Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
Tom Moliterno <moliterno@moore.sc.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: interpreting -test, accumulate- output |

Date |
Wed, 17 Feb 2010 16:00:24 -0500 |

Hi Statalisters, Hope I could get some help interpreting output from a test command, using the accumulate option. All the searches I've done make it seem like it's straightforward, but I'm a bit puzzled ... First here's the model: I'll give you just the results for the variables I'm interested in. It's an -xtreg, fe- rY_RDistSAQ1 | -.5356885 .2935574 -1.82 0.069 -1.114338 .0429612 rY_RDistSAQ2 | -.6776527 .2659942 -2.55 0.012 -1.201971 -.1533346 rY_RDistSAQ3 | -.2888348 .3427794 -0.84 0.400 -.9645092 .3868396 rY_RDistSAQ4 | -.8127006 .4679536 -1.74 0.084 -1.735114 .1097126 So let's call these var1-var4, using the last number of the variable names. As a side bar, these are 4 linear splines from a continuous variable made using the -mkspline- command. Now my objective is to be able to interpret the relationship between these coefficients. Obviously, var2 is sig at p<0.05, and var4 is marginally sig at p<0.10. But what more can I say ... so I ran -test-: . test (rY_RDistSAQ1-rY_RDistSAQ2)=0 ( 1) rY_RDistSAQ1 - rY_RDistSAQ2 = 0 F( 1, 213) = 0.16 Prob > F = 0.6886 So I interpret this to say that there is not a significant difference between the coefficient for var1 and var2. (right?) Now ... I ran the test command using the accumulate option ... and this is what I'm not sure how to interpret. Here is the output: . foreach var in rY_RDistSAQ1 rY_RDistSAQ3 rY_RDistSAQ4 rY_RDistSAQ2{ 2. test `var', accumulate 3. } ( 1) rY_RDistSAQ1 = 0 F( 1, 213) = 3.33 Prob > F = 0.0694 ( 1) rY_RDistSAQ1 = 0 ( 2) rY_RDistSAQ3 = 0 F( 2, 213) = 1.68 Prob > F = 0.1897 ( 1) rY_RDistSAQ1 = 0 ( 2) rY_RDistSAQ3 = 0 ( 3) rY_RDistSAQ4 = 0 F( 3, 213) = 1.85 Prob > F = 0.1395 ( 1) rY_RDistSAQ1 = 0 ( 2) rY_RDistSAQ3 = 0 ( 3) rY_RDistSAQ4 = 0 ( 4) rY_RDistSAQ2 = 0 F( 4, 213) = 2.44 Prob > F = 0.0481 So do I have this right: 1st iteration --> var1 (marginally) improves model fit 2nd iteration --> adding var3 doesn't improve model fit, conditioned on having var1 in the model 3rd iteration --> adding var 4 doesn't improve model fit, conditioned on having var1 and var3 in the model 4th iteration --> adding var2 DOES improve model fit, conditioned on the other three vars being in the model Is that right? Is there anything else interesting I can say about that last iteration? I'm theoretically interested in var2 ... I'm just not sure what the F-test is describing, exactly, in that last iteration. Any help would be most appreciated! Tom - ********************************************************** Thomas P. Moliterno, PhD Moore School of Business University of South Carolina moliterno@moore.sc.edu ********************************************************** "The way to succeed is to double your error rate." -- Thomas J. Watson * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

- Prev by Date:
**st: RE: RE: RE: RE: where did my matrix go after calling -diagt-** - Next by Date:
**st: RE: RE: Estout question** - Previous by thread:
**st: test for rho=0 in Heckman two-step procedure?** - Next by thread:
**st: nonparametric regression with sampling weights** - Index(es):