help lclogit2 postestimation

Title

lclogit2 postestimation -- Postestimation tools for
lclogit2 and lclogitml2

Description

lclogitpr2 predicts the probabilities of choice and class membership after lclogit2 or lclogitml2.

lclogitcov2 predicts the implied covariances of choice model coefficients after lclogit2 or lclogitml2.

lclogitwtp2 derives the ratios of coefficients, often called the willingness-to-pay (WTP), after lclogit2 or lclogitml2.

Notes: Both lclogitpr2 and lclogitcov2 offer the same functionalities as lclogitpr and lclogitcov of Pacifico and Yoo (2013), though internal codes have been revised.
The descriptions below borrow heavily from the help file for the latter two commands.

Syntax for lclogitpr2

lclogitpr2 [type] stubname [if] [in]
[,	class(numlist)
pr0
pr
up
cp
]

Description for lclogitpr2

lclogitpr2 predicts the probabilities of choosing each alternative in a choice situation (choice probabilities hereafter), the class shares or prior probabilities of class membership
and the posterior probabilities of class membership. The predicted probabilities are stored in a set of variables named stubname# where # refers to the relevant class number; the only exception is the unconditional choice probability as it is stored in a varable named stubname.

The command assumes pr when no other option is specified.

Options for lclogitpr2

class(numlist) specifies the classes for which the probabilities are going to be predicted. The default setting assumes all classes.

pr0 predicts the unconditional choice probability, which equals the average of the class-specific choice probabilities weighted by the corresponding class shares.

pr predicts the unconditional choice probability and the choice probabilities conditional on being in particular classes. This is the default option when no other option is specified.

up predicts the class shares or prior probabilities that the agent is in particular classes. They correspond to the class shares predicted by using the class memberhsip model coefficient estimates.

cp predicts the posterior probabilities that the agent is in particular classes taking into account her sequence of choices.

Syntax for lclogitcov2

lclogitcov2 varlist [if] [in]
[,	nokeep
varname(stubname)
covname(stubname)
matrix(name)
]

Description for lclogitcov2

lclogitcov2 predicts the implied variances and covariances of choice model coefficients using lclogit2 or lclogitml2 estimates.
The underlying formula is equation (6) in Pacifico and Yoo (2013, p.627). varlist must be variables with heterogeneous coefficients,
i.e. a subset or all of variables listed in option rand() of lclogit2 or lclogitml2.

The defaulting setting stores the predicted variances in a set of variables named var_1, var_2, ... where var_k is the predicted variance of the coefficient on the kth variable
listed in varlist, and the predicted covariances in cov_12, cov_13, ..., cov23, ... where cov_kj is the predicted covariance between the coefficients
on the kth variable and the jth variable in varlist.

The averages of these variance and covariances across agents (as identified by id() in lclogit2 or lclogitml2) in the prediction sample are reported as a covariance matrix at the end of lclogitcov2's execution.

Options for lclogitcov2

nokeep drops the predicted variances and covariances from the data set at the end of the command's execution. The average covariance matrix is still displayed.

varname(stubname) requests the predicted variances to be stored as stubname1,stubname2,....

covname(stubname) requests the predicted covariances to be stored as stubname12,stubname13,....

matrix(name) stores the reported average covariance matrix in a Stata matrix called name.

Syntax for lclogitwtp2

lclogitwtp2, income(varname)
[,	nonlcom
options_for_nlcom]
]

lclogitwtp2, cost(varname)
[,	nonlcom
options_for_nlcom]
]

Description for lclogitwtp2

lclogitwtp2 calculates the usual measures of willingness-to-pay (WTP), using active lclogit2 or lclogitml2 results for the choice model component
(i.e. results that are unrelated to the membership function). The WTP measures will be reported in a default table formatted like an lclogit2 output table.
When lclogitml2 results are active, lclogitwtp2 will use nlcom to derive standard errors and confidence intervals associated with the WTP measures;
the nlcom results will be presented after the default output table.

To derive the WTP for an independent variable in the choice model, lclogitwtp2 takes the ratio of the coefficient on that variable to the coefficient on varname in
income(varname), or alternatively to -1 times the coefficient on varname in cost(varname). Provided that the choice model's index function
is linear in varname, the denominator can be interpreted as the marginal utility of money, allowing the ratio to be interpreted as WTP. Both the numerator and denominator
will use the coefficients estimated for the same class.

Options for lclogitwtp2

income(varname) or cost(varname) is required. The former option should be used when the coefficient on varname is positive and
can be interpreted as the marginal utility of money. The latter option should be used when the coefficient on varname is negative and -1 times that coefficient
can be interpreted as the marginal utility of money.

nonlcom requests the nlcom step to be skipped. This option is relevant only when lclogitml2 results are active.

options_for_nlcom specifies options to be applied when executing nlcom. See nlcom. This option is relevant only when lclogitml2 results are active.
Perhaps the most useful option is post, which stores the nlcom results in e(), allowing the researcher to use test to carry out
single and joint hypothesis tests on the WTP measures.

Saved results for lclogitwtp2

lclogitwtp2 saves the following in r():

Matrices

r(WTP_rand) WTP measures that vary across classes

r(WTP_fix) WTP measures that do not vary across classes

Reference

Pacifico, D. and H. Yoo, 2013. lclogit: A Stata command for fitting latent-class conditional logit models via the expectation-maximization algorithm, Stata Journal 13, 625-639.

Author

Hong Il Yoo (h.i.yoo@durham.ac.uk): Durham University Business School, United Kingdom.

Also see

lclogit2, lclogitml2, nlcom, lclogitpr (if installed), lclogitcov (if installed)