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