AIRLINE_SIM - Simulated values of an Airline Model

Jacquie Nesbitt

October 24, 2016 18:07

Calculates the out-of-sample conditional simulated values

Syntax

AIRLINE_SIM(X, Order, mean, sigma, s, theta, theta2, T, seed)

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).

T

is the forecast time/horizon (expressed in terms of steps beyond end of the time series).

seed

is an unsigned integer for setting up the random number generator(s)

The underlying model is described here.
ARMA_SIM returns an array of one simulation path starting from the end of the input data.
The input data argument (i.e. latest observations) is optional. If omitted, an array of zeroes is assumed.
The time series is homogeneous or equally spaced.
The time series may include missing values (e.g. #N/A) at either end.
The long-run mean argument (mean) can take any value or be omitted, in which case a zero value is assumed.
The value of the residuals/innovations standard deviation (sigma) must be positive.
The season length must be greater than one.
The input argument for the non-seasonal MA parameter - theta - is optional and can be omitted, in which case no non-seasonal MA component is included.
The input argument for the seasonal MA parameter - theta2 - is optional and can be omitted, in which case no seasonal MA component is included.
The function was added in version 1.63 SHAMROCK.

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