{smcl} {title:mgf_unbal} {p 4 4 2} Computes the average marginal effect (AME) of an explanatory variable after {helpb xtprobitunbal}. {title:Syntax} {p 8 8 2} {bf:mgf_unbal} {ifin}, dydx({it:marginvar}) [val0(#) val1(#)] {p 4 4 2} where {it:marginvar} should fit to one of the following cases: {bf:lag}, {bf:d.{varname}} or {bf:c.{varname}}. {col 5}{it:marginvar} case{col 25}{it:Description} {space 4}{hline 72} {col 5}{bf:lag}{col 25}to compute the effect of a discrete change (from 0 {col 5}{bf: }{col 25}{bf: } to 1) of the lagged dependent variable. {col 5}{bf:d.varname}{col 25}to compute the effect of a discrete change of the {col 5}{bf: }{col 25}{bf: } variable varname. {col 5}{bf:c.varname}{col 25}to compute the marginal effect of an infinitesimal {col 5}{bf: }{col 25}{bf: } change of the continuous variable varname. {space 4}{hline 72} {p 4 4 2} When {bf:d.varname} is specified, the user can use the options {bf:val0(#)} and {bf:val1(#)}. The command will compute the marginal effect of a discrete change in the variable {it:varname} when it changes from the value set in {it:val0} to the value set in {it:val1}. Defaults values are {bf:val0(0)} and {bf:val1(1)}. {title:Examples} {p 4 4 2} Setup: the examples bellow require the package to be installed with ancillary files {p 4 4 2} {bf:. ssc install xtprobitunbal, all replace} {p 4 4 2} Load the data {p 4 4 2} {bf:. sysuse exportunbal} Estimate the model (see {helpb xtprobitunbal} for further details) {p 4 4 2} {bf:. xtprobitunbal export size trend med_skill age, meansvars(size med_skill)} {p 4 4 2} Marginal effect of the lagged dependent variable {break} {p 4 4 2} {bf:. mgf_unbal, dydx(lag)} {p 4 4 2} Marginal effect of a continuous change in an exogenous variable {p 4 4 2} {bf:. mgf_unbal, dydx(c.med_skill)} {p 4 4 2} Marginal effect of a discrete change in an exogenous variable {p 4 4 2} {bf:. mgf_unbal, dydx(d.age)} {p 4 4 2} Marginal effect of a discrete change, from 2 to 3, in an exogenous variable {p 4 4 2} {bf:. mgf_unbal, dydx(d.age) val0(2) val1(3)} {title:Stored results} {p 4 4 2} {bf:mgf_unbal} stores the following in {bf:r()}: {p 4 4 2}{bf:Macros:} {p 8 8 2} {bf: r(nobsAME)} : total number of observations used in computing the AME {p 8 8 2} {bf: r(ngr_AME)} : number of groups (individuals) used in computing the AME {p 8 8 2} {bf: r(AME)} : average marginal effect (AME) {p 8 8 2} {bf: r(seAME)} : standard error of AME {p 8 8 2} {bf: r(zst_AME)} : test statistic {p 8 8 2} {bf:r(pval_AME)} : p-value {title:Authors} {p 4 4 2} Pedro Albarran {break} Universidad de Alicante {break} {it:albarran@ua.es} {break} {p 4 4 2} Raquel Carrasco {break} Universidad Carlos III de Madrid {break} {it:rcarras@eco.uc3m.es} {p 4 4 2} Jesus M. Carro {break} Universidad Carlos III de Madrid {break} {it:jcarro@eco.uc3m.es} {break} {title:License} {p 4 4 2} This code is licensed under GPLv3 {title:References} {p 4 4 2} Albarran, P., R. Carrasco and J. Carro. 2019. {browse "https://onlinelibrary.wiley.com/doi/abs/10.1111/obes.12308":Estimation of Dynamic Nonlinear Random Effects Models with Unbalanced Panels}. {it:Oxford Bulletin of Economics and Statistics}, 81(6), 1424-1441.