Description Usage Arguments Details Value Author(s) References See Also Examples
Computes the likelihood ratio of the partially cointegrated model vs the null model
1 2 3 4 5 | likelihood_ratio.pci(Y, X,
robust = FALSE,
null_model = c("rw", "ar1"),
pci_opt_method = c("jp", "twostep"),
nu = 5)
|
Y |
The time series that is to be modeled. A plain or |
X |
A (possibly |
robust |
If |
null_model |
This specifies the model that is assumed under the null hypothesis.
|
pci_opt_method |
Method to be used for fitting Y to X.
|
nu |
If |
First searches for the optimal fit under the null model, and computes the log of the likelihood score of this fit. Then, searches for the optimal fit under the full model, and computes the log of the likelihood score of this fit. Returns the difference of the two likelihood scores. Since the null model is nested in the full model, the log likelihood ratio score is guaranteed to be negative.
The log of the ratio of the likelihoods of the two models.
Matthew Clegg matthewcleggphd@gmail.com
Christopher Krauss christopher.krauss@fau.de
Jonas Rende jonas.rende@fau.de
Clegg, Matthew, 2015. Modeling Time Series with Both Permanent and Transient Components using the Partially Autoregressive Model. Available at SSRN: http://ssrn.com/abstract=2556957
fit.pci
Fitting partially cointegrated models
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