Dear Statalist

I am working on triple hurdle model as introduced by BURKE et al, (2015)-doi: 10.1093/ajae/aav009. Thanks to separability feature of the log likelihood function, I am able to estimate the betas of my equations with ease. However, I understand that the marginal effect of the explanatory variables on my outcome variables should take into account the parameters and explanatory variables from all three stages. Thus in order to compute the marginal effect I intend to use the expression option on the margins command. Since I do not have effective identification available in the data, I could not estimate the model simultaneous using say Roodman's "cmp" command (Roodman, D. 2011. Estimating fully observed recursive mixed-process models with cmp. Stata Journal 11(2): 159-206).

The formula I need to code looks like this

and my code looks like this

When I run the above code I keep getting empty tables

Any help would be appreciated

Kind regards

Kaleb

I am working on triple hurdle model as introduced by BURKE et al, (2015)-doi: 10.1093/ajae/aav009. Thanks to separability feature of the log likelihood function, I am able to estimate the betas of my equations with ease. However, I understand that the marginal effect of the explanatory variables on my outcome variables should take into account the parameters and explanatory variables from all three stages. Thus in order to compute the marginal effect I intend to use the expression option on the margins command. Since I do not have effective identification available in the data, I could not estimate the model simultaneous using say Roodman's "cmp" command (Roodman, D. 2011. Estimating fully observed recursive mixed-process models with cmp. Stata Journal 11(2): 159-206).

The formula I need to code looks like this

and my code looks like this

Code:

global pdvarlist1 // explanatory variables for the first hurdle equationglobal mdvarlist2// explanatory variables for the first hurdle equationglobal indvarlist3 // explanatory variables for the outcome equation//individually estimating the modelsprobit srprod $pd //the first hurdle model predict srp, xb oprobit nsrmposio $md //the second hurdle model predict srmp_n, xb reg lnetsell $id //the outcome equations predict sell, xb predict sigma, stdp margins, expression (normal(srp)*normal(srmp_n - 0.1206016)*exp(sell +(sigma^2)/2)) margins, expression (normal(srp)*normal(srmp_n - 0.1206016)*exp(sell +(sigma^2)/2)) dydx(*)

Code:

. margins, expression (normal(srp)*normal(srmp_n - 0.1206016)*exp(sell +(sigma^2)/2)) Warning: expression() does not contain predict() or xb(). Predictive margins Number of obs = 1,208 Model VCE : OLS Expression : normal(srp)*normal(srmp_n - 0.1206016)*exp(sell +(sigma^2)/2) ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _cons | .9553197 . . . . . ------------------------------------------------------------------------------ . margins, expression (normal(srp)*normal(srmp_n - 0.1206016)*exp(sell +(sigma^2)/2)) dydx(age15_64) Warning: expression() does not contain predict() or xb(). Average marginal effects Number of obs = 1,208 Model VCE : OLS Expression : normal(srp)*normal(srmp_n - 0.1206016)*exp(sell +(sigma^2)/2) dy/dx w.r.t. : age15_64 ------------------------------------------------------------------------------ | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age15_64 | 0 (omitted) ------------------------------------------------------------------------------

Kind regards

Kaleb

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