logitx {gets} | R Documentation |
Estimate an autoregressive logit model with covariates
Description
Estimate a dynamic Autoregressive (AR) logit model with covariates ('X') by maximising the logit likelihood.
Usage
logitx(y, intercept = TRUE, ar = NULL, ewma = NULL, xreg = NULL,
vcov.type = c("ordinary", "robust"), lag.length = NULL,
initial.values = NULL, lower = -Inf, upper = Inf, control = list(),
eps.tol = .Machine$double.eps, solve.tol = .Machine$double.eps,
singular.ok = TRUE, plot = NULL)
dlogitx(y, ...)
Arguments
y |
a binary numeric vector, time-series or |
intercept |
logical. |
ar |
either |
ewma |
either |
xreg |
either |
vcov.type |
character vector of length 1, either "ordinary" (default) or "robust". Partial matching is allowed. If "ordinary", then the ordinary variance-covariance matrix is used for inference. If "robust", then a robust coefficient-covariance of the Newey and West (1987) type is used |
lag.length |
|
initial.values |
|
lower |
numeric vector, either of length 1 or the number of parameters to be estimated, see |
upper |
numeric vector, either of length 1 or the number of parameters to be estimated, see |
control |
a |
eps.tol |
numeric, a small value that ensures the fitted zero-probabilities are not too small when the log-transformation is applied when computing the log-likelihood |
solve.tol |
numeric value passed on to the |
singular.ok |
logical. If |
plot |
|
... |
arguments passed on to |
Details
The function estimates a dynamic Autoregressive (AR) logit model with (optionally) covariates ('X') by maximising the logit likelihood. The estimated model is an augmented version of the model considered by Kauppi and Saikkonen (2008). Also, they considered estimation is by maximisation of the probit likelihood. Here, by contrast, estimation is by maximisation of the logit likelihood.
Value
A list of class 'logitx'.
Author(s)
Genaro Sucarrat, http://www.sucarrat.net/
References
Heikki Kauppi and Pentti Saikkonen (2008): 'Predicting U.S. Recessions with Dynamic Binary Response Models'. The Review of Economics and Statistics 90, pp. 777-791
Whitney K. Newey and Kenned D. West (1987): 'A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix', Econometrica 55, pp. 703-708
See Also
Methods: coef.logitx
, fitted.logitx
, gets.logitx
,
logLik.logitx
, plot.logitx
, print.logitx
, summary.logitx
, toLatex.logitx
and vcov.logitx
Related functions: logitxSim
, logit
, nlminb
Examples
##simulate from ar(1):
set.seed(123) #for reproducibility
y <- logitxSim(100, ar=0.3)
##estimate ar(1) and store result:
mymod <- logitx(y, ar=1)
##estimate ar(4) and store result:
mymod <- logitx(y, ar=1:4)
##create some more data, estimate new model:
x <- matrix(rnorm(5*100), 100, 5)
mymod <- logitx(y, ar=1:4, xreg=x)