ARMAX_SIM - ARMAX Model Simulation

Calculates the out-of-sample simulated values.

Syntax

ARMAX_SIM ([y], [x], order, [β], µ, σ, [φ], [θ], t, seed)

[Y]

Required. Is the response or the dependent variable time series data array (a one-dimensional array of cells (e.g., rows or columns)).

[X]

Required. Is the independent variables (exogenous factors) time series data matrix, so each column represents one variable.

Order

Optional. Is the time order in the data series (i.e., the first data point's corresponding date (earliest date = 1 (default), latest date = 0)).

Value	Order
1	Ascending (the first data point corresponds to the earliest date) (default).
0	Descending (the first data point corresponds to the latest date).

[β]

Optional. Is the coefficients array of the exogenous factors.

µ

Optional. Is the ARMA model long-run mean (i.e., mu). If missing, the process mean is assumed to be zero.

σ

Required. Is the standard deviation value of the model's residuals/innovations.

[φ]

Optional. Are the parameters of the AR(p) component model: [φ1, φ2 … φp] (starting with the lowest lag)

[θ]

Optional. Are the parameters of the MA(q) component model: [θ1, θ2 … θq] (starting with the lowest lag).

T

Required. Is the simulation time/horizon (expressed in steps beyond the end of the time series).

Seed

Required. Is an unsigned integer for setting up the random number generator(s).

Remarks

1. The underlying model is described here.
2. The Log Likelihood Function (LLF) is described here.
3. ARMAX_SIM returns an array of one simulation path starting from the end of the input data.
4. The response input data argument (i.e., latest observations) is optional. If omitted, an array of zeroes is assumed.
5. The number of observations in the factors (exogenous variables) input data must be greater than or equal to the size of response input data plus horizon.
6. The time series is homogeneous or equally spaced.
7. The time series may include missing values (e.g., #N/A) at either end.
8. The observation at any given time is examined using the response and factors value, so missing values (e.g., #N/A) in any input time series deem the whole observation missing.
9. The long-run mean can take any value or be omitted, in which case a zero value is assumed.
10. The residuals/innovations standard deviation (σ) must be greater than zero.
11. For the input argument - ([φ]):
• The input argument is optional and can be omitted, in which case no AR component is included.
• The order of the parameters starts with the lowest lag.
• One or more parameters may have a missing value or an error code (i.e., #NUM!, #VALUE!, etc.).
• The order of the AR component model is solely determined by the order of the last value in the array with a numeric value (vs. missing or error).
12. For the input argument - ([θ]):
• The input argument is optional and can be omitted, in which case no MA component is included.
• The order of the parameters starts with the lowest lag.
• One or more values in the input argument can be missing or an error code (i.e., #NUM!, #VALUE!, etc.).
• The order of the MA component model is solely determined by the order of the last value in the array with a numeric value (vs. missing or error).
13. The function ARMAX_SIM is available starting with version 1.63 SHAMROCK.

Files Examples

Related Links

• Wikipedia - Likelihood function.
• Wikipedia - Likelihood principle.
• Wikipedia - Autoregressive moving average model.

References

• James Douglas Hamilton; Time Series Analysis, Princeton University Press; 1st edition(Jan 11, 1994), ISBN: 691042896.
• Tsay, Ruey S.; Analysis of Financial Time Series, John Wiley & SONS; 2nd edition(Aug 30, 2005), ISBN: 0-471-690740.