Dear Statalist,
I am trying to obtain average marginal effects from a population-averaged negative binomial regression.
I ran the following:
As you can see, the p-values of the results obtained from the regression, and from the margins command, are very different. Significant results in the regression are insignificant in the results produced by the margins command.
My question is, does the difference in p-values indicate that I am doing something wrong, and if so, what corrections should I make?
Thank you for any and all help provided.
Best wishes,
Adam
I am trying to obtain average marginal effects from a population-averaged negative binomial regression.
I ran the following:
Code:
. xtgee cnrparticipants ///
> sqrt_cnrdeaths ///
> pulparticipants_lagged ///
> pulpop_pcent ///
> pulpop_pcent_sq ///
> popchange ///
> popchange_squared ///
> unemployment_ratio ///
> cnrworker ///
> hbclaimants ///
> cnrdegree ///
> cnrparticipants_lagged ///
> electionyear_a ///
> , family(nbinomial 1) exposure(cnrpop) vce(robust) ///
> corr(ind)
Iteration 1: tolerance = 7.528e-07
GEE population-averaged model Number of obs = 1,390
Group variable: settlementid Number of groups = 139
Link: log Obs per group:
Family: negative binomial(k=1) min = 10
Correlation: independent avg = 10.0
max = 10
Wald chi2(12) = 262.93
Scale parameter: 1 Prob > chi2 = 0.0000
Pearson chi2(1390): 51831.07 Deviance = 7213.68
Dispersion (Pearson): 37.28854 Dispersion = 5.189696
(Std. Err. adjusted for clustering on settlementid)
----------------------------------------------------------------------------------------
| Semirobust
cnrparticipants | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
sqrt_cnrdeaths | -.1507375 .0740523 -2.04 0.042 -.2958774 -.0055976
pulparticipants_lagged | .0125712 .003579 3.51 0.000 .0055564 .0195859
pulpop_pcent | -.076741 .039356 -1.95 0.051 -.1538773 .0003954
pulpop_pcent_sq | .0004652 .0004765 0.98 0.329 -.0004687 .0013991
popchange | -.1993546 .4461408 -0.45 0.655 -1.073775 .6750654
popchange_squared | -.0681788 .1272669 -0.54 0.592 -.3176174 .1812598
unemployment_ratio | 1.550292 .4837341 3.20 0.001 .6021904 2.498393
cnrworker | .0892763 .0353052 2.53 0.011 .0200794 .1584731
hbclaimants | -.0436729 .022623 -1.93 0.054 -.0880132 .0006674
cnrdegree | -.0225913 .0425565 -0.53 0.596 -.1060004 .0608179
cnrparticipants_lagged | .0533955 .0115392 4.63 0.000 .030779 .076012
electionyear_a | -.1913063 .2326139 -0.82 0.411 -.6472212 .2646085
_cons | -5.327789 1.523732 -3.50 0.000 -8.314249 -2.341329
ln(cnrpop) | 1 (exposure)
----------------------------------------------------------------------------------------
.
. margins, dydx(*) post
Average marginal effects Number of obs = 1,390
Model VCE : Semirobust
Expression : Exponentiated linear prediction considering offset, predict()
dy/dx w.r.t. : sqrt_cnrdeaths pulparticipants_lagged pulpop_pcent pulpop_pcent_sq popchange popchange_squared unemployment_ratio
cnrworker hbclaimants cnrdegree cnrparticipants_lagged electionyear_a
----------------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. z P>|z| [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
sqrt_cnrdeaths | -2501.737 3338.087 -0.75 0.454 -9044.267 4040.793
pulparticipants_lagged | 208.6391 229.0139 0.91 0.362 -240.2199 657.4981
pulpop_pcent | -1273.642 1640.604 -0.78 0.438 -4489.167 1941.883
pulpop_pcent_sq | 7.720792 13.53815 0.57 0.568 -18.8135 34.25509
popchange | -3308.617 8823.409 -0.37 0.708 -20602.18 13984.95
popchange_squared | -1131.539 2667.132 -0.42 0.671 -6359.022 4095.944
unemployment_ratio | 25729.64 25755.41 1.00 0.318 -24750.03 76209.31
cnrworker | 1481.686 1602.212 0.92 0.355 -1658.592 4621.964
hbclaimants | -724.8237 910.6557 -0.80 0.426 -2509.676 1060.029
cnrdegree | -374.939 832.4995 -0.45 0.652 -2006.608 1256.73
cnrparticipants_lagged | 886.1862 1084.372 0.82 0.414 -1239.145 3011.517
electionyear_a | -3175.043 5522.194 -0.57 0.565 -13998.34 7648.259
----------------------------------------------------------------------------------------
My question is, does the difference in p-values indicate that I am doing something wrong, and if so, what corrections should I make?
Thank you for any and all help provided.
Best wishes,
Adam
