# simulate

Monte Carlo simulation of univariate ARIMA or ARIMAX models

## Syntax

## Description

uses additional options specified by one or more name-value arguments.
`Y`

= simulate(`Mdl`

,`numobs`

,`Name=Value`

)`simulate`

returns numeric arrays when all optional input data are
numeric arrays. For example, `simulate(Mdl,100,NumPaths=1000,Y0=PS)`

returns a numeric array of 1000, 100-period simulated response paths from
`Mdl`

and specifies the numeric array of presample response data
`PS`

.

`[`

uses any input-argument combination in the previous syntaxes to return numeric arrays of
one or more independent series of model innovations `Y`

,`E`

,`V`

] = simulate(___)`E`

and, when
`Mdl`

represents a composite conditional mean and variance model,
conditional variances `V`

, resulting from simulating the ARIMA
model.

returns the table or timetable `Tbl`

= simulate(`Mdl`

,`numobs`

,Presample=`Presample`

,PresampleResponseVariable=`PresampleResponseVariable`

)`Tbl`

containing a variable for each of
the random paths of response, innovation, and conditional variance series resulting from
simulating the ARIMA model `Mdl`

. `simulate`

uses
the response variable `PresampleResponseVariable`

in the table or
timetable of presample data `Presample`

to initialize the response
series.* (since R2023b)*

To initialize the model using presample innovation or conditional variance data,
replace the `PresampleResponseVariable`

name-value argument with
`PresampleInnovationVariable`

or
`PresampleVarianceVariable`

name-value argument.

specifies the variables `Tbl`

= simulate(`Mdl`

,`numobs`

,InSample=`InSample`

,PredictorVariables=`PredictorVariables`

)`PredictorVariables`

in the in-sample table or
timetable of data `InSample`

containing the predictor data for the
exogenous regression component in the ARIMA model `Mdl`

.* (since R2023b)*

specifies presample response data to initialize the model and in-sample predictor data for
the exogenous regression component.`Tbl`

= simulate(`Mdl`

,`numobs`

,Presample=`Presample`

,PresampleResponseVariable=`PresampleResponseVariable`

,InSample=`InSample`

,PredictorVariables=`PredictorVariables`

)* (since R2023b)*

uses additional options specified by one or more name-value arguments, using any input
argument combination in the previous three syntaxes.`Tbl`

= simulate(___,`Name=Value`

)* (since R2023b)*

For example,
`simulate(Mdl,100,NumPaths=1000,Presample=PSTbl,PresampleResponseVariables="GDP")`

returns a timetable containing a variable for each of the response, innovations, and
conditional variance series. Each variable is a 100-by-1000 matrix representing 1000,
100-period paths simulated from the ARIMA model. `simulate`

initializes the model by using the presample data in the `GDP`

variable
of the timetable `PSTbl`

.

## Examples

### Return Vector of Simulated Response Path from ARIMA Model

Simulate a response path from an ARIMA model. Return the path in a vector.

Consider the ARIMA(4,1,1) model

$$(1-0.75{L}^{4})(1-L){y}_{t}=2+(1+0.1L){\epsilon}_{t},$$

where ${\epsilon}_{\mathit{t}}$ is a Gaussian innovations series with a mean of 0 and a variance of 1.

Create the ARIMA(4,1,1) model.

Mdl = arima(AR=-0.75,ARLags=4,MA=0.1,Constant=2,Variance=1)

Mdl = arima with properties: Description: "ARIMA(4,0,1) Model (Gaussian Distribution)" SeriesName: "Y" Distribution: Name = "Gaussian" P: 4 D: 0 Q: 1 Constant: 2 AR: {-0.75} at lag [4] SAR: {} MA: {0.1} at lag [1] SMA: {} Seasonality: 0 Beta: [1×0] Variance: 1

`Mdl`

is a fully specified `arima`

object representing the ARIMA(4,1,1) model.

Simulate a 100-period random response path from the ARIMA(4,1,1) model.

rng(1,"twister") % For reproducibility y = simulate(Mdl,100);

`y`

is a 100-by-1 vector containing the random response path.

Plot the simulated path.

plot(y) ylabel("y") xlabel("Time")

### Simulate Predictors and Responses

Simulate three predictor series and a response series.

Specify and simulate a path of length 20 for each of the three predictor series modeled by

$$(1-0.2L){x}_{it}=2+(1+0.5L-0.3{L}^{2}){\eta}_{it},$$

where $${\eta}_{it}$$ follows a Gaussian distribution with mean 0 and variance 0.01, and $$i$$ = {1,2,3}.

[MdlX1,MdlX2,MdlX3] = deal(arima(AR=0.2,MA={0.5 -0.3}, ... Constant=2,Variance=0.01)); rng(4,"twister"); % For reproducibility simX1 = simulate(MdlX1,20); simX2 = simulate(MdlX2,20); simX3 = simulate(MdlX3,20); SimX = [simX1 simX2 simX3];

Specify and simulate a path of length 20 for the response series modeled by

$$(1-0.05L+0.02{L}^{2}-0.01{L}^{3})(1-L{)}^{1}{y}_{t}=0.05+{x}_{t}^{\prime}\left[\begin{array}{c}0.5\\ -0.03\\ -0.7\end{array}\right]+(1+0.04L+0.01{L}^{2}){\epsilon}_{t},$$

where $${\epsilon}_{t}$$ follows a Gaussian distribution with mean 0 and variance 1.

```
MdlY = arima(AR={0.05 -0.02 0.01},MA={0.04 0.01}, ...
D=1,Constant=0.5,Variance=1,Beta=[0.5 -0.03 -0.7]);
simY = simulate(MdlY,20,X=SimX);
```

Plot the series together.

figure plot([SimX simY]) title("Simulated Series") legend("x_1","x_2","x_3","y")

### Forecast Process Using Simulations

Forecast the daily NASDAQ Composite Index using Monte Carlo simulations. Supply presample observations to initialize the model.

Load the NASDAQ data included with the toolbox. Extract the first 1500 observations for fitting.

```
load Data_EquityIdx
nasdaq = DataTable.NASDAQ(1:1500);
T = length(nasdaq);
```

Fit an ARIMA(1,1,1) model.

NasdaqModel = arima(1,1,1); NasdaqFit = estimate(NasdaqModel,nasdaq);

ARIMA(1,1,1) Model (Gaussian Distribution): Value StandardError TStatistic PValue _________ _____________ __________ __________ Constant 0.43031 0.18555 2.3191 0.020392 AR{1} -0.074391 0.081985 -0.90737 0.36421 MA{1} 0.31126 0.077266 4.0284 5.6158e-05 Variance 27.826 0.63625 43.735 0

Simulate 1000 paths with 500 observations each. Use the observed data as presample data.

```
rng(1,"twister");
Y = simulate(NasdaqFit,500,NumPaths=1000,Y0=nasdaq);
```

Plot the simulation mean forecast and approximate 95% forecast intervals.

lower = prctile(Y,2.5,2); upper = prctile(Y,97.5,2); mn = mean(Y,2); x = T + (1:500); figure plot(nasdaq,Color=[.7,.7,.7]) hold on h1 = plot(x,lower,"r:",LineWidth=2); plot(x,upper,"r:",LineWidth=2) h2 = plot(x,mn,"k",LineWidth=2); legend([h1 h2],"95% confidence interval","Simulation mean", ... Location="NorthWest") title("NASDAQ Composite Index Forecast") hold off

### Simulate Responses and Innovations

Simulate response and innovation paths from a multiplicative seasonal model.

Specify the model

$$(1-L)(1-{L}^{12}){y}_{t}=(1-0.5L)(1+0.3{L}^{12}){\epsilon}_{t},$$

where $${\epsilon}_{t}$$ follows a Gaussian distribution with mean 0 and variance 0.1.

```
Mdl = arima(MA=-0.5,SMA=0.3,SMALags=12,D=1, ...
Seasonality=12,Variance=0.1,Constant=0);
```

Simulate 500 paths with 100 observations each.

rng(1,"twister") % For reproducibility [Y,E] = simulate(Mdl,100,NumPaths=500); figure tiledlayout(2,1) nexttile plot(Y) title("Simulated Response") nexttile plot(E) title("Simulated Innovations")

Plot the 2.5th, 50th (median), and 97.5th percentiles of the simulated response paths.

lower = prctile(Y,2.5,2); middle = median(Y,2); upper = prctile(Y,97.5,2); figure plot(1:100,lower,"r:",1:100,middle,"k", ... 1:100,upper,"r:") legend("95% confidence interval","Median")

Compute statistics across the second dimension (across paths) to summarize the sample paths.

Plot a histogram of the simulated paths at time 100.

```
figure
histogram(Y(100,:),10)
title("Response Distribution at Time 100")
```

### Supply Presample Data in Timetable

Fit an ARIMA(1,1,1) model to the weekly average NYSE closing prices. Supply a timetable of presample responses to initialize the model and return a timetable of simulated values from the model.

**Load Data**

Load the US equity index data set `Data_EquityIdx`

.

```
load Data_EquityIdx
T = height(DataTimeTable)
```

T = 3028

The timetable `DataTimeTable`

includes the time series variable `NYSE`

, which contains daily NYSE composite closing prices from January 1990 through December 2001.

Plot the daily NYSE price series.

```
figure
plot(DataTimeTable.Time,DataTimeTable.NYSE)
title("NYSE Daily Closing Prices: 1990 - 2001")
```

**Prepare Timetable for Estimation**

When you plan to supply a timetable, you must ensure it has all the following characteristics:

The selected response variable is numeric and does not contain any missing values.

The timestamps in the

`Time`

variable are regular, and they are ascending or descending.

Remove all missing values from the timetable, relative to the NYSE price series.

```
DTT = rmmissing(DataTimeTable,DataVariables="NYSE");
T_DTT = height(DTT)
```

T_DTT = 3028

Because all sample times have observed NYSE prices, `rmmissing`

does not remove any observations.

Determine whether the sampling timestamps have a regular frequency and are sorted.

`areTimestampsRegular = isregular(DTT,"days")`

`areTimestampsRegular = `*logical*
0

areTimestampsSorted = issorted(DTT.Time)

`areTimestampsSorted = `*logical*
1

`areTimestampsRegular = 0`

indicates that the timestamps of `DTT`

are irregular. `areTimestampsSorted = 1`

indicates that the timestamps are sorted. Business day rules make daily macroeconomic measurements irregular.

Remedy the time irregularity by computing the weekly average closing price series of all timetable variables.

DTTW = convert2weekly(DTT,Aggregation="mean"); areTimestampsRegular = isregular(DTTW,"weeks")

`areTimestampsRegular = `*logical*
1

T_DTTW = height(DTTW)

T_DTTW = 627

`DTTW`

is regular.

```
figure
plot(DTTW.Time,DTTW.NYSE)
title("NYSE Daily Closing Prices: 1990 - 2001")
```

**Create Model Template for Estimation**

Suppose that an ARIMA(1,1,1) model is appropriate to model NYSE composite series during the sample period.

Create an ARIMA(1,1,1) model template for estimation.

Mdl = arima(1,1,1);

`Mdl`

is a partially specified `arima`

model object.

**Fit Model to Data**

`infer`

requires `Mdl.P`

presample observations to initialize the model. `infer`

backcasts for necessary presample responses, but you can provide a presample.

Partition the data into presample and in-sample, or estimation sample, observations.

T0 = Mdl.P; DTTW0 = DTTW(1:T0,:); DTTW1 = DTTW((T0+1):end,:);

Fit an ARIMA(1,1,1) model to the in-sample weekly average NYSE closing prices. Specify the response variable name, presample timetable, and the presample response variable name.

EstMdl = estimate(Mdl,DTTW1,ResponseVariable="NYSE", ... Presample=DTTW0,PresampleResponseVariable="NYSE");

ARIMA(1,1,1) Model (Gaussian Distribution): Value StandardError TStatistic PValue ________ _____________ __________ ___________ Constant 0.83624 0.453 1.846 0.064891 AR{1} -0.32862 0.23526 -1.3968 0.16246 MA{1} 0.42703 0.22613 1.8885 0.058965 Variance 56.065 1.8433 30.416 3.3809e-203

`EstMdl`

is a fully specified, estimated `arima`

model object.

**Simulate Model**

Simulate the fitted model 20 weeks beyond the final period. Specify the entire in-sample data as a presample and the presample response variable name in the in-sample timetable.

rng(1,"twister") % For reproducibility numobs = 20; Tbl = simulate(EstMdl,numobs,Presample=DTTW1, ... PresampleResponseVariable="NYSE"); tail(Tbl)

Time Y_Response Y_Innovation Y_Variance ___________ __________ ____________ __________ 05-Apr-2002 564.68 -11.302 56.065 12-Apr-2002 570.47 6.5582 56.065 19-Apr-2002 570.39 -1.8179 56.065 26-Apr-2002 571.72 1.249 56.065 03-May-2002 557.94 -14.716 56.065 10-May-2002 547.51 -9.5098 56.065 17-May-2002 556.51 8.7992 56.065 24-May-2002 573.34 15.194 56.065

size(Tbl)

`ans = `*1×2*
20 3

`Tbl`

is a 20-by-3 timetable containing the simulated response path `NYSE_Response`

, the corresponding simulated innovation path `NYSE_Innovation`

, and the constant variance path `NYSE_Variance`

(`Mdl.Variance = 56.065`

).

Plot the simulated responses.

figure plot(DTTW1.Time((end-50):end),DTTW1.NYSE((end-50):end), ... Color=[0.7 0.7 0.7],LineWidth=2); hold on plot(Tbl.Time,Tbl.Y_Response,LineWidth=2); legend("Observed","Simulated responses") title("Weekly Average NYSE CLosing Prices") hold off

### Supply Timetable of Presample and Predictor Data

Fit an ARIMAX(1,1,1) model to the weekly average NYSE closing prices. Include an exogenous regression term identifying whether a measurement observation occurs during a recession. Supply timetables of presample and in-sample exogenous data. Simulate the weekly average closing prices over a 10-week horizon, and compare the forecasts to held out data.

Load the US equity index data set `Data_EquityIdx`

.

```
load Data_EquityIdx
T = height(DataTimeTable)
```

T = 3028

Remedy the time irregularity by computing the weekly average closing price series of all timetable variables.

```
DTTW = convert2weekly(DataTimeTable,Aggregation="mean");
T_DTTW = height(DTTW)
```

T_DTTW = 627

Load the US recessions data set.

load Data_Recessions RDT = datetime(Recessions,ConvertFrom="datenum", ... Format="yyyy-MM-dd");

Determine whether each sampling time in the data occurs during a US recession.

isrecession = @(x)any(isbetween(x,RDT(:,1),RDT(:,2))); DTTW.IsRecession = arrayfun(isrecession,DTTW.Time)*1;

`DTTW`

contains a variable `IsRecession`

, which represents the exogenous variable in the ARIMAX model.

Create an ARIMA(1,1,1) model template for estimation. Set the response series name to `NYSE`

.

```
Mdl = arima(1,1,1);
Mdl.SeriesName = "NYSE";
```

Partition the data into required presample, in-sample (estimation sample), and 10 holdout sample observations.

T0 = Mdl.P; T2 = 10; DTTW0 = DTTW(1:T0,:); DTTW1 = DTTW((T0+1):(end-T2),:); DTTW2 = DTTW((end-T2+1):end,:);

Fit an ARIMA(1,1,1) model to the in-sample weekly average NYSE closing prices. Specify the presample timetable, the presample response variable name, and the in-sample predictor variable name.

EstMdl = estimate(Mdl,DTTW1,Presample=DTTW0,PresampleResponseVariable="NYSE", ... PredictorVariables="IsRecession");

ARIMAX(1,1,1) Model (Gaussian Distribution): Value StandardError TStatistic PValue ________ _____________ __________ ___________ Constant 1.0635 0.50013 2.1264 0.033468 AR{1} -0.33828 0.2088 -1.6201 0.1052 MA{1} 0.43879 0.20045 2.189 0.028593 Beta(1) -2.425 1.0861 -2.2327 0.02557 Variance 55.527 1.9255 28.838 7.0988e-183

Simulate 1000 paths of responses from the fitted model into a 10-week horizon. Specify the in-sample data as a presample, the presample response variable name, the predictor data in the forecast horizon, and the predictor variable name.

rng(1,"twister") Tbl = simulate(EstMdl,T2,Presample=DTTW1,PresampleResponseVariable="NYSE", ... InSample=DTTW2,PredictorVariables="IsRecession",NumPaths=1000);

`Tbl`

is a 10-by-6 timetable containing all variables in `InSample`

, and the following variables:

`NYSE_Response`

: a 10-by-1000 matrix of 1000 random response paths simulated from the model.`NYSE_Innovation`

: a 10-by-1000 matrix of 1000 random innovation paths filtered through the model to product the response paths`NYSE_Variance`

: a 10-by-1000 matrix containing only the constant`Mdl.Variance = 55.527`

.

Plot the observed responses, median of the simulated responses, a 95% percentile intervals of the simulated values.

SimStats = quantile(Tbl.NYSE_Response,[0.025 0.5 0.975],2); figure h1 = plot(DTTW.Time((end-50):end),DTTW.NYSE((end-50):end),"k",LineWidth=2); hold on plot(Tbl.Time,Tbl.NYSE_Response,Color=[0.8 0.8 0.8]) h2 = plot(Tbl.Time,SimStats(:,2),'r--'); h3 = plot(Tbl.Time,SimStats(:,[1 3]),'b--'); legend([h1 h2 h3(1)],["Observations" "Sim. medians" "95% percentile intervals"]) title("NYSE Weekly Average Price Series and Monte Carlo Forecasts")

## Input Arguments

`numobs`

— Sample path length

positive integer

Sample path length, specified as a positive integer. `numobs`

is the number of random observations to generate per output path.

**Data Types: **`double`

`Presample`

— Presample data

table | timetable

*Since R2023b*

Presample data containing paths of responses
*y _{t}*, innovations

*ε*, or conditional variances

_{t}*σ*

_{t}^{2}to initialize the model, specified as a table or timetable with

`numprevars`

variables and `numpreobs`

rows.`simulate`

returns the simulated variables in the output table
or timetable `Tbl`

, which is the same type as
`Presample`

. If `Presample`

is a timetable,
`Tbl`

is a timetable that immediately follows
`Presample`

in time with respect to the sampling frequency.

Each selected variable is a single path (`numpreobs`

-by-1 vector)
or multiple paths (`numpreobs`

-by-`numprepaths`

matrix) of `numpreobs`

observations representing the presample of
`numpreobs`

observations of responses, innovations, or conditional
variances.

Each row is a presample observation, and measurements in each row occur
simultaneously. The last row contains the latest presample observation.
`numpreobs`

must be one of the following values:

At least

`Mdl.P`

when`Presample`

provides only presample responsesAt least

`Mdl.Q`

when`Presample`

provides only presample disturbances or conditional variancesAt least

`max([Mdl.P Mdl.Q])`

otherwise

When `Mdl.Variance`

is a conditional variance model,
`simulate`

can require more than the minimum required number of
presample values.

If `numpreobs`

exceeds the minimum number,
`simulate`

uses the latest required number of observations
only.

If `numprepaths`

> `NumPaths`

,
`simulate`

uses only the first `NumPaths`

columns.

If `Presample`

is a timetable, all the following conditions must
be true:

If `Presample`

is a table, the last row contains the latest
presample observation.

By default, `simulate`

sets the following values:

For necessary presample responses:

The unconditional mean of the model when

`Mdl`

represents a stationary AR process without a regression componentZero when

`Mdl`

represents a nonstationary process or when it contains a regression component.

For necessary presample disturbances, zero.

For necessary presample conditional variances, the unconditional variance of the conditional variance model n

`Mdl.Variance`

.

If you specify the `Presample`

, you must specify the presample
response, innovation, or conditional variance variable name by using the
`PresampleResponseVariable`

,
`PresampleInnovationVariable`

, or
`PresampleVarianceVariable`

name-value argument.

`PresampleResponseVariable`

— Response variable *y*_{t} to select from `Presample`

string scalar | character vector | integer | logical vector

_{t}

*Since R2023b*

Response variable *y _{t}* to select from

`Presample`

containing presample response data, specified as one of
the following data types:String scalar or character vector containing a variable name in

`Presample.Properties.VariableNames`

Variable index (positive integer) to select from

`Presample.Properties.VariableNames`

A logical vector, where

`PresampleResponseVariable(`

selects variable) = true`j`

from`j`

`Presample.Properties.VariableNames`

The selected variable must be a numeric matrix and cannot contain missing values
(`NaN`

s).

If you specify presample response data by using the `Presample`

name-value argument, you must specify
`PresampleResponseVariable`

.

**Example: **`PresampleResponseVariable="Stock0"`

**Example: **`PresampleResponseVariable=[false false true false]`

or
`PresampleResponseVariable=3`

selects the third table variable as
the presample response variable.

**Data Types: **`double`

| `logical`

| `char`

| `cell`

| `string`

`InSample`

— In-sample predictor data

table | timetable

*Since R2023b*

In-sample predictor data for the exogenous regression component of the model,
specified as a table or timetable. `InSample`

contains
`numvars`

variables, including `numpreds`

predictor
variables *x _{t}*.

`simulate`

returns the simulated variables in the output table
or timetable `Tbl`

, which is commensurate with
`InSample`

.

Each row corresponds to an observation in the simulation horizon, the first row is
the earliest observation, and measurements in each row, among all paths, occur
simultaneously. `InSample`

must have at least
`numobs`

rows to cover the simulation horizon. If you supply more
rows than necessary, `simulate`

uses only the first
`numobs`

rows.

Each selected predictor variable is a numeric vector without missing values
(`NaN`

s). All predictor variables are present in the regression
component of each response equation and apply to all response paths.

If `InSample`

is a timetable, the following conditions apply:

If `InSample`

is a table, the last row contains the latest
observation.

By default, `simulate`

does not include the regression
component in the model, regardless of the value of `Mdl.Beta`

.

`PredictorVariables`

— Exogenous predictor variables *x*_{t} to select from `InSample`

string vector | cell vector of character vectors | vector of integers | logical vector

_{t}

Exogenous predictor variables *x _{t}* to select
from

`InSample`

containing predictor data for the regression component,
specified as one of the following data types:String vector or cell vector of character vectors containing

`numpreds`

variable names in`InSample.Properties.VariableNames`

A vector of unique indices (positive integers) of variables to select from

`InSample.Properties.VariableNames`

A logical vector, where

`PredictorVariables(`

selects variable) = true`j`

from`j`

`InSample.Properties.VariableNames`

The selected variables must be numeric vectors and cannot contain missing values
(`NaN`

s).

By default, `simulate`

excludes the regression component,
regardless of its presence in `Mdl`

.

**Example: **```
PredictorVariables=["M1SL" "TB3MS"
"UNRATE"]
```

**Example: **`PredictorVariables=[true false true false]`

or
`PredictorVariable=[1 3]`

selects the first and third table variables
to supply the predictor data.

**Data Types: **`double`

| `logical`

| `char`

| `cell`

| `string`

### 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.*

**Example: **`simulate(Mdl,10,NumPaths=1000,Y0=y0)`

simulates
`1000`

sample paths of length `10`

from the ARIMA model
`Mdl`

, and uses the observations in `y0`

as a presample
to initialize each generated path.

`NumPaths`

— Number of independent sample paths to generate

`1`

(default) | positive integer

Number of independent sample paths to generate, specified as a positive integer.

**Example: **`NumPaths=1000`

**Data Types: **`double`

`Y0`

— Presample response data *y*_{t}

numeric column vector | numeric matrix

_{t}

Presample response data *y _{t}* used as
initial values for the model, specified as a

`numpreobs`

-by-1 numeric
column vector or a `numpreobs`

-by-`numprepaths`

numeric matrix. Use `Y0`

only when you supply optional data inputs as
numeric arrays.`numpreobs`

is the number of presample observations.
`numprepaths`

is the number of presample response paths.

Each row is a presample observation (sampling time), and measurements in each row
occur simultaneously. The last row contains the latest presample observation.
`numpreobs`

must be at least `Mdl.P`

to initialize
the AR model component. If `numpreobs`

> `Mdl.P`

,
`simulate`

uses the latest required number of observations
only.

Columns of `Y0`

are separate, independent presample paths. The
following conditions apply:

If

`Y0`

is a column vector, it represents a single response path.`simulate`

applies it to each output path.If

`Y0`

is a matrix,`simulate`

applies`Y0(:,`

to initialize path)`j`

.`j`

`Y0`

must have at least`NumPaths`

columns;`simulate`

uses only the first`NumPaths`

columns of`Y0`

.

By default, `simulate`

sets any necessary presample
responses to one of the following values:

The unconditional mean of the model when

`Mdl`

represents a stationary AR process without a regression componentZero when

`Mdl`

represents a nonstationary process or when it contains a regression component

**Data Types: **`double`

`E0`

— Presample innovation data *ε*_{t}

numeric column vector | numeric matrix

_{t}

Presample innovation data *ε _{t}* used to
initialize either the moving average (MA) component of the ARIMA model or the
conditional variance model, specified as a

`numpreobs`

-by-1 numeric
column vector or a `numpreobs`

-by-`numprepaths`

matrix. Use `E0`

only when you supply optional data inputs as numeric
arrays.Each row is a presample observation (sampling time), and measurements in each row
occur simultaneously. The last row contains the latest presample observation.
`numpreobs`

must be at least `Mdl.Q`

to initialize
the MA model component. If `Mdl.Variance`

is a conditional variance
model (for example, a `garch`

model object),
`simulate`

can require more rows than
`Mdl.Q`

. If `numpreobs`

is larger than required,
`simulate`

uses the latest required number of observations
only.

Columns of `E0`

are separate, independent presample paths. The
following conditions apply:

If

`E0`

is a column vector, it represents a single residual path.`simulate`

applies it to each output path.If

`E0`

is a matrix,`simulate`

applies`E0(:,`

to initialize simulating path)`j`

.`j`

`E0`

must have at least`NumPaths`

columns;`simulate`

uses only the first`NumPaths`

columns of`E0`

.

By default, `simulate`

sets the necessary presample
disturbances to zero.

**Data Types: **`double`

`V0`

— Presample conditional variance data *σ*_{t}^{2}

positive numeric column vector | positive numeric matrix

_{t}

Presample conditional variance data
*σ _{t}*

^{2}used to initialize the conditional variance model, specified as a

`numpreobs`

-by-1 positive numeric column vector or a
`numpreobs`

-by-`numprepaths`

positive numeric
matrix. If the conditional variance `Mdl.Variance`

is constant,
`simulate`

ignores `V0`

. Use
`V0`

only when you supply optional data inputs as numeric
arrays.Each row is a presample observation (sampling time), and measurements in each row
occur simultaneously. The last row contains the latest presample observation.
`numpreobs`

must be at least `Mdl.Q`

to initialize
the conditional variance model in `Mdl.Variance`

. For details, see
the `simulate`

function of conditional variance
models. If `numpreobs`

is larger than required,
`simulate`

uses the latest required number of observations
only.

Columns of `V0`

are separate, independent presample paths. The
following conditions apply:

If

`V0`

is a column vector, it represents a single path of conditional variances.`simulate`

applies it to each output path.If

`V0`

is a matrix,`simulate`

applies`V0(:,`

to initialize simulating path)`j`

.`j`

`V0`

must have at least`NumPaths`

columns;`simulate`

uses only the first`NumPaths`

columns of`V0`

.

By default, `simulate`

sets all necessary presample
observations to the unconditional variance of the conditional variance process.

**Data Types: **`double`

`PresampleInnovationVariable`

— Residual variable *e*_{t} to select from `Presample`

string scalar | character vector | integer | logical vector

_{t}

*Since R2023b*

Residual variable *e _{t}* to select from

`Presample`

containing the presample residual data, specified as
one of the following data types:String scalar or character vector containing a variable name in

`Presample.Properties.VariableNames`

Variable index (positive integer) to select from

`Presample.Properties.VariableNames`

A logical vector, where

`PresampleInnovationVariable(`

selects variable) = true`j`

from`j`

`Presample.Properties.VariableNames`

The selected variable must be a numeric matrix and cannot contain missing values
(`NaN`

s).

If you specify presample residual data by using the `Presample`

name-value argument, you must specify
`PresampleInnovationVariable`

.

**Example: **`PresampleInnovationVariable="StockRateDist0"`

**Example: **`PresampleInnovationVariable=[false false true false]`

or
`PresampleInnovationVariable=3`

selects the third table variable as
the presample innovation variable.

**Data Types: **`double`

| `logical`

| `char`

| `cell`

| `string`

`PresampleVarianceVariable`

— Conditional variance variable *σ*_{t}^{2} to select from
`Presample`

string scalar | character vector | integer | logical vector

_{t}

*Since R2023b*

Conditional variance variable
*σ _{t}*

^{2}to select from

`Presample`

containing presample conditional variance data,
specified as one of the following data types:String scalar or character vector containing a variable name in

`Presample.Properties.VariableNames`

Variable index (positive integer) to select from

`Presample.Properties.VariableNames`

A logical vector, where

`PresampleVarianceVariable(`

selects variable) = true`j`

from`j`

`Presample.Properties.VariableNames`

The selected variable must be a numeric vector and cannot contain missing values
(`NaN`

s).

If you specify presample conditional variance data by using the
`Presample`

name-value argument, you must specify
`PresampleVarianceVariable`

.

**Example: **`PresampleVarianceVariable="StockRateVar0"`

**Example: **`PresampleVarianceVariable=[false false true false]`

or
`PresampleVarianceVariable=3`

selects the third table variable as
the presample conditional variance variable.

**Data Types: **`double`

| `logical`

| `char`

| `cell`

| `string`

`X`

— Exogenous predictor data

numeric matrix

Exogenous predictor data for the regression component in the model, specified as a
numeric matrix with `numpreds`

columns. `numpreds`

is the number of predictor variables (`numel(Mdl.Beta)`

). Use
`X`

only when you supply optional data inputs as numeric
arrays.

Each row of `X`

corresponds to a period in the length
`numobs`

simulation sample (period for which
`simulate`

simulates observations; the period after the
presample). `X`

must have at least `numobs`

rows.
The last row contains the latest predictor data. If `X`

has more than
`numobs`

rows, `simulate`

uses only the
latest `numobs`

rows.

`simulate`

does not use the regression component in the
presample period.

Columns of `X`

are separate predictor variables.

`simulate`

applies `X`

to each simulated
path; that is, `X`

represents one path of observed predictors.

By default, `simulate`

excludes the regression component,
regardless of its presence in `Mdl`

.

**Data Types: **`double`

**Note**

`NaN`

values in`X`

,`Y0`

,`E0`

, and`V0`

indicate missing values.`simulate`

removes missing values from specified data by list-wise deletion.For the presample,

`simulate`

horizontally concatenates the possibly jagged arrays`Y0`

,`E0`

, and`V0`

with respect to the last rows, and then it removes any row of the concatenated matrix containing at least one`NaN`

.For in-sample data,

`simulate`

removes any row of`X`

containing at least one`NaN`

.

This type of data reduction reduces the effective sample size and can create an irregular time series.

For numeric data inputs,

`simulate`

assumes that you synchronize the presample data such that the latest observations occur simultaneously.`simulate`

issues an error when any table or timetable input contains missing values.

## Output Arguments

`Y`

— Simulated response paths *y*_{t}

numeric column vector | numeric matrix

_{t}

Simulated response paths *y _{t}*, returned as a

`numobs`

-by-1 numeric column vector or a `numobs`

-by-`NumPaths`

numeric matrix. `simulate`

returns `Y`

by default and when you supply optional data in numeric arrays.`Y`

represents the continuation of the presample responses in `Y0`

.

Each row corresponds to a period in the simulated series; the simulated series has the periodicity of `Mdl`

. Each column is a separate simulated path.

`E`

— Simulated model innovations paths
*ε*_{t}

numeric column vector | numeric matrix

_{t}

`V`

— Simulated conditional variance paths *σ*_{t}^{2}

numeric column vector | numeric matrix

_{t}

Simulated conditional variance paths
*σ _{t}*

^{2}of the mean-zero innovations associated with

`Y`

, returned as a
`numobs`

-by-1 numeric column vector or a
`numobs`

-by-`NumPaths`

numeric matrix.
`simulate`

returns `V`

by default and when
you supply optional data in numeric arraysThe dimensions of `V`

correspond to the dimensions of
`Y`

.

`Tbl`

— Simulated response *y*_{t}, innovation *ε*_{t}, and conditional variance
*σ*_{t}^{2}
paths

table | timetable

_{t}

_{t}

_{t}

*Since R2023b*

Simulated response *y _{t}*, innovation

*ε*, and conditional variance

_{t}*σ*

_{t}^{2}paths, returned as a table or timetable, the same data type as

`Presample`

or `InSample`

. `simulate`

returns
`Tbl`

only when you supply at least one of the inputs
`Presample`

and `InSample`

.`Tbl`

contains the following variables:

The simulated response paths, which are in a

`numobs`

-by-`NumPaths`

numeric matrix, with rows representing observations and columns representing independent paths. Each path represents the continuation of the corresponding presample path in`Presample`

, or each path corresponds, in time, with the rows of`InSample`

.`simulate`

names the simulated response variable in`Tbl`

, where_Response`responseName`

is`responseName`

`Mdl.SeriesName`

. For example, if`Mdl.SeriesName`

is`StockReturns`

,`Tbl`

contains a variable for the corresponding simulated response paths with the name`StockReturns_Response`

.The simulated innovation paths, which are in a

`numobs`

-by-`NumPaths`

numeric matrix, with rows representing observations and columns representing independent paths. Each path represents the continuation of the corresponding presample path in`Presample`

, or each path corresponds, in time, with the rows of`InSample`

.`simulate`

names the simulated innovation variable in`Tbl`

, where_Innovation`responseName`

is`responseName`

`Mdl.SeriesName`

. For example, if`Mdl.SeriesName`

is`StockReturns`

,`Tbl`

contains a variable for the corresponding simulated innovation paths with the name`StockReturns_Innovation`

.The simulated conditional variance paths, which are in a

`numobs`

-by-`NumPaths`

numeric matrix, with rows representing observations and columns representing independent paths. Each path represents the continuation of the corresponding presample path in`Presample`

, or each path corresponds, in time, with the rows of`InSample`

.`simulate`

names the simulated conditional variance variable in`Tbl`

, where_Variance`responseName`

is`responseName`

`Mdl.SeriesName`

. For example, if`Mdl.SeriesName`

is`StockReturns`

,`Tbl`

contains a variable for the corresponding simulated conditional variance paths with the name`StockReturns_Variance`

.When you supply

`InSample`

,`Tbl`

contains all variables in`InSample`

.

If `Tbl`

is a timetable, the following conditions hold:

The row order of

`Tbl`

, either ascending or descending, matches the row order of`Preample`

.If you specify

`InSample`

, row times`Tbl.Time`

are`InSample.Time(1:numobs)`

. Otherwise,`Tbl.Time(1)`

is the next time after`Presample(end)`

relative to the sampling frequency, and`Tbl.Time(2:numobs)`

are the following times relative to the sampling frequency.

## References

[1] Box, George E. P., Gwilym M. Jenkins, and Gregory C. Reinsel. *Time Series Analysis: Forecasting and Control*. 3rd ed. Englewood Cliffs, NJ: Prentice Hall, 1994.

[2] Enders, Walter. *Applied Econometric Time Series*. Hoboken, NJ: John Wiley & Sons, Inc., 1995.

[3] Hamilton, James D. *Time Series Analysis*. Princeton, NJ: Princeton University Press, 1994.

## Version History

**Introduced in R2012a**

### R2023a: `simulate`

accepts input data in tables and timetables, and returns results in tables and timetables

In addition to accepting presample and in-sample predictor data in numeric arrays,
`simulate`

accepts input data in tables or regular timetables. When
you supply input data in a table or timetable, the following conditions apply:

If you specify optional presample response, innovation, or conditional variance data to initialize the model, you must also specify corresponding variable names containing the data to use.

If you specify optional in-sample predictor data for the exogenous regression component of the model, you must also specify corresponding predictor variable names containing the data to use.

`simulate`

returns results in a table or timetable.

Name-value arguments to support tabular workflows include:

`Presample`

specifies the input table or regular timetable of presample innovations and conditional variance data.`PresampleResponseVariable`

specifies the variable name of the response paths to select from`Presample`

.`PresampleInnovationVariable`

specifies the variable name of the innovation paths to select from`Presample`

.`PresampleVarianceVariable`

specifies the variable name of the conditional variance paths to select from`Presample`

.`InSample`

specifies the input table or regular timetable of in-sample predictor data.`PredictorVariables`

specifies the names of the predictor series to select from`InSample`

for a model regression component.

## See Also

### Objects

### Functions

### Topics

- Simulate Stationary Processes
- Simulate Trend-Stationary and Difference-Stationary Processes
- Simulate Multiplicative ARIMA Models
- Simulate Conditional Mean and Variance Models
- 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

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