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From |
"Millimet, Daniel" <millimet@post.cis.smu.edu> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: Standard Errors of Regression Coefficients |

Date |
Mon, 19 Aug 2002 15:43:04 -0500 |

since these are linear, you can use the -test bi* = 0- otherwise, in general, you can use the delta method to obtain std errors of some parameter which is a function of the estimated parameters. dann -----Original Message----- From: RonDorsey [mailto:ron@dorsey21.fsnet.co.uk] Sent: Mon 8/19/2002 3:20 PM To: statalist@hsphsun2.harvard.edu Cc: Subject: st: Standard Errors of Regression Coefficients Dear Statalisters Once again I would value your advice. I have run a fixed effects model and am interested in the size and significance of the fixed effects for each group. I am aware that fixed effects is (essentially) the same as running OLS on the model with group dummies (minus one group). Since it is the fixed effects I'm interested in, dummy coefficients only from OLS are reproduced below: Regression with robust standard errors Number of obs = 759 F( 27, 731) = 3.86 Prob > F = 0.0000 R-squared = 0.1054 Root MSE = 12.619 Robust diffpts Coef. Std. Err. t P>t dum1 -5.032168 2.92946 -1.718 0.086 dum2 -9.752335 2.821768 -3.456 0.001 dum3 -7.194816 2.911494 -2.471 0.014 dum4 -3.296102 2.813756 -1.171 0.242 dum5 -2.073403 3.053997 -0.679 0.497 dum6 -2.028223 2.795275 -0.726 0.468 dum7 -2.925705 2.833143 -1.033 0.302 dum8 -1.564355 2.830483 -0.553 0.581 dum9 .3787652 2.563237 0.148 0.883 dum10 -6.388214 2.810872 -2.273 0.023 dum11 -4.371068 2.875142 -1.520 0.129 dum12 -8.458449 2.813401 -3.006 0.003 dum13 -2.659176 2.756806 -0.965 0.335 dum14 1.783952 2.830733 0.630 0.529 dum15 -5.033113 3.180156 -1.583 0.114 dum16 -3.483705 2.818563 -1.236 0.217 dum17 -3.617528 2.818313 -1.284 0.200 _cons 4.268866 2.278385 1.874 0.061 The t test here refers to whether diffpts in each group (team) differs significantly from the excluded team (dum18). My area of interest is whether or not each teams diffpts deviate from the league average. To measure this I have used the Suits(1984) technique referred to in Greene (2000) p.562 to calculate the value of the dummy for team 18 (and adjust the others accordingly) i.e. k = -(b1 + b2 + b3.......+ b17 + 0) / 18 where bi are the dummy coefficients from OLS. the 'new' dummy coefficients are bi* = bi + k and the 'new' constant is c* = _cons - k This gives: Var bi* _cons 0.61799696 dum1 -1.381299 dum2 -6.101466 dum3 -3.543947 dum4 0.35476704 dum5 1.57746604 dum6 1.62264604 dum7 0.72516404 dum8 2.08651404 dum9 4.02963424 dum10 -2.737345 dum11 -0.720199 dum12 -4.80758 dum13 0.99169304 dum14 5.43482104 dum15 -1.382244 dum16 0.16716404 dum17 0.03334104 dum18 3.65086904 Does anyone know how I calculate the standard errors for 'new' bi* and the constant? Given that the 'new' coefficients are a linear function of the original ones I presume this is possible. Having said that I'm sure it involves matrix algebra (which I'm useless at!) and was hoping someone could devise a routine to do the necessary calculations. Many thanks for your time. Best wishes Ron Dorsey * * 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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