gets (version 0.29)

dlogitx: Estimate an autoregressive logit model with covariates

Description

Estimate a dynamic Autoregressive (AR) logit model with covariates ('X') by maximising the logit likelihood.

Usage

dlogitx(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, 
    plot = NULL)

Arguments

y

a binary numeric vector, time-series or zoo object. Missing values in the beginning and at the end of the series is allowed, as they are removed with the na.trim command

intercept

logical. TRUE, the default, includes an intercept in the logit specification, whereas FALSE does not

ar

either NULL (default) or an integer vector, say, c(2,4) or 1:4. The AR-lags to include in the logit specification. If NULL, then no lags are included

ewma

either NULL (default) or a list with arguments sent to the eqwma function. In the latter case a lagged moving average of y is included as a regressor

xreg

either NULL (default) or a numeric vector or matrix, say, a zoo object, of covariates. Note that, if both y and xreg are zoo objects, then their samples are chosen to match

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

NULL or an integer that determines the lag-length used in the robust coefficient covariance. If lag.length is an integer, then it is ignored unless method = 3

initial.values

NULL or a numeric vector with the initial parameter values passed on to the optimisation routine, nlminb. If NULL, the default, then the values are chosen automatically

lower

numeric vector, either of length 1 or the number of parameters to be estimated, see nlminb

upper

numeric vector, either of length 1 or the number of parameters to be estimated, see nlminb

control

a list passed on to the control argument of nlminb

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 tol argument of solve, which is called whenever the coefficient-coariance matrix is computed. The value controls the toleranse for detecting linear dependence between columns when inverting a matrix

plot

NULL or logical. If TRUE, then a plot is produced. If NULL (default), then the value set by options determines whether a plot is produced or not.

Value

A list of class 'dlogitx'.

Details

The function estimates a dynamic Autoregressive (AR) logit model with (optionally) covariates ('X') by maximising the logit likelihood. The estimated model is that of Kauppi and Saikkonen (2008). However, there (in contrast to here) estimation is by maximisation of the probit likelihood.

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

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.dlogitx, fitted.dlogitx, gets.dlogitx, logLik.dlogitx, plot.dlogitx, print.dlogitx, summary.dlogitx, toLatex.dlogitx and vcov.dlogitx

Related functions: dlogitxSim, logit, nlminb

Examples

# NOT RUN {
##simulate from ar(1):
set.seed(123) #for reproducibility
y <- dlogitxSim(100, ar=0.3)

##estimate ar(1) and store result:
mymod <- dlogitx(y, ar=1)

##estimate ar(4) and store result:
mymod <- dlogitx(y, ar=1:4)

##create some more data, estimate new model:
x <- matrix(rnorm(5*100), 100, 5)
mymod <- dlogitx(y, ar=1:4, xreg=x)

# }