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From |
Joseph Coveney <[email protected]> |

To |
Statalist <[email protected]> |

Subject |
Re: st: How to test for Fixed Effect after "areg" |

Date |
Mon, 21 Jul 2003 14:37:05 +0900 |

Jayesh Kumar wrote: ---------------------begin excerpt from posting--------------------------------- As I want to test whether the Fixed Effect, is at all needed verses OLS? I am using firm level data to analyze firm's performance. My equation is something like: Y_it= (X1_it + X2_it + X3_it + ...+ X9_it) + (Z1_it + Z2_it + Z3_it +Z4_it) + u_it where X's and Z's both are firm specific variables. -----------------------end excerpt from posting--------------------------------- Use the same approach as for the ealier illustration: -test- X's Z's together. You can also use likelihood-ratio tests, dropping both sets of the predictors at once from the model that contains them. I've illustrated both below by slightly modifying the previous illustration do-file to recast the between- subjects predictors, ind1 and ind2, to within-subjects predictors, x1d through x9d and z1d through z4d. For the illustration, I'm assuming that Jayesh is referring to a panel dataset (modeled with two error terms, u_i and e_it) of 2000 companies evaluated at each of 15 intervals with two sets (X1 through X9 and Z1 through Z4) of time- varying covariates. For brevity here, the initial model isn't the default one that is typical for a blind or naive model-building exercise or hypothesis- testing exercise, that is, fully saturated with two- and three-way interactions of covariates and time. Joseph Coveney ------------------------begin illustration do-file------------------------------ clear set seed 20030720 set obs 15 set more off forvalues i = 1/15 { generate float a`i' = 0.7 quietly replace a`i' = 1 in `i' } mkmat a*, matrix(A) local means "100" local sd "15" local dep "dep1" forvalues i = 2/15 { local means = "`means'" + ", 100" local sd = "`sd'" + ", 15" local dep = "`dep'" + " dep`i'" } drawnorm "`dep'", n(2000) means("`means'") /* */ sd("`sd'") corr(A) clear matrix drop A generate int rid = _n reshape long dep, i(rid) j(tim) forvalues i = 1/9 { generate byte x`i'd = uniform() >= 0.5 } forvalues i = 1/4 { generate byte z`i'd = uniform() >= 0.5 } * xi: xtreg dep x*d z*d i.tim, i(rid) fe * * Testing each alone * test x1d x2d x3d x4d x5d x6d x7d x8d x9d test z1d z2d z3d z4d * * Testing both together * test x1d x2d x3d x4d x5d x6d x7d x8d x9d, notest test z1d z2d z3d z4d, accumulate * * Likelihood-ratio tests (using -xtreg , mle-) * xi: xtreg dep x*d z*d i.tim, i(rid) mle nolog estimates store A xi: xtreg dep x*d i.tim, i(rid) mle nolog estimates store B xi: xtreg dep z*d i.tim, i(rid) mle nolog estimates store C xi: xtreg dep i.tim, i(rid) mle nolog estimates store D * Testing Z's lrtest A B, stats * Testing X's lrtest A C, stats * Testing both lrtest A D, stats exit --------------------------end illustration do-file------------------------------ * * 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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