Rchoice: Estimate discrete choice model with random parameters

In Rchoice: Discrete Choice (Binary, Poisson and Ordered) Models with Random Parameters

Estimate discrete choice model with random parameters

Description

Estimation of discrete choice models such as Binary (logit and probit), Poisson and Ordered (logit and probit) model with random coefficients for cross-sectional and panel data using simulated maximum likelihood.

Usage

Rchoice(
  formula,
  data,
  subset,
  weights,
  na.action,
  family,
  start = NULL,
  ranp = NULL,
  R = 40,
  haltons = NA,
  seed = NULL,
  correlation = FALSE,
  panel = FALSE,
  index = NULL,
  mvar = NULL,
  print.init = FALSE,
  init.ran = 0.1,
  gradient = TRUE,
  ...
)

## S3 method for class 'Rchoice'
terms(x, ...)

## S3 method for class 'Rchoice'
model.matrix(object, ...)

## S3 method for class 'Rchoice'
coef(object, ...)

## S3 method for class 'Rchoice'
fitted(object, ...)

## S3 method for class 'Rchoice'
residuals(object, ...)

## S3 method for class 'Rchoice'
df.residual(object, ...)

## S3 method for class 'Rchoice'
update(object, new, ...)

## S3 method for class 'Rchoice'
logLik(object, ...)

## S3 method for class 'Rchoice'
print(
  x,
  digits = max(3, getOption("digits") - 3),
  width = getOption("width"),
  ...
)

## S3 method for class 'Rchoice'
summary(object, ...)

## S3 method for class 'summary.Rchoice'
print(
  x,
  digits = max(3, getOption("digits") - 3),
  width = getOption("width"),
  ...
)

Arguments

formula	a symbolic description of the model to be estimated. The formula consists in two parts. The first one is reserved for standard variables with fixed and random parameters. The second one is reserved for variables that enter in the mean of the random parameters. See for example rFormula,
data	the data. It may be a pdata.frame object or an ordinary data.frame,
subset	an optional vector specifying a subset of observations,
weights	an optional vector of weigths,
na.action	a function wich indicated what should happen when the data contains NA's,
family	the distribution to be used. It might be family = binomial("probit") for a Probit Model, family = binomial("logit") for a Logit model, family = ordinal("probit") for an Ordered Probit Model, family = ordinal("logit") for a Ordered Logit Model for an Ordered Logit Model, and family = "poisson" for a Poisson Model,
start	a vector of starting values,
ranp	a named vector whose names are the random parameters and values the distribution: "n" for normal, "ln" for log-normal, "cn" for truncated normal, "u" for uniform, "t" for triangular, "sb" for Johnson Sb,
R	the number of draws if ranp is not NULL,
haltons	only relevant if ranp is not NULL. If not NULL, halton sequence is used instead of pseudo-random numbers. If haltons=NA, some default values are used for the prime of the sequence and for the number of element dropped. Otherwise, haltons should be a list with elements prime and drop,
seed	the seed for the pseudo-random draws. This is only relevant if haltons = NULL,
correlation	only relevant if ranp is not NULL. If TRUE, the correlation between random parameters is taken into account,
panel	if TRUE a panel data model is estimated,
index	a string indicating the ‘id’ for individuals in the data. This argument is not required if data is a pdata.frame object,
mvar	only valid if ranp is not NULL. This is a named list, where the names correspond to the variables with random parameters, and the values correspond to the variables that enter in the mean of each random parameters,
print.init	if TRUE, the initial values for the optimization procedure are printed,
init.ran	initial values for standard deviation of random parameters. Default is 0.1,
gradient	if FALSE, numerical gradients are used for the optimization procedure of models with random parameters,
...	further arguments passed to maxLik,
x, object	and object of class Rchoice,
new	an updated formula for the update method,
digits	number of digits,
width	width,

Details

The models are estimated using the maxLik function from maxLik package.

If ranp is not NULL, the random parameter model is estimated. A random parameter model or random coefficient models permits regression parameter to vary across individuals according to some distribution. A fully parametric random parameter model specifies the latent variable y^{*} conditional on regressors x and given parameters \beta_i to have conditional density f(y|x, \beta_i) where \beta_i are iid with density g(\beta_i|\theta_i). The density is assumed a priori by the user by the argument ranp. If the parameters are assumed to be normally distributed \beta_i ~ N(\beta, \Sigma), then the random parameter are constructed as:

\beta_{ir}=\beta+L\omega_{ir}

where LL'=\Sigma and \omega_{ir} is the r-th draw from standard normal distribution for individual i.

Once the model is specified by the argument family, the model is estimated using Simulated Maximum Likelihood (SML). The probabilities, given by f(y|x, \beta_i), are simulated using R pseudo-draws if halton=NULL or R halton draws if halton = NA. The user can also specified the primes and the number of dropped elements for the halton draws. For example, if the model consists of two random parameters, the user can specify haltons = list("prime" = c(2, 3), "drop" = c(11, 11)).

A random parameter hierarchical model can be estimated by including heterogeneity in the mean of the random parameters:

\beta_{ir}=\beta+\pi's_i+L\omega_{ir}

Rchoice manages the variables in the hierarchical model by the formula object: all the hierarchical variables (s_i) are included after the | symbol. The argument mvar indicate which variables enter in each random parameter. See examples below

Value

An object of class “Rchoice”, a list elements:

coefficients	the named vector of coefficients,
family	type of model,
link	distribution of the errors,
logLik	a set of values of the maximum likelihood procedure,
mf	the model framed used,
formula	the formula (a Formula object),
time	proc.time() minus the start time,
freq	frequency of dependent variable,
draws	type of draws used,
R.model	TRUE if a random parameter model is fitted,
R	number of draws used,
bi	an array of dimension N \times R \times K with the individual parameters,
Qir	matrix of dimension N \times R representing P_{ir}/\sum_r P_{ir},
ranp	vector indicating the variables with random parameters and their distribution,
probabilities	the fitted probabilities for each individuals,
residuals	the residuals,
call	the matched call.

Author(s)

Mauricio Sarrias [e-mail]

References

Greene, W. H. (2012). Econometric Analysis. 7 edition. Prentice Hall.

Train, K. (2009). Discrete Choice Methods with Simulation. Cambridge university press.

See Also

plot.Rchoice, effect.Rchoice

Examples

## Probit model
data("Workmroz")
probit <- Rchoice(lfp ~ k5 + k618 + age + wc + hc + lwg + inc,  
                 data = Workmroz, family = binomial('probit'))
summary(probit)

## Poisson model
data("Articles")
poisson <- Rchoice(art ~ fem + mar + kid5 + phd + ment, data = Articles, family = poisson)
summary(poisson)

## Ordered probit model
data("Health")
oprobit <- Rchoice(newhsat ~ age + educ + hhinc + married + hhkids, 
data = Health, family = ordinal('probit'), subset = year == 1988)
summary(oprobit)

## Poisson Model with Random Parameters
poisson.ran <- Rchoice(art ~ fem + mar + kid5 + phd + ment, 
                       data = Articles,  family = poisson,
                       ranp = c(kid5 = "n", phd = "n", ment = "n"))
summary(poisson.ran)                      

## Poisson Model with Correlated Random Parameters
poissonc.ran <- Rchoice(art ~ fem + mar + kid5 + phd + ment, 
                       data = Articles, 
                       ranp = c(kid5 = "n", phd = "n", ment = "n"), 
                       family = poisson, 
                       correlation =  TRUE,
                       R = 20)
summary(poissonc.ran)

## Hierarchical Poisson Model
poissonH.ran <- Rchoice(art ~ fem + mar + kid5 + phd + ment | fem + phd,
                       data = Articles,
                       ranp = c(kid5 = "n", phd = "n", ment = "n"),
                       mvar = list(phd = c("fem"), ment = c("fem", "phd")),
                       family = poisson,
                       R = 10)
summary(poissonH.ran)

## Ordered Probit Model with Random Effects and Random Parameters
Health$linc <- log(Health$hhinc)
oprobit.ran <- Rchoice(newhsat ~ age + educ + married + hhkids + linc,
                     data = Health[1:2000, ],
                     family = ordinal('probit'),
                     ranp = c(constant = "n", hhkids = "n", linc = "n"),
                     panel = TRUE,
                     index = "id",
                     R = 10,
                     print.init = TRUE)
summary(oprobit.ran)
 

Rchoice documentation built on March 31, 2023, 11:13 p.m.