CRE: A Model with Correlated Random Effects in Poisson and Probit Equations

Description

Estimate a model in panel counting data, in which the selection equation is a Probit model with random effects on individuals, and the outcome equation is a Poisson model with random effects on the same individuals. The random effects on the same individual are correlated across two equations.

Usage

CRE(sel_form, out_form, id, data = NULL, par = NULL, par_files = NULL,
  delta = 1, max_delta = 3, sigma = 1, max_sigma = 3, rho = 0,
  lower = c(rho = -1), upper = c(rho = 1), method = "L-BFGS-B",
  H = c(10, 10), psnH = 20, prbH = 20, accu = 10000, reltol = 1e-08,
  verbose = 0, tol_gtHg = Inf)

Value

A list containing the results of the estimated model

Arguments

sel_form: Formula for selection equation, a probit model with random effects
out_form: Formula for outcome equation, a Poisson model with random effects
id: A vector that represents the identity of individuals, numeric or character
data: Input data, a data frame
par: Starting values for estimates
par_files: Loading initial values from saved ProbitRE and PoissonRE estimates
delta: Variance of random effects in Probit model
max_delta: Largest allowed initial delta
sigma: Variance of random effects in Poisson model
max_sigma: Largest allowed initial sigma
rho: Correlation between random effects in Probit and Poisson models
lower: Lower bound for estiamtes
upper: Upper bound for estimates
method: Searching algorithm, don't change default unless you know what you are doing
H: A vector of length 2, specifying the number of points for inner and outer Quadratures
psnH: Number of Quadrature points for Poisson RE model
prbH: Number of Quddrature points for Probit RE model
accu: L-BFGS-B only, 1e12 for low accuracy; 1e7 for moderate accuracy; 10.0 for extremely high accuracy. See optim
reltol: Relative convergence tolerance. default typically 1e-8
verbose: Level of output during estimation. Lowest is 0.
tol_gtHg: tolerance on gtHg, not informative for L-BFGS-B

References

1. Jing Peng and Christophe Van den Bulte. Participation vs. Effectiveness of Paid Endorsers in Social Advertising Campaigns: A Field Experiment. Working Paper.

2. Jing Peng and Christophe Van den Bulte. How to Better Target and Incent Paid Endorsers in Social Advertising Campaigns: A Field Experiment. In Proceedings of the 2015 International Conference on Information Systems.

Examples

Run this code

# \donttest{
data(rt)
# Note: estimation may take 2~3 minutes
est = CRE(isRetweet~fans+tweets+as.factor(tweet.id),
                   num.words~fans+tweets+as.factor(tweet.id),
                   id=rt$user.id, data=rt)
# }