# infer

Infer univariate ARIMA or ARIMAX model residuals or conditional variances

## Syntax

## Description

returns the table or timetable `Tbl2`

= infer(`Mdl`

,`Tbl1`

)`Tbl2`

containing paths of
residuals and conditional variances inferred from the model
`Mdl`

and the response data in the input table or
timetable `Tbl1`

. * (since R2023b)*

`infer`

selects the response variable named in
`Mdl.SeriesName`

or the sole variable in
`Tbl1`

. To select a different response variable in
`Tbl1`

to infer residuals and conditional variances, use
the `ResponseVariable`

name-value argument.

`[___] = infer(___,`

specifies options using one or more name-value arguments in
addition to any of the input argument combinations in previous syntaxes.
`Name=Value`

)`infer`

returns the output argument combination for the
corresponding input arguments. For example, `infer(Mdl,Y,Y0=PS,X=Pred)`

infers
residuals from the numeric vector of responses `Y`

with
respect to the ARIMAX `Mdl`

, and specifies the numeric vector
of presample response data `PS`

to initialize the model and the
exogenous predictor data `Pred`

for the regression
component.

## Examples

### Infer Residuals From Model and Vector of Response Data

Infer residuals from an AR model by supplying a hypothetical response series in a vector.

Specify an AR(2) model using known parameters.

```
Mdl = arima(AR={0.5 -0.8},Constant=0.002, ...
Variance=0.8);
```

Simulate response data with 100 observations.

```
rng(1,"twister");
Y = simulate(Mdl,100);
```

`Y`

is a 100-by-1 vector containing a random response path drawn from `Mdl`

.

Infer residuals for all corresponding responses.

E = infer(Mdl,Y);

`E`

is a 100-by-1 vector containing a residuals corresponding to `Y`

, with respect to `Mdl`

. By default, `infer`

backcasts for required presample observations.

Plot the residuals.

```
figure
plot(E)
title("Inferred Residuals")
```

### Infer Conditional Variances

Infer the conditional variances from an AR(1) and GARCH(1,1) composite model. Return the loglikelihood value.

Specify an AR(1) model using known parameters. Set the variance equal to a `garch`

model.

Mdl = arima(AR={0.8 -0.3},Constant=0); MdlVar = garch(Constant=0.0002,GARCH=0.6,ARCH=0.2); Mdl.Variance = MdlVar;

Simulate response data with 100 observations.

```
rng(1,"twister")
Y = simulate(Mdl,100);
```

Infer residuals and conditional variances for the entire response series. Compute the loglikelihood at the simulated data.

[E,V,logL] = infer(Mdl,Y); logL

logL = 209.6405

`E`

and `V`

are 100-by-1 vectors of inferred residuals and conditional variances, given the response data and model.

Plot the conditional variances.

```
figure
plot(V)
title("Inferred Conditional Variances")
```

### Supply Presample Responses

Infer residuals from an AR model by supplying a hypothetical response series in a vector. Supply presample responses to initialize the model.

Specify an AR(2) model using known parameters.

```
Mdl = arima(AR={0.5 -0.8},Constant=0.002, ...
Variance=0.8)
```

Mdl = arima with properties: Description: "ARIMA(2,0,0) Model (Gaussian Distribution)" SeriesName: "Y" Distribution: Name = "Gaussian" P: 2 D: 0 Q: 0 Constant: 0.002 AR: {0.5 -0.8} at lags [1 2] SAR: {} MA: {} SMA: {} Seasonality: 0 Beta: [1×0] Variance: 0.8

Consider inferring residuals from a response series of length `T`

= 100. Because the model requires `Mdl.P`

responses to initialize the model, simulate `T + Mdl.P`

= 102 responses from the model.

```
rng(1,"twister");
T = 100;
TSim = T + Mdl.P;
y = simulate(Mdl,TSim);
```

`Y`

is a 102-by-1 vector representing a random response path drawn from the model.

Infer residuals from the last `T`

response and use the first `Mdl.P`

observations as a presample to initialize the model.

E = infer(Mdl,y((Mdl.P+1):end),Y0=y(1:Mdl.P)); size(E)

`ans = `*1×2*
100 1

`E`

is a 100-by-1 vector containing a residuals corresponding to the last 100 observations of `y`

, with respect to `Mdl`

.

Plot the residuals.

```
figure
plot(E)
title("Inferred Residuals")
```

### Infer Residuals From Model and Response Data in Timetable

*Since R2023b*

Fit an ARIMA(1,1,1) model to the weekly average NYSE closing prices. Supply timetables of in-sample and presample data for the fit. Then, infer the residuals from the fit.

**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.

**Infer Residuals**

Infer the residuals from the fitted model and in-sample observations. Specify the response variable name, presample timetable, and the presample response variable name.

Tbl2 = infer(EstMdl,DTTW1,ResponseVariable="NYSE", ... Presample=DTTW0,PresampleResponseVariable="NYSE"); tail(Tbl2)

Time NYSE NASDAQ Y_Residual Y_Variance ___________ ______ ______ __________ __________ 16-Nov-2001 577.11 1886.9 5.8649 56.065 23-Nov-2001 583 1898.3 5.3303 56.065 30-Nov-2001 581.41 1925.8 -2.7678 56.065 07-Dec-2001 584.96 1998.1 3.3787 56.065 14-Dec-2001 574.03 1981 -12.038 56.065 21-Dec-2001 582.1 1967.9 8.7774 56.065 28-Dec-2001 590.28 1967.2 6.2526 56.065 04-Jan-2002 589.8 1950.4 -1.3008 56.065

size(Tbl2)

`ans = `*1×2*
625 4

`Tbl2`

is a 625-by-4 timetable containing all variables in `DTTW1`

, and the inferred residuals from the fit `NYSE_Response`

and constant variance paths `NYSE_Variance`

(`Mdl.Variance = 56.065`

).

### Compute Fitted Responses

*Since R2023b*

Fit an ARIMA(1,1,1) model to the weekly average NYSE closing prices. Supply a timetable of data and specify the series for the fit. Then, compute fitted responses.

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

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 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 presample timetable, and the presample response variable name.

EstMdl = estimate(Mdl,DTTW1,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

Infer the residuals from the fitted model and in-sample observations. Specify the presample timetable, and the presample response variable name.

Tbl2 = infer(EstMdl,DTTW1,Presample=DTTW0, ... PresampleResponseVariable="NYSE");

Compute fitted response values by subtracting the residuals from the observed response series.

Tbl2.YHat = Tbl2.NYSE - Tbl2.NYSE_Residual;

Plot the observed responses and the fitted values.

figure plot(Tbl2.Time,[Tbl2.NYSE Tbl2.YHat]) legend("Observations","Fitted values") title("NYSE Weekly Average Price Series")

The fitted values closely track the observations.

Plot the residuals versus the fitted values.

figure plot(Tbl2.YHat,Tbl2.NYSE_Residual,".",MarkerSize=15) ylabel("Residuals") xlabel("Fitted Values") title("Residual Plot")

Residual variance appears larger for larger fitted values. One remedy for this behavior is to apply the log transform to the data.

### Infer Residuals from ARMAX Model

Infer residuals from an ARMAX model.

Specify an ARMA(1,2) model using known parameters for the response (`MdlY`

) and an AR(1) model for the predictor data (`MdlX`

).

```
MdlY = arima(AR=0.2,MA={-0.1,0.6},Constant=1, ...
Variance=2,Beta=3)
```

MdlY = arima with properties: Description: "ARIMAX(1,0,2) Model (Gaussian Distribution)" SeriesName: "Y" Distribution: Name = "Gaussian" P: 1 D: 0 Q: 2 Constant: 1 AR: {0.2} at lag [1] SAR: {} MA: {-0.1 0.6} at lags [1 2] SMA: {} Seasonality: 0 Beta: [3] Variance: 2

MdlX = arima(AR=0.3,Constant=0,Variance=1);

If you do not specify presample responses, `infer`

requires at least `T + MdlY.P`

predictor observations to simulate a response series of length `T`

.

Consider simulating a response series of length 100. Simulate a predictor series of length 101, and then simulate the response series. Provide the predictor data to `simulate`

for the exogenous regression component.

rng(1,"twister") % For reproducibility T = 100; Pred = simulate(MdlX,T + MdlY.P); Y = simulate(MdlY,T,X=Pred);

Infer residuals using the entire series.

```
E = infer(MdlY,Y,X=Pred);
figure
plot(E)
title("Inferred Residuals")
```

## Input Arguments

`Y`

— Response data *y*_{t}

numeric column vector | numeric matrix

_{t}

Response data *y _{t}*, specified as a

`numobs`

-by-1 numeric column vector or
`numobs`

-by-`numpaths`

numeric matrix.
`numObs`

is the length of the time series (sample
size). `numpaths`

is the number of separate, independent
paths of response series.`infer`

infers the residuals and conditional
variances of columns of `Y`

, which are time series
characterized by `Mdl`

. `Y`

is the
continuation of the presample series `Y0`

.

Each row corresponds to a sampling time. The last row contains the latest set of observations.

Each column corresponds to a separate, independent path of response data.
`infer`

assumes that responses across any row
occur simultaneously.

**Data Types: **`double`

`Tbl1`

— Time series data

table | timetable

*Since R2023b*

Time series data containing the observed response variable
*y _{t}* and, optionally,
predictor variables

*x*for the exogenous regression component, specified as a table or timetable with

_{t}`numvars`

variables and `numobs`

rows.
You can optionally select the response variable or
`numpreds`

predictor variables by using the
`ResponseVariable`

or
`PredictorVariables`

name-value arguments,
respectively.Each row is an observation, and measurements in each row occur
simultaneously. The selected response variable is a single path
(`numobs`

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

-by-`numpaths`

matrix) of
`numobs`

observations of response data.

Each path (column) of the selected response variable is independent of the
other paths, but path

of all
presample and in-sample variables correspond, for
`j`

=
1,…,`j`

`numpaths`

. Each selected predictor variable is a
`numobs`

-by-1 numeric vector representing one path. The
`infer`

function includes all predictor
variables in the model when it infers residuals and conditional variances.
Variables in `Tbl1`

represent the continuation of
corresponding variables in `Presample`

.

If `Tbl1`

is a timetable, it must represent a sample
with a regular datetime time step (see `isregular`

), and the datetime
vector `Tbl1.Time`

must be strictly ascending or
descending.

If `Tbl1`

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

### 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: **`infer(Mdl,Y,Y0=PS,X=Pred)`

infers residuals from the
numeric vector of responses `Y`

through the ARIMAX
`Mdl`

, and specifies the numeric vector of presample response
data `PS`

to initialize the model and the exogenous predictor data
`Pred`

for the regression component.

`ResponseVariable`

— Response variable *y*_{t} to select from `Tbl1`

string scalar | character vector | integer | logical vector

_{t}

*Since R2023b*

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

`Tbl1`

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

`Tbl1.Properties.VariableNames`

Variable index (positive integer) to select from

`Tbl1.Properties.VariableNames`

A logical vector, where

`DisturbanceVariable(`

selects variable) = true`j`

from`j`

`Tbl1.Properties.VariableNames`

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

s).

If `Tbl1`

has one variable, the default specifies that variable.
Otherwise, the default matches the variable to names in
`Mdl.SeriesName`

.

**Example: **`ResponseVariable="StockRate"`

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

or
`ResponseVariable=3`

selects the third table variable as the
response variable.

**Data Types: **`double`

| `logical`

| `char`

| `cell`

| `string`

`Y0`

— Presample response data *y*_{t}

numeric column vector | numeric matrix

_{t}

Presample response data *y _{t}*
to initialize the model, specified as a

`numpreobs`

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

-by-`numprepaths`

numeric matrix. Use `Y0`

only when you supply the
numeric array of response data `Y`

.`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`

,
`infer`

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.`infer`

applies it to each output path.If

`Y0`

is a matrix, each column represents a presample response path.`infer`

applies`Y0(:,`

to initialize path)`j`

.`j`

`numprepaths`

must be at least`numpaths`

. If`numprepaths`

>`numpaths`

,`infer`

uses the first`size(Y,2)`

columns only.

By default, `infer`

backcasts to obtain the
necessary observations.

**Data Types: **`double`

`E0`

— Presample residual data *e*_{t}

numeric column vector | numeric matrix

_{t}

Presample residual data *e _{t}*
to initialize the model, specified as a

`numpreobs`

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

-by-`numprepaths`

numeric matrix. Use `E0`

only when you supply the
numeric array of response data `Y`

.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),
`infer`

can require more rows than
`Mdl.Q`

. If `numpreobs`

is larger
than required, `infer`

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.`infer`

applies it to each output path.If

`E0`

is a matrix, each column represents a presample residual path.`infer`

applies`E0(:,`

to initialize path)`j`

.`j`

`numprepaths`

must be at least`numpaths`

. If`numprepaths`

>`numpaths`

,`infer`

uses the first`size(Y,2)`

columns only.`infer`

assumes each column of`E0`

has a mean of zero.

By default, `infer`

sets the necessary
presample disturbances to zero.

**Data Types: **`double`

`V0`

— Presample conditional variances *σ*_{t}^{2}

positive numeric column vector | positive numeric matrix

_{t}

Presample conditional variances
*σ _{t}*

^{2}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,
`infer`

ignores `V0`

. Use
`V0`

only when you supply the numeric array of
response data `Y`

.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 `infer`

function of
conditional variance models. If `numpreobs`

is larger
than required, `infer`

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.`infer`

applies it to each output path.If

`V0`

is a matrix, each column represents a presample path of conditional variances.`infer`

applies`V0(:,`

to initialize path)`j`

.`j`

`numprepaths`

must be at least`numpaths`

. If`numprepaths`

>`numpaths`

,`infer`

uses the first`size(Y,2)`

columns only.

By default, `infer`

sets all necessary
presample conditional variances to the average squared value of inferred
residuals.

**Data Types: **`double`

`Presample`

— Presample data

table | timetable

*Since R2023b*

Presample data containing paths of response
*y _{t}*, residual

*e*, or conditional variance

_{t}*σ*

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

`Tbl1`

, with
`numprevars`

variables and
`numpreobs`

rows. Use
`Presample`

only when you supply a table or
timetable of data `Tbl1`

.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 the response, residual, or conditional variance series for
`ResponseVariable`

, the selected response
variable in `Tbl1`

.

Each row is a presample observation, and measurements in each row
occur simultaneously. `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, `infer`

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

If you supply more rows than necessary,
`infer`

uses the latest required number of
observations only.

When `Presample`

provides presample residuals,
`infer`

assumes each presample residual
path has a mean of zero.

If `Presample`

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

`Presample`

must represent a sample with a regular datetime time step (see`isregular`

).The inputs

`Tbl1`

and`Presample`

must be consistent in time such that`Presample`

immediately precedes`Tbl1`

with respect to the sampling frequency and order.The datetime vector of sample timestamps

`Presample.Time`

must be ascending or descending.

If `Presample`

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

By default:

When

`Mdl`

is a model without a exogenous linear regression component (ARIMAX),`infer`

backcasts for necessary presample responses, sets necessary presample residuals to 0, and sets necessary presample variances to the average squared value of inferred residuals.When

`Mdl`

is an ARIMAX model (you specify the`PredictorVariables`

name-value argument), you must specify presample response data, but`infer`

sets necessary presample residuals to 0 and sets necessary presample variances to the average squared value of inferred residuals.

If you specify the `Presample`

, you must specify
the presample response, residual, or conditional variance 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`

`PresampleInnovationVariable`

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

string scalar | character vector | integer | logical vector

_{t}

*Since R2023b*

Presample residual variable
*e _{t}* to select from

`Presample`

containing 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 model regression component, specified
as a numeric matrix with `numpreds`

columns.
`numpreds`

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

). Use `X`

only
when you supply the numeric array of response data
`Y`

.

If you do not specify `Y0`

, the number of rows of
`X`

must be at least ```
numObs +
Mdl.P
```

. Otherwise, the number of rows of
`X`

must be at least `numObs`

. If
the number of rows of `X`

exceeds the number necessary,
`infer`

uses only the latest observations.
`infer`

does not use the regression
component in the presample period.

Columns of `X`

are separate predictor
variables.

`infer`

applies `X`

to each
path; that is, `X`

represents one path of observed
predictors.

By default, `infer`

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

.

**Data Types: **`double`

`PredictorVariables`

— Exogenous predictor variables *x*_{t} to select from
`Tbl1`

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

_{t}

*Since R2023b*

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

`Tbl1`

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

`numpreds`

variable names in`Tbl1.Properties.VariableNames`

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

`Tbl1.Properties.VariableNames`

A logical vector, where

`PredictorVariables(`

selects variable) = true`j`

from`j`

`Tbl1.Properties.VariableNames`

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

s).

If you specify `PredictorVariables`

, you must also
specify presample response data to by using the
`Presample`

and
`PresampleResponseVariable`

name-value arguments.
For more details, see Algorithms.

By default, `infer`

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`

**Note**

`NaN`

values in`Y`

,`X`

,`Y0`

,`E0`

, and`V0`

indicate missing values.`infer`

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

`infer`

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,

`infer`

horizontally concatenates the possibly jagged arrays`Y`

and`X`

, and then it removes any row of the concatenated matrix 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,

`infer`

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

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

## Output Arguments

`E`

— Inferred residual paths *e*_{t}

numeric matrix

_{t}

Inferred residual paths *e _{t}*,
returned as a

`numobs`

-by-`numpaths`

numeric matrix. `infer`

returns
`E`

only when you supply the input
`Y`

.`E(`

is the path * j*,

*)*

`k`

`k`

residual of time
*; it is the residual associated with response*

`j`

`Y(``j`

,`k`

)

.`V`

— Inferred conditional variance paths *σ*_{t}

numeric matrix

_{t}

Inferred conditional variance paths
*σ _{t}*, returned as a

`numobs`

-by-`numpaths`

numeric matrix.
`infer`

returns `V`

only
when you supply the input `Y`

.`V(`

is the path * j*,

*)*

`k`

`k`

conditional
variance of time *; it is the conditional variance associated with response*

`j`

`Y(``j`

,`k`

)

.`Tbl2`

— Inferred residual *e*_{t} and conditional variance
*σ*_{t}^{2}
paths

table | timetable

_{t}

_{t}

*Since R2023b*

Inferred residual *e _{t}* and
conditional variance

*σ*

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

`Tbl1`

. `infer`

returns
`Tbl2`

only when you supply the input
`Tbl1`

.`Tbl2`

contains the following variables:

The inferred residual paths, which are in a

`numobs`

-by-`numpaths`

numeric matrix, with rows representing observations and columns representing independent paths. Each path corresponds to the input response path in`Tbl1`

and represents the continuation of the corresponding presample residual path in`Presample`

.`infer`

names the inferred residual variable in`Tbl2`

, where_Residual`responseName`

is`responseName`

`Mdl.SeriesName`

. For example, if`Mdl.SeriesName`

is`StockReturns`

,`Tbl2`

contains a variable for the corresponding inferred innovations paths with the name`StockReturns_Residual`

.The inferred 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 path of presample conditional variances in`Presample`

.`infer`

names the inferred conditional variance variable in`Tbl2`

, where_Variance`responseName`

is`responseName`

`Mdl.SeriesName`

. For example, if`Mdl.SeriesName`

is`StockReturns`

,`Tbl2`

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

.All variables

`Tbl1`

.

If `Tbl1`

is a timetable, row times of
`Tbl1`

and `Tbl2`

are
equal.

`logL`

— Loglikelihood objective function values

numeric scalar | numeric vector

## Algorithms

If you supply data in the table or timetable `Tbl1`

to estimate an
ARIMAX model, `infer`

cannot backcast for presample responses.
Therefore, if you specify `PredictorVariables`

, you must also specify
presample response data by using the `Presample`

and
`PresampleResponseVariable`

name-value arguments.

## 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] Enders, W. *Applied Econometric Time Series*. Hoboken, NJ: John Wiley & Sons, 1995.

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

## Version History

**Introduced in R2012a**

### R2023b: `infer`

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

In addition to accepting input data (in-sample and presample) in numeric arrays,
`infer`

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

`infer`

chooses the default in-sample response series on which to operate, but you can use the specified optional name-value argument to select a different series.If you specify optional presample response, residual, or conditional variance data to initialize the model, you must also specify the appropriate presample variable names.

`infer`

returns results in a table or timetable.

Name-value arguments to support tabular workflows include:

`ResponseVariable`

specifies the variable name of the response paths in the input data, from which`infer`

infers conditional variances and innovations.`Presample`

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

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

.`PresampleInnovationVariable`

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

.`PresampleVarianceVariable`

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

.`PredictorVariables`

specifies the names of the predictor series to select from the input data for a model regression component.

## See Also

### Objects

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