simulate
Class: regARIMA
Monte Carlo simulation of regression model with ARIMA errors
Syntax
[Y,E] =
simulate(Mdl,numObs)
[Y,E,U]
= simulate(Mdl,numObs)
[Y,E,U]
= simulate(Mdl,numObs,Name,Value)
Description
[
simulates
one sample path of observations (Y
,E
] =
simulate(Mdl
,numObs
)Y
) and innovations
(E
) from the regression model with ARIMA time series
errors, Mdl
. The software simulates numObs
observations
and innovations per sample path.
[
additionally
simulates unconditional disturbances, Y
,E
,U
]
= simulate(Mdl
,numObs
)U
.
[
simulates
sample paths with additional options specified by one or more Y
,E
,U
]
= simulate(Mdl
,numObs
,Name,Value
)Name,Value
pair
arguments.
Input Arguments
|
Regression model with ARIMA errors, specified as a The properties of |
|
Number of observations (rows) to generate for each path of |
Name-Value Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.
Before R2021a, use commas to separate each name and value, and enclose
Name
in quotes.
|
Presample innovations that have mean 0 and provide initial values
for the ARIMA error model, specified as the comma-separated pair consisting
of
Default: |
|
Number of sample paths (columns) to generate for Default: |
|
Presample unconditional disturbances that provide initial values
for the ARIMA error model, specified as the comma-separated pair consisting
of
Default: |
|
Predictor data in the regression model, specified as the comma-separated
pair consisting of The columns of Default: |
Notes
NaN
s inE0
,U0
, andX
indicate missing values andsimulate
removes them. The software merges the presample data sets (E0
andU0
), then uses list-wise deletion to remove anyNaN
s.simulate
similarly removesNaN
s fromX
. RemovingNaN
s in the data reduces the sample size, and can also create irregular time series.simulate
assumes that you synchronize presample data such that the latest observation of each presample series occurs simultaneously.All predictors (i.e., columns in
X
) are associated with each response path inY
.
Output Arguments
|
Simulated responses, returned as a |
|
Simulated, mean 0 innovations, returned as a |
|
Simulated unconditional disturbances, returned as a |
Examples
References
[1] Box, G. E. P., G. M. Jenkins, and G. C. Reinsel. Time Series Analysis: Forecasting and Control. 3rd ed. Englewood Cliffs, NJ: Prentice Hall, 1994.
[2] Davidson, R., and J. G. MacKinnon. Econometric Theory and Methods. Oxford, UK: Oxford University Press, 2004.
[3] Enders, W. Applied Econometric Time Series. Hoboken, NJ: John Wiley & Sons, Inc., 1995.
[4] Hamilton, J. D. Time Series Analysis. Princeton, NJ: Princeton University Press, 1994.
[5] Pankratz, A. Forecasting with Dynamic Regression Models. John Wiley & Sons, Inc., 1991.
[6] Tsay, R. S. Analysis of Financial Time Series. 2nd ed. Hoboken, NJ: John Wiley & Sons, Inc., 2005.
See Also
regARIMA
| estimate
| filter
| forecast
| infer
Topics
- Alternative ARIMA Model Representations
- Simulate Stationary Processes
- Simulate Trend-Stationary and Difference-Stationary Processes
- Monte Carlo Simulation of Conditional Mean Models
- Presample Data for Conditional Mean Model Simulation
- Transient Effects in Conditional Mean Model Simulations
- Monte Carlo Forecasting of Conditional Mean Models