View source: R/vcov.stepmented.R
vcov.stepmented | R Documentation |
Returns the variance-covariance matrix of the parameters estimates (including breakpoints) of a fitted stepmented model object.
## S3 method for class 'stepmented'
vcov(object, k=NULL, zero.cor=TRUE, type=c("cdf", "none", "abs"), ...)
object |
a fitted model object of class "stepmented", returned by any |
k |
The power of |
zero.cor |
If |
type |
How the covariance matrix should be computed. If |
... |
additional arguments. |
The full covariance matrix is based on the smooth approximation
I(x>\psi)\approx \Phi((x-\psi)/n^{k})
via the sandwich formula using the empirical information matrix and assuming x \in [0,1]
. \Phi(\cdot)
is the standard Normal cdf, and k
is the argument k
. When k=NULL
(default), it is computed via
k=-(0.6 + 0.5 \ \log(snr)/\sqrt snr - (|\hat\psi-0.5|/n)^{1/2})
where snr
is the signal-to-noise ratio corresponding to the estimated changepoint \hat\psi
(in the range (0,1)). The above formula comes from extensive simulation studies under different scenarios: Seo and Linton (2007) discuss using the normal cdf to smooth out the indicator function by suggesting \log(n)/n^{1/2}
as bandwidth; we found such suggestion does not perform well in practice.
The full matrix of the estimated covariances between the parameter estimates, including the breakpoints.
The function, including the value of k
, must be considered at preliminary stage. Currently the value of k
appears to overestimate slightly the true \hat\psi
variability.
If the fit object
has been called by stepmented(.., var.psi=TRUE)
, then vcov.stepmented
will return object$vcov
, unless the power k
differs from -2/3
.
Vito Muggeo
Seo MH, Linton O (2007) A smoothed least squares estimator for threshold regression models, J of Econometrics, 141: 704-735
stepmented
##see ?stepmented
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.