Simulate from a dynamic logit-x model

Simulate from a dynamic Autoregressive (AR) logit model with covariates ('X'). This model is essentially a logit-version of the model of Kauppi and Saikkonen (2008).

Usage

logitxSim(n, intercept = 0, ar = NULL, xreg = NULL, verbose = FALSE, 
    as.zoo = TRUE)

dlogitxSim(n, ...)

Arguments

n: integer, the number of observations to generate
intercept: numeric, the value of the intercept in the logit specification
ar: NULL or a numeric vector with the autoregressive parameters
xreg: NULL or numeric vector with the values of the X-term
verbose: logical. If FALSE, then only the binary process (a vector) is returned. If TRUE, then a matrix with all the simulated information is returned (binary process, probabilities, etc.)
as.zoo: logical. If TRUE, then the returned object - a vector or matrix - will be of class zoo
...: arguments passed on to logitxSim

Details

No details, for the moment.

Value

A vector or matrix, depending on whether verbose is FALSE or TRUE, of class zoo, depending on whether as.zoo is TRUE or FALSE

References

Heikki Kauppi and Penti Saikkonen (2008): 'Predicting U.S. Recessions with Dynamic Binary Response Models'. The Review of Economic Statistics 90, pp. 777-791

Author

Genaro Sucarrat, http://www.sucarrat.net/

Examples


##simulate from ar(1):
set.seed(123) #for reproducibility
y <- logitxSim(100, ar=0.3)

##more output (value, probability, logit):
set.seed(123) #for reproducibility
y <- logitxSim(100, ar=0.3, verbose=TRUE)