help cub

Title

cub - Ordinal outcome model estimated by a mixture of a Uniform and a shifted Binomial

Syntax

cub
outcome
[if] [in]
[weight],
[pi(varlist_pi)
xi(varlist_xi)
shelter(#)
m(#)
prob(newvarname)
graph
outname(name)
save_graph(filename)]

fweights and pweights are allowed;
see weight.

Description

cub estimates a probability model for an ordinal outcome variable, where the probability to observe
a specific ordinal value (a preference for a given commodity, for instance) is modeled as a mixture of a Uniform and a shifted Binomial distribution.
The Uniform distribution models individual uncertainty in setting a preference, whereas the
shifted Binomial distribution is the law of probability governing individual feeling on the item.
The user can specify the covariates expected to drive individual uncertainty, as well as those possibly affecting individual feeling.
Furthermore, cub can handle categories presenting an exceptional large frequency (the so-called "shelters") thus
accounting for model inflation. The parameters of the cub model are estimated via maximum likelihood.

Options

pi(varlist_pi) specifes the covariates explaining the "uncertainty" parameter.

xi(varlist_xi) specifes the covariates explaining the "feeling" parameter.

shelter(#) specifies the "shelter", i.e. the category presenting an exceptional high frequency.

m(#) specifies the number of categories of the dependent variable, both observed and hidden (whenever hidden categories are present).
If this option is not specified, only observed categories are considered in the estimation phase.

prob(newvarname) allows to generate a new variable containing the model fitted probabilities.

graph allows to generate a graph displaying a plot of the actual and predicted probabilities.

outname(name) allows to specify a convenient name for the outcome variable to appear in the graph, when the 'graph' option is invoked.

save_graph(filename) allows to save the graph generated by the 'graph' option.

Example

********************************************************************************
* GENERATE A DATASET WITH M=5 (I.E. 5 PREFERENCE LEVELS)
********************************************************************************
. clear
. set more off
. set obs 3000
. set seed 10101
. global A=5
. gen Y=rpoisson($A)
. keep if Y>0 & Y<=$A
. gen x1=round(runiform(5,8))
. gen x2=round(runiform(1,6))
. gen x3=exp(round(runiform(3,5)))
. gen x4=rnormal(2,5)
********************************************************************************
* ESTIMATE THE MODEL VIA "cub"
********************************************************************************
. cub Y , pi(x3 x2 x1) xi(x3 x4) vce(oim)
********************************************************************************

Remarks

The outcome variable has to be coded as an integer taking value as 1, 2, 3,..., M.

References

Piccolo, D., and Simone, R. 2019a. The class of CUB models: statistical foundations, inferential issues and empirical evidence. Statistical Methods & Applications, Vol. 28, pp. 389–435.

Piccolo, D., and Simone, R. 2019b. Rejoinder to the discussion of "The class of cub models: statistical foundations, inferential issues and empirical evidence". Statistical Methods & Applications, Vol. 28, Issue 3, pp. 477–493.

Baum, F.C., Cerulli, G., Di Iorio, F., Piccolo, D., Simone, R. 2018, The Stata module CUB for fitting mixture models for ordinal data. Presented at: "The 2018 Italian Stata Users Group meeting", Bologna, 15 November.

Piccolo, D. 2006. Observed information matrix for cub models. Quaderni di Statistica, Vol. 8.

Author

Giovanni Cerulli

IRCrES-CNR

Research Institute for Sustainable Economic Growth, National Research Council of Italy

E-mail: giovanni.cerulli@ircres.cnr.it

Acknowledgements

I wish to thank Kit Baum, Francesca Di Iorio, Domenico Piccolo, and Rosaria Simone for their help and suggestions in improving this work. I am also grateful to the organizers and participants to the 2018 Italian Stata Users Group meeting held in Bologna (Italy) on the 15th of November 2018.

Also see

Online: scattercub , glm