# infer

Infer residuals of univariate regression model with ARIMA time series errors

## Syntax

## Description

returns the table or timetable `Tbl2`

= infer(`Mdl`

,`Tbl1`

)`Tbl2`

containing paths of residuals,
unconditional disturbances, innovation 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,
unconditional disturbances, and innovation 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,U0=u0,X=Pred)`

infers residuals
from the numeric vector of response data `Y`

with respect to the
regression model with ARIMA errors `Mdl`

, and specifies the numeric
vector of presample regression model residual data `u0`

to initialize the
model and the predictor data `Pred`

for the regression component.

## Examples

### Infer Vector of Residuals from Regression Model with ARIMA Errors

Infer error model residuals from a simulated path of responses from the following regression model with ARMA(2,1) errors:

$$\begin{array}{l}\begin{array}{c}{y}_{t}={X}_{t}\left[\begin{array}{c}0.1\\ -0.2\end{array}\right]+{u}_{t}\\ {u}_{t}=0.5{u}_{t-1}-0.8{u}_{t-2}+{\epsilon}_{t}-0.5{\epsilon}_{t-1},\end{array}\end{array}$$

where $${\epsilon}_{t}$$ is Gaussian with variance 0.1. Assume the predictors are standard Gaussian random variables. Provide data as numeric arrays.

Create the regression model with ARIMA errors. Simulate responses from the model and two predictor series.

Mdl = regARIMA(Intercept=0,AR={0.5 -0.8},MA=-0.5, ... Beta=[0.1; -0.2],Variance=0.1); rng(1,"twister"); % For reproducibility Pred = randn(100,2); y = simulate(Mdl,100,X=Pred);

Infer and plot the error model residuals. By default, `infer`

backcasts for the necessary presample unconditional disturbances and sets necessary presample error model residuals to zero.

```
e = infer(Mdl,y,X=Pred);
figure
plot(e)
title("Inferred Residuals")
```

`e`

is a 100-by-1 vector of error model residuals, associated with error model innovations ${\epsilon}_{\mathit{t}}$.

### Examine Residuals of Estimated Model in Timetable

*Since R2023b*

Fit a regression model with ARMA(1,1) errors by regressing the US gross domestic product (GDP) growth rate onto consumer price index (CPI) quarterly changes. Examine the error model and regression residuals. Supply a timetable of data and specify the series for the fit.

**Load and Transform Data**

Load the US macroeconomic data set. Compute the series of GDP quarterly growth rates and CPI quarterly changes.

load Data_USEconModel DTT = price2ret(DataTimeTable,DataVariables="GDP"); DTT.GDPRate = 100*DTT.GDP; DTT.CPIDel = diff(DataTimeTable.CPIAUCSL); T = height(DTT)

T = 248

figure tiledlayout(2,1) nexttile plot(DTT.Time,DTT.GDPRate) title("GDP Rate") ylabel("Percent Growth") nexttile plot(DTT.Time,DTT.CPIDel) title("Index")

The series appear stationary, albeit heteroscedastic.

**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.

DTT = rmmissing(DTT); T_DTT = height(DTT)

T_DTT = 248

Because each sample time has an observation for all variables, `rmmissing`

does not remove any observations.

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

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

`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. Macroeconomic series in this example are timestamped at the end of the month. This quality induces an irregularly measured series.

Remedy the time irregularity by shifting all dates to the first day of the quarter.

dt = DTT.Time; dt = dateshift(dt,"start","quarter"); DTT.Time = dt; areTimestampsRegular = isregular(DTT,"quarters")

`areTimestampsRegular = `*logical*
1

`DTT`

is regular.

**Create Model Template for Estimation**

Suppose that a regression model of CPI quarterly changes onto the GDP rate, with ARMA(1,1) errors, is appropriate.

Create a model template for a regression model with ARMA(1,1) errors template. Specify the response variable name.

```
Mdl = regARIMA(1,0,1);
Mdl.SeriesName = "GDPRate";
```

`Mdl`

is a partially specified `regARIMA`

object.

**Fit Model to Data**

Fit a regression model with ARMA(1,1) errors to the data. Specify the entire series GDP rate and CPI quarterly changes series, and specify the predictor variable name.

`EstMdl = estimate(Mdl,DTT,PredictorVariables="CPIDel");`

Regression with ARMA(1,1) Error Model (Gaussian Distribution): Value StandardError TStatistic PValue ________ _____________ __________ __________ Intercept 0.0162 0.0016077 10.077 6.9995e-24 AR{1} 0.60515 0.089912 6.7305 1.6906e-11 MA{1} -0.16221 0.11051 -1.4678 0.14216 Beta(1) 0.002221 0.00077691 2.8587 0.0042532 Variance 0.000113 7.2753e-06 15.533 2.0838e-54

`EstMdl`

is a fully specified, estimated `regARIMA`

object. By default, `estimate`

backcasts for the required `Mdl.P = 1`

presample regression model residual and sets the required `Mdl.Q = 1`

presample error model residual to 0.

**Examine Residuals**

Infer a timetable of error model and regression residuals for all observations. Specify the predictor variable name.

`Tbl2 = infer(EstMdl,DTT,PredictorVariables="CPIDel")`

`Tbl2=`*248×6 timetable*
Time Interval GDP GDPRate CPIDel GDPRate_ErrorResidual GDPRate_RegressionResidual
_____ ________ ___________ _________ ______ _____________________ __________________________
Q2-47 91 0.00015183 0.015183 0.08 -0.0007572 -0.0011947
Q3-47 92 0.00018374 0.018374 0.76 0.0010863 0.00048617
Q4-47 92 0.000427 0.0427 0.57 0.025116 0.025234
Q1-48 91 0.00025617 0.025617 0.09 -0.0019795 0.0092168
Q2-48 91 0.00028739 0.028739 0.65 0.005197 0.011096
Q3-48 92 0.00026512 0.026512 0.21 0.0039745 0.0098461
Q4-48 92 5.1468e-05 0.0051468 -0.31 -0.015678 -0.010365
Q1-49 90 -0.00021196 -0.021196 -0.14 -0.033356 -0.037085
Q2-49 91 -0.00015576 -0.015576 0.01 -0.014767 -0.031798
Q3-49 92 6.1077e-05 0.0061077 -0.17 0.0071327 -0.0097147
Q4-49 91 -0.00010311 -0.010311 -0.14 -0.019164 -0.0262
Q1-50 91 0.00040675 0.040675 0.03 0.037154 0.024408
Q2-50 91 0.00036908 0.036908 0.24 0.011432 0.020175
Q3-50 91 0.00065211 0.065211 0.46 0.037635 0.04799
Q4-50 91 0.00040718 0.040718 0.64 0.00016008 0.023097
Q1-51 91 0.00053382 0.053382 0.9 0.021232 0.035183
⋮

`Tbl2`

is a 248-by-6 timetable containing the error model residuals `GDPRate_ErrorResidual`

, regression residuals `GDPRate_RegressionResidual`

, and all variables in `DTT`

.

Separately plot the inferred error model and regression residuals.

Tbl2.GDPRate_Fitted = Tbl2.GDPRate - Tbl2.GDPRate_RegressionResidual; figure h = tiledlayout(2,2); title(h,"Error Model Residuals") nexttile plot(Tbl2.Time,Tbl2.GDPRate_ErrorResidual,'b',Tbl2.Time([1 end]),[0 0],'--r') title("Case Order") nexttile histogram(Tbl2.GDPRate_ErrorResidual) title("Histogram") nexttile plot(Tbl2.GDPRate_ErrorResidual(1:end-1),Tbl2.GDPRate_ErrorResidual(2:end),'o') title("e_{t-1} versus e_t") nexttile plot(Tbl2.GDPRate_Fitted,Tbl2.GDPRate_ErrorResidual,'o') title("Fitted versus e_t")

figure h = tiledlayout(2,2); title(h,"Regression Residuals") nexttile plot(Tbl2.Time,Tbl2.GDPRate_RegressionResidual,'b',Tbl2.Time([1 end]),[0 0],'--r') title("Case Order") nexttile histogram(Tbl2.GDPRate_RegressionResidual) title("Histogram") nexttile plot(Tbl2.GDPRate_RegressionResidual(1:end-1),Tbl2.GDPRate_RegressionResidual(2:end),'o') title("e_{t-1} versus e_t") nexttile plot(Tbl2.GDPRate_Fitted,Tbl2.GDPRate_RegressionResidual,'o') title("Fitted versus e_t")

### Compare Model Fits By Using Likelihood Ratio Test

Fit this regression model with ARMA(2,1) errors to simulated data:

$$\begin{array}{l}\begin{array}{c}{y}_{t}=1+{X}_{t}\left[\begin{array}{c}0.1\\ -0.2\end{array}\right]+{u}_{t}\\ {u}_{t}=0.5{u}_{t-1}-0.8{u}_{t-2}+{\epsilon}_{t}-0.5{\epsilon}_{t-1},\end{array}\end{array}$$

where $${\epsilon}_{t}$$ is Gaussian with variance 0.1. Compare the fit to an intercept-only regression model by conducting a likelihood ratio test. Provide response and predictor data in vectors.

**Simulate Data**

Specify the regression model ARMA(2,1) errors. Simulate responses from the model, and simulate two predictor series from the standard Gaussian distribution.

Mdl0 = regARIMA(Intercept=1,AR={0.5 -0.8},MA=-0.5, ... Beta=[0.1; -0.2],Variance=0.1); rng(1,"twister") % For reproducibility Pred = randn(100,2); y = simulate(Mdl0,100,X=Pred);

`y`

is a 100-by-1 random response path simulated from `Mdl`

.

**Fit Unrestricted Model**

Create an unrestricted model template of a regression model with ARMA(2,1) errors for estimation.

Mdl = regARIMA(2,0,1)

Mdl = regARIMA with properties: Description: "ARMA(2,1) Error Model (Gaussian Distribution)" SeriesName: "Y" Distribution: Name = "Gaussian" Intercept: NaN Beta: [1×0] P: 2 Q: 1 AR: {NaN NaN} at lags [1 2] SAR: {} MA: {NaN} at lag [1] SMA: {} Variance: NaN

The AR coefficients, MA coefficients, and the innovation variance are `NaN`

values. `estimate`

estimates those parameters. When `Beta`

is an empty array, `estimate`

determines the number of regression coefficients to estimate.

Fit the unrestricted model to the data. Specify the predictor data.

EstMdlUR = estimate(Mdl,y,X=Pred);

Regression with ARMA(2,1) Error Model (Gaussian Distribution): Value StandardError TStatistic PValue ________ _____________ __________ __________ Intercept 1.0167 0.010154 100.13 0 AR{1} 0.64995 0.093794 6.9295 4.2226e-12 AR{2} -0.69174 0.082575 -8.3771 5.4247e-17 MA{1} -0.64508 0.11055 -5.835 5.3796e-09 Beta(1) 0.10866 0.020965 5.183 2.1835e-07 Beta(2) -0.20979 0.022824 -9.1917 3.8679e-20 Variance 0.073117 0.008716 8.3888 4.9121e-17

`EstMdlUR`

is a fully specified `regARIMA`

object representing the estimated unrestricted regression model with ARIMA errors.

**Fit Restricted Model**

The restricted model contains the same error model, but the regression model contains only an intercept. That is, the restricted model imposes two restrictions on the unrestricted model: ${\beta}_{1}={\beta}_{2}=0$.

Fit the restricted model to the data.

EstMdlR = estimate(Mdl,y);

ARMA(2,1) Error Model (Gaussian Distribution): Value StandardError TStatistic PValue ________ _____________ __________ __________ Intercept 1.0176 0.024905 40.859 0 AR{1} 0.51541 0.18536 2.7805 0.0054271 AR{2} -0.53359 0.10949 -4.8735 1.0963e-06 MA{1} -0.34923 0.19423 -1.798 0.07218 Variance 0.1445 0.020214 7.1486 8.7671e-13

`EstMdlR`

is a fully specified `regARIMA`

object representing the estimated restricted regression model with ARIMA errors.

**Compute Residuals and Loglikelihoods**

Compute the residual series and loglikelihoods for the estimated models.

[eUR,uUR,~,logLUR] = infer(EstMdlUR,y,X=Pred); [eR,uR,~,logLR] = infer(EstMdlR,y);

`eUR`

and `uUR`

are 100-by-1 vectors containing the error model and regression residuals from the unrestricted estimation. `loglUR`

is the corresponding loglikelihood.

`eR`

and `uR`

are 100-by-1 vectors containing the error model and regression residuals from the restricted estimation. `loglR`

is the corresponding loglikelihood.

**Conduct Likelihood Ratio Test**

The likelihood ratio test requires the optimized loglikelihoods of the unrestricted and restricted models, and it requires the number of model restrictions (degrees of freedom).

Conduct a likelihood ratio test to determine which model has the better fit to the data.

dof = 2; [h,p] = lratiotest(logLUR,logLR,dof)

`h = `*logical*
1

p = 1.6653e-15

The $\mathit{p}$-value is close to zero, which suggests that there is strong evidence to reject the null hypothesis that the data fits the restricted model better than the unrestricted model.

## 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, unconditional disturbances,
and innovation variances of columns of `Y`

, which are time series
characterized by `Mdl`

.

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 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. 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,U0=u0,X=Pred)`

infers residuals from the numeric
vector of response data `Y`

with respect to the regression model with
ARIMA errors `Mdl`

, and specifies the numeric vector of presample
regression model residual data `u0`

to initialize the model and the
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`

`X`

— Predictor data

numeric matrix

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`

.

`X`

must have at least `numobs`

rows. If the
number of rows of `X`

exceeds `numobs`

,
`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`

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

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

_{t}

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).

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`

`E0`

— Presample error model residual data *e*_{t}

numeric column vector | numeric matrix

_{t}

Presample error model residual data *e _{t}*
to initialize the error 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 moving average (MA) component of the error model. 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`

`U0`

— Presample regression residual data

numeric column vector | numeric matrix

Presample regression residual data, associated with the unconditional disturbances
*u _{t}*, to initialize the error model,
specified as a

`numpreobs`

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

-by-`numprepaths`

numeric matrix. Use
`U0`

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.P`

to initialize
the error model autoregressive (AR) component. If `numpreobs`

is
larger than required, `infer`

uses the latest required
observations only.

Columns of `U0`

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

If

`U0`

is a column vector, it represents a single path.`infer`

applies it to each path.If

`U0`

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

applies`U0(:,`

to initialize path)`j`

.`j`

`numprepaths`

must be at least`numpaths`

. If`numprepaths`

>`numpaths`

,`infer`

uses the first`size(Z,2)`

columns only.

By default, `infer`

backcasts for necessary presample
unconditional disturbances.

**Data Types: **`double`

`Presample`

— Presample data

table | timetable

*Since R2023b*

Presample data containing paths of error model residual
*e _{t}* or regression residual series to
initialize the model, specified as a table or timetable, the same type as

`Tbl1`

, with `numprevars`

variables and
`numpreobs`

rows. Regression residuals are associated with the
unconditional disturbances *u*. Use

_{t}`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
error model or regression residual 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 regression residualsAt least

`Mdl.Q`

when`Presample`

provides only presample error model residualsAt least

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

otherwise

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 error model 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, `infer`

backcasts for necessary presample
regression residuals and sets necessary presample error model residuals to
zero.

If you specify the `Presample`

, you must specify the presample
error model or regression residual name by using the
`PresampleInnovationVariable`

or
`PresampleRegressionDisturbanceVariable`

name-value
argument.

`PresampleInnovationVariable`

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

string scalar | character vector | integer | logical vector

_{t}

*Since R2023b*

Error model residual variable *e _{t}* to
select from

`Presample`

containing the presample error model residual
data, specified as one of the following data types:String scalar or character vector containing the variable name to select from

`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 vector and cannot contain missing values
(`NaN`

s).

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

name-value argument, you must specify
`PresampleInnovationVariable`

.

**Example: **`PresampleInnovationVariable="GDP_Z"`

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

or
`PresampleInnovationVariable=3`

selects the third table variable
for presample error model residual data.

**Data Types: **`double`

| `logical`

| `char`

| `cell`

| `string`

`PresampleRegressionDistrubanceVariable`

— Regression model residual variable to select from `Presample`

string scalar | character vector | integer | logical vector

*Since R2023b*

Regression model residual variable, associated with unconditional disturbances
*u _{t}*, to select from

`Presample`

containing data for the presample regression model
residuals, 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

`PresampleRegressionDistrubanceVariable(`

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 regression model residual data by using the
`Presample`

name-value argument, you must specify
`PresampleRegressionDistrubanceVariable`

.

**Example: **`PresampleRegressionDistrubanceVariable="StockRateU"`

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

or `PresampleRegressionDistrubanceVariable=3`

selects the third table variable as the presample regression model residual
data.

**Data Types: **`double`

| `logical`

| `char`

| `cell`

| `string`

**Note**

`NaN`

values in`Y`

,`X`

,`E0`

and`U0`

indicate missing values.`infer`

removes missing values from specified data by listwise deletion.For the presample,

`infer`

horizontally concatenates the possibly jagged arrays`E0`

and`U0`

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.All predictor variables (columns) in

`X`

are associated with each input response series to produce`numpaths`

output series.

## Output Arguments

`E`

— Inferred error model residuals *e*_{t}

numeric matrix

_{t}

Inferred error model residuals *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`

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

`j`

`Y(``j`

,`k`

)

.Inferred residuals are

$${e}_{t}={\widehat{u}}_{t}-{\varphi}_{1}{\widehat{u}}_{t-1}-\mathrm{...}-{\varphi}_{P}{\widehat{u}}_{t-P}-{\theta}_{1}{e}_{t-1}-\mathrm{...}-{\theta}_{Q}{e}_{t-Q}$$

$${\widehat{u}}_{t}$$ is row *t* of the inferred unconditional disturbances
`U`

,
*ϕ*_{j} is composite
autoregressive coefficient *j*, and
*θ*_{k} is composite moving
average coefficient *k*.

`U`

— Inferred regression residuals

numeric matrix

Inferred regression residuals associated with the unconditional disturbances
*u _{t}*, returned as a

`numobs`

-by-`numpaths`

numeric matrix.
`infer`

returns `V`

only when you supply
the input `Y`

.`U(`

is
the path * j*,

*)*

`k`

`k`

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

`j`

`Y(``j`

,`k`

)

.Inferred unconditional disturbances are

$${\widehat{u}}_{t}={y}_{t}-c-{x}_{t}\beta .$$

*y*_{t} is row
*t* of the response data `Y`

,
*x*_{t} is row
*t* of the predictor data `X`

,
*c* is the model intercept `Mdl.Intercept`

, and
*β* is the vector of regression coefficients
`Mdl.Beta`

.

`V`

— Inferred innovation variances

numeric matrix

Inferred innovation variances, returned as a
`numobs`

-by-`numpaths`

numeric matrix.
`infer`

returns `V`

only when you supply
the input `Y`

. All elements in `V`

are equal to
`Mdl.Variance`

.

`Tbl2`

— Inferred error model residual *e*_{t} and regression residual paths

table | timetable

_{t}

*Since R2023b*

Inferred error model residual *e _{t}* and
regression residual paths, returned as a table or timetable, the same data type as

`Tbl1`

. `infer`

returns
`Tbl2`

only when you supply the input `Tbl1`

.
Regression residuals are associated with the unconditional disturbances
*u*.

_{t}`Tbl2`

contains the following variables:

The inferred error model 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 error model residual path in`Presample`

.`infer`

names the inferred residual variable in`Tbl2`

, where_ErrorResidual`responseName`

is`responseName`

`Mdl.SeriesName`

. For example, if`Mdl.SeriesName`

is`StockReturns`

,`Tbl2`

contains a variable for the corresponding inferred error model residual paths with the name`StockReturns_ErrorResidual`

.The inferred regression residual 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 regression residuals in`Presample`

.`infer`

names the inferred regression residual variable in`Tbl2`

, where_RegressionResidual`responseName`

is`responseName`

`Mdl.SeriesName`

. For example, if`Mdl.SeriesName`

is`StockReturns`

,`Tbl2`

contains a variable for the corresponding inferred regression residual paths with the name`StockReturns_RegressionResidual`

.All variables

`Tbl1`

.

If `Tbl1`

is a timetable, row times of `Tbl1`

and `Tbl2`

are equal.

`Tbl2`

does not include a variable containing inferred paths of
innovation variances. To create such a variable, enter
`Tbl2.`

.* responseName*_Variance =
Mdl.Variance*ones(size(Tbl2));

`logL`

— Loglikelihood objective function values

numeric scalar | numeric vector

## 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] Davidson, R., and J. G. MacKinnon. *Econometric Theory and Methods*. Oxford, UK: Oxford University Press, 2004.

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

[4] Hamilton, James 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.

## Version History

**Introduced in R2013b**

### R2023b: `infer`

accepts input data in tables and timetables

In addition to accepting input data (in-sample and presample data) 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 error model residual or regression model residual 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 name of the response series to select from the input data, from which residuals are inferred.`PredictorVariables`

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

specifies the input table or timetable of presample regression residual or error model residual data.`PresampleInnovationVariable`

specifies the name of the error model residual series to select from`Presample`

.`PresampleRegressionDisturbanceVariable`

specifies the name of the regression residual series to select from`Presample`

.

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