Returns an array of cells for the initial/quick guess of the model's parameters
Syntax
AIRLINE_PARAM(X, Order, mean, sigma, s, theta, theta2, Type, maxIter)
- X
- is the univariate time series data (one dimensional array of cells (e.g. rows or columns)).
- Order
- is the time order in the data series (i.e. the first data point's corresponding date (earliest date=1 (default), latest date=0)).
Order Description 1 ascending (the first data point corresponds to the earliest date) (default) 0 descending (the first data point corresponds to the latest date) - mean
- is the model mean (i.e. mu).
- sigma
- is the standard deviation of the model's residuals/innovations.
- s
- is the length of seasonality (expressed in terms of lags, where s > 1).
- theta
- is the coefficient of non-seasonal MA component (see model description).
- theta2
- is the coefficient of seasonal MA component (see model description).
- Type
- is an integer switch to select the output array: (1=Quick Guess (default), 2= Calibrated , 3=Std. Errors)
Order Description 1 Quick guess (non-optimal) of parameters values (default) 2 Calibrated (optimal) values for the model's parameters 3 Standard error of the parameters' values. - maxIter
- is the maximum number of iterations used to calibrate the model. If missing, the default maximum of 100 is assumed.
Remarks
- The underlying model is described here.
- The time series is homogeneous or equally spaced.
- The time series may include missing values (e.g. #N/A) at either end.
- AIRLINE_PARAM returns an array of the values (or errors) of the model's parameters in the following order:
- $\mu$
- $\theta$
- $\Theta$
- $\sigma$
- The AIRLINE_GUESS sets the $\mu$ and $\sigma$ equal to the differenced sample (i.e. $Z_t=(1-L)(1-L^s)Y_t$) average, and standard deviation respectively, and it sets the $\theta = 0$ and $\Theta=0$
- The function was added in version 1.63 SHAMROCK.
Examples
Example 1:
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| Formula | Description (Result) | |
|---|---|---|
| =AIRLINE_AIC(Sheet1!$B$2:$B$15,1,$D$3,$D$6,$D$7,$D$4,$D$5) | 65.6 | Akaike's information criterion (AIC) |
| =AIRLINE_LLF(Sheet1!$B$2:$B$15,1,$D$3,$D$6,$D$7,$D$4,$D$5) | -25.47 | Log-Likelihood Function |
| =AIRLINE_CHECK($D$3,$D$6,$D$7,$D$4,$D$5) | 1 | Is the AIRLINE model stable? |
Files Examples
Related Links
References
- Hamilton, J .D.; Time Series Analysis , Princeton University Press (1994), ISBN 0-691-04289-6
- Tsay, Ruey S.; Analysis of Financial Time Series John Wiley & SONS. (2005), ISBN 0-471-690740
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