Hello member,
Can you please help to describe the model and interpret the results below correctly? I found the literature on multilevel model quite confusing as the same model appear called and interpreted differently.
Am right to state that:
1. This is a random slope model where by overall model intercept (_cons) is shifted by magnitude of the standard error of the variance of the variable new-retai? In effect,estimated coefficient for new-retai is fixed but clustered data at a panel identified level (SKUCode) and at new-retai mean that the model intercept is no longer constant.
2. Such model allows to get more efficient estimates than OLS even if variance of new-retai is not constant over time?
Thanks,
Mohmaud
Can you please help to describe the model and interpret the results below correctly? I found the literature on multilevel model quite confusing as the same model appear called and interpreted differently.
Am right to state that:
1. This is a random slope model where by overall model intercept (_cons) is shifted by magnitude of the standard error of the variance of the variable new-retai? In effect,estimated coefficient for new-retai is fixed but clustered data at a panel identified level (SKUCode) and at new-retai mean that the model intercept is no longer constant.
2. Such model allows to get more efficient estimates than OLS even if variance of new-retai is not constant over time?
Thanks,
Mohmaud
Code:
xtmixed price_ad d##ib1.cool ib1.branv ib1.new_packz ib1.new_retai ib1.new_eatq ib1.speci s1 s2 s3 if price_ad>0SKUCode:retai, nolog mle > cov(unstructured) variance note: 3.speci omitted because of collinearity Mixed-effects ML regression Number of obs = 881 Group variable: SKUCode Number of groups = 19 Obs per group: min = 28 avg = 46.4 max = 60 Wald chi2(22) = 744.82 Log likelihood = -1408.9143 Prob > chi2 = 0.0000 price_ad Coef. Std. Err. z P>z [95% Conf. Interval] 1.d -.0676438 .1142086 -0.59 0.554 -.2914885 .1562008 cool English .8446842 .7522606 1.12 0.261 -.6297195 2.319088 Irish -.8442959 .6345829 -1.33 0.183 -2.088055 .3994636 New Zealand 3.0832 .6348334 4.86 0.000 1.83895 4.327451 Undeclared -3.05322 .79554 -3.84 0.000 -4.61245 -1.49399 d#cool 1#English 1.414085 .4242636 3.33 0.001 .5825437 2.245627 1#Irish 1.490209 .2787894 5.35 0.000 .943792 2.036626 1#New Zealand .4875265 .3287033 1.48 0.138 -.1567202 1.131773 1#Undeclared .3319101 .2004654 1.66 0.098 -.0609949 .7248152 branv Mid range -2.335512 .8236805 -2.84 0.005 -3.949896 -.7211273 Value -2.34774 .8840788 -2.66 0.008 -4.080503 -.6149777 new_packz 2 1.62276 .7776197 2.09 0.037 .0986534 3.146866 3 2.540382 .2145492 11.84 0.000 2.119873 2.960891 new_retai 2 -2.821306 .9139832 -3.09 0.002 -4.612681 -1.029932 3 -.8948616 .7698844 -1.16 0.245 -2.403807 .6140842 4 -1.321566 1.100434 -1.20 0.230 -3.478376 .8352452 2.new_eatq .55709 .5786807 0.96 0.336 -.5771033 1.691283 speci Chicken 2.712263 1.200454 2.26 0.024 .3594168 5.065109 Lamb 0 (omitted) Pork .7339984 .5613633 1.31 0.191 -.3662534 1.83425 s1 -.0226442 .1140905 -0.20 0.843 -.2462574 .2009691 s2 .1004148 .1117495 0.90 0.369 -.1186102 .3194398 s3 -.0045074 .1111552 -0.04 0.968 -.2223676 .2133527 _cons 7.042653 1.402746 5.02 0.000 4.293321 9.791986 Random-effects Parameters Estimate Std. Err. [95% Conf. Interval] SKUCode: Unstructured var(retai) .0764931 .0392668 .0279684 .2092072 var(_cons) 1.899935 .9071493 .7452827 4.843466 cov(retai,_cons) -.3812243 .1881468 -.7499853 -.0124633 var(Residual) 1.386305 .0665228 1.261866 1.523016 LR test vs. linear model: chi2(3) = 78.02 Prob > chi2 = 0.0000 Note: LR test is conservative and provided only for reference.