[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

From |
"Narasimhan Sowmyanarayanan" <narasimhan.sowmyanarayanan@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: Question on constraining estimates |

Date |
Fri, 28 Mar 2008 10:49:02 -0400 |

Hi Statalisters: First of all I apologize if this question does not relate entirely to a software problem in stata. However, I am a little confused about some results that stata is throwing up. I am trying to run some simple OLS models across two different groups. Group A and Group B. My regression equation is of the simple form y=a+a1 *x1 +e However, the variable x1 is measured across two different groups. My objectives were to examine the following. a) examine possible invariance of the equation across the two groups b) test for significant differences in the regression coefficients across the groups. For testing both I started with creating a group dummy variable and the interaction terms for each of the independent variable as in http://www.ats.ucla.edu/stat/stata/faq/compreg3.htm I created 1 interaction term. For testing a) I used a constrained regression with the coefficient of the interaction variables to be constrained to 0 and comparing the two regression equations using with a change in F value between the constrained and free models. For testing (b) I used the suest procedure in stata, and the alternative equivalent procedure I understood is that i can test for the interaction coefficients to be zero is to use the test procedure. I am finding that despite getting a significant improvement in the model fit (Decrease in F values) when releasing the parameters (each one of them) as opposed to a fully constrained regression equation (as in (a) -- suggesting that substantively the impact of the IV's on the models is different), I find none of the slopes are significantly different when I test for the coefficient of the interaction variable to be zero. My expectation was, given the significant changes in the model F values on changing each parameter, I should find significant differences in regression coefficients across the models ? Is this a reasonable expectation ? I was curious to understand how these are implemented Thanks Narsi * * 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/

- Prev by Date:
**Re: st: -postfile, every()-?** - Next by Date:
**RE: st: Definition of strata and PSUs when svysetting** - Previous by thread:
**st: Adding variable from baseline file into cohort based file** - Next by thread:
**Subject: st: poisson exposure problem** - Index(es):

© Copyright 1996–2019 StataCorp LLC | Terms of use | Privacy | Contact us | What's new | Site index |