fit_EXPAR {EXPAR} | R Documentation |
Fitting of EXPAR model with known parameters
Description
Fits an EXPAR model to the dataset for given parameter values, without any optimisation.
Usage
fit_EXPAR(ts_data, par)
Arguments
ts_data |
A univariate data to which the model is to be fitted, preferably (but not limited to) a |
par |
The parameter vector. Any value from the parametric space of EXPAR model is permissible. Number of required parameters is |
Details
This function uses the form of EXPAR model to generate consecutive fitted observations as theoretical values obtained from the model. The form of EXPAR model employed is given by,
where, and
is white noise process with zero mean and constant variance
The process of fitting involves using actual observations in the dataset in the RHS of above equation and obtaining the fitted values . No optimization is done as parameter estimation is omitted. It is useful for simulation of data with given order and parameters.
Value
Returns the fitted EXPAR model as a list with the following components,
Fitted |
Fitted values obtained from LHS of the aforementioned model. Due to obvious reasons, fits of the first |
Residuals |
The residuals |
RSS |
The residual sum of squares. |
AIC |
Akaike information criterion, evaluated from |
AIC_c |
Corrected Akaike information criterion, evaluated from |
BIC |
Bayesian information criterion, evaluated from |
Examples
datats <- ts(egg_price_index[,3], start = c(2013, 1), frequency = 12)
fit_EXPAR(datats, par = c(0.45,0.68,0.48))