlation. I however have used GMM (through ivreg2 procedure) to obtain estimates. Can SUR be used with GMM? The big question then becomes: how do I test coefficients equality across equations estimated on different samples when samples are not independent? Any help is highly appreciated. Sincerely, Bersant Hobdari Content-Type: multipart/alternative; boundary="--=a921961f-c433-46a5-840e-89a84a4a191d" ----=a921961f-c433-46a5-840e-89a84a4a191d Content-Type: text/plain Content-Transfer-Encoding: 7bit **************************************************************************** Denne e-mail er scannet af mailFence fra Sure Solutions (www.suresolutions.dk), og der er ikke fundet vira. **************************************************************************** ----=a921961f-c433-46a5-840e-89a84a4a191d Content-Type: text/html Content-Transfer-Encoding: quoted-printable <HTML><HEAD> </HEAD><BODY>=20 <HR> <DIV align=3Dcenter><FONT face=3DArial size=3D1>Denne e-mail er scannet a= f mailFence=20 fra Sure Solutions (</FONT><A href=3D"http://www.suresolutions.dk";><FONT = face=3DArial size=3D1>www.suresolutions.dk</FONT></A><FONT face=3DArial s= ize=3D1>), og=20 der er ikke fundet vira.</FONT></DIV> <DIV> <HR> </DIV> </BODY></HTML> ----=a921961f-c433-46a5-840e-89a84a4a191d--

----- Original Message ----- From: "Bersant Hobdari" <bh.cees@cbs.dk> To: <statalist@hsphsun2.harvard.edu> Sent: Thursday, June 26, 2003 11:51 AM Subject: Re: st: Re: sample selection bias > Hi Everyone, > > I had a question on testing coefficient across separately estimated > samples. The problem is the following: I estimate firm-level production > function where I divide the sample in 5 sub-samples defined by majority > owner: I.e., if majority owner is the State I classify the firm in that > group, if it is a financial institution I classify it in that group and > so on. After estimating regressions I would like to test the equality of > coefficients across equations. > > Any suggestion how this could be implemented is highly appreciated. > > Sincerely, > Bersant Hobdari You could create a dummy variable on majority owner then interact it with your other variables and test the coefficients on the fully interacted model (see the Stata FAQ on Chow tests). Example using the auto dataset. Equation 1: mpg = b0 + b1*price (if domestic) Equation 2: mpg = b0' + b1'*price (if foreign) Create the interaction term (if you have more categories -xi- comes in handy) gen priceXforeign = price *foreign Regress the full interacted model regress mpg = price foreign priceXforeign A test on foreign will compare common intercepts, a test on priceXforeign will test common slopes, and a test on both foreign and priceXforeign will test if they are jointly equal to zero, or if equation 2 differs from equation 1. However, if you are concerned about correlation across equation (or wish to test for it), -reshape- your data into a wide data structure and use -sureg-. Hope this helps, Scott * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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**Follow-Ups**:**st: Error bars on bar plots***From:*Fred Wolfe <fwolfe@arthritis-research.org>

**st: RE: Test coefficients across equations***From:*"Mike Hollis" <mehla@earthlink.net>

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