Main Content

Fit autoregressive integrated moving average (ARIMA) model to data

uses additional
options specified by one or more name-value pair arguments. For example, `EstMdl`

= estimate(`Mdl`

,`y`

,`Name,Value`

)`'X',X`

includes a linear regression component in the model for the exogenous data in `X`

.

`[`

also returns the variance-covariance matrix associated with the estimated parameters `EstMdl`

,`EstParamCov`

,`logL`

,`info`

] = estimate(___)`EstParamCov`

, optimized loglikelihood objective function value `logL`

, and summary information `info`

, using any of the input argument combinations in the previous syntaxes.

Fit an ARMA(2,1) model to simulated data.

**Simulate Data from Known Model**

Suppose that the data generating process (DGP) is

$${y}_{t}=0.5{y}_{t-1}-0.3{y}_{t-2}+{\epsilon}_{t}+0.2{\epsilon}_{t-1},$$

where $${\epsilon}_{t}$$ is a series of iid Gaussian random variables with mean 0 and variance 0.1.

Create the ARMA(2,1) model representing the DGP.

DGP = arima('AR',{0.5,-0.3},'MA',0.2,... 'Constant',0,'Variance',0.1)

DGP = arima with properties: Description: "ARIMA(2,0,1) Model (Gaussian Distribution)" Distribution: Name = "Gaussian" P: 2 D: 0 Q: 1 Constant: 0 AR: {0.5 -0.3} at lags [1 2] SAR: {} MA: {0.2} at lag [1] SMA: {} Seasonality: 0 Beta: [1×0] Variance: 0.1

`DGP`

is a fully specified `arima`

model object.

Simulate a random 500 observation path from the ARMA(2,1) model.

```
rng(5); % For reproducibility
T = 500;
y = simulate(DGP,T);
```

y is a 500-by-1 column vector representing a simulated response path from the ARMA(2,1) model `DGP`

.

**Estimate Model**

Create an ARMA(2,1) model template for estimation.

Mdl = arima(2,0,1)

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

`Mdl`

is a partially specified `arima`

model object. Only required, nonestimable parameters that determine the model structure are specified. `NaN`

-valued properties, including ${\varphi}_{1}$, ${\varphi}_{2}$, ${\theta}_{1}$, $\mathit{c}$, and ${\sigma}^{2}$, are unknown model parameters to be estimated.

Fit the ARMA(2,1) model to `y`

.

EstMdl = estimate(Mdl,y)

ARIMA(2,0,1) Model (Gaussian Distribution): Value StandardError TStatistic PValue _________ _____________ __________ __________ Constant 0.0089018 0.018417 0.48334 0.62886 AR{1} 0.49563 0.10323 4.8013 1.5767e-06 AR{2} -0.25495 0.070155 -3.6341 0.00027897 MA{1} 0.27737 0.10732 2.5846 0.0097492 Variance 0.10004 0.0066577 15.027 4.9017e-51

EstMdl = arima with properties: Description: "ARIMA(2,0,1) Model (Gaussian Distribution)" Distribution: Name = "Gaussian" P: 2 D: 0 Q: 1 Constant: 0.00890178 AR: {0.495632 -0.254951} at lags [1 2] SAR: {} MA: {0.27737} at lag [1] SMA: {} Seasonality: 0 Beta: [1×0] Variance: 0.100043

`MATLAB®`

displays a table containing an estimation summary, which includes parameter estimates and inferences. For example, the `Value`

column contains corresponding maximum-likelihood estimates, and the `PValue`

column contains $\mathit{p}$-values for the asymptotic $\mathit{t}$-test of the null hypothesis that the corresponding parameter is 0.

`EstMdl`

is a fully specified, estimated `arima`

model object; its estimates resemble the parameter values of the DGP.

Fit an AR(2) model to simulated data while holding the model constant fixed during estimation.

**Simulate Data from Known Model**

Suppose the DGP is

$${y}_{t}=0.5{y}_{t-1}-0.3{y}_{t-2}+{\epsilon}_{t},$$

where $${\epsilon}_{t}$$ is a series of iid Gaussian random variables with mean 0 and variance 0.1.

Create the AR(2) model representing the DGP.

DGP = arima('AR',{0.5,-0.3},... 'Constant',0,'Variance',0.1);

Simulate a random 500 observation path from the model.

```
rng(5); % For reproducibility
T = 500;
y = simulate(DGP,T);
```

**Create Model Object Specifying Constraint**

Assume that the mean of ${\mathit{y}}_{\mathit{t}}$ is 0, which implies that $\mathit{c}$ is 0.

Create an AR(2) model for estimation. Set $\mathit{c}$ to 0.

Mdl = arima('ARLags',1:2,'Constant',0)

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

`Mdl`

is a partially specified `arima`

model object. Specified parameters include all required parameters and the model constant. `NaN`

-valued properties, including ${\varphi}_{1}$, ${\varphi}_{2}$, and ${\sigma}^{2}$, are unknown model parameters to be estimated.

**Estimate Model**

Fit the AR(2) model template containing the constraint to `y`

.

EstMdl = estimate(Mdl,y)

ARIMA(2,0,0) Model (Gaussian Distribution): Value StandardError TStatistic PValue ________ _____________ __________ __________ Constant 0 0 NaN NaN AR{1} 0.56342 0.044225 12.74 3.5474e-37 AR{2} -0.29355 0.041786 -7.0252 2.137e-12 Variance 0.10022 0.006644 15.085 2.0476e-51

EstMdl = arima with properties: Description: "ARIMA(2,0,0) Model (Gaussian Distribution)" Distribution: Name = "Gaussian" P: 2 D: 0 Q: 0 Constant: 0 AR: {0.563425 -0.293554} at lags [1 2] SAR: {} MA: {} SMA: {} Seasonality: 0 Beta: [1×0] Variance: 0.100222

`EstMdl`

is a fully specified, estimated `arima`

model object; its estimates resemble the parameter values of the AR(2) model `DGP`

. The value of $\mathit{c}$ in the estimation summary and object display is `0`

, and corresponding inferences are trivial or do not apply.

Because an ARIMA model is a function of previous values, `estimate`

requires presample data to initialize the model early in the sampling period. Although, `estimate`

backcasts for presample data by default, you can specify required presample data instead. The `P`

property of an `arima`

model object specifies the required number of presample observations.

**Load Data**

Load the US equity index data set `Data_EquityIdx`

.

`load Data_EquityIdx`

The table `DataTable`

includes the time series variable `NYSE`

, which contains daily NYSE composite closing prices from January 1990 through December 1995.

Convert the table to a timetable.

dt = datetime(dates,'ConvertFrom','datenum','Format','yyyy-MM-dd'); TT = table2timetable(DataTable,'RowTimes',dt); T = size(TT,1); % Total sample size

**Create Model Template**

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

`Mdl`

is a partially specified `arima`

model object.

**Partition Sample**

Create vectors of indices that partition the sample into presample and estimation sample periods, so that the presample occurs first and contains `Mdl.P`

= `2`

observations, and the estimation sample contains the remaining observations.

presample = 1:Mdl.P; estsample = (Mdl.P + 1):T;

**Estimate Model**

Fit an ARIMA(1,1,1) model to the estimation sample. Specify the presample responses.

EstMdl = estimate(Mdl,TT{estsample,"NYSE"},'Y0',TT{presample,"NYSE"});

ARIMA(1,1,1) Model (Gaussian Distribution): Value StandardError TStatistic PValue ________ _____________ __________ ________ Constant 0.15775 0.097888 1.6115 0.10707 AR{1} -0.21984 0.15652 -1.4045 0.16016 MA{1} 0.28529 0.15393 1.8534 0.063825 Variance 17.17 0.20065 85.573 0

`EstMdl`

is a fully specified, estimated `arima`

model object.

Fit an ARIMA(1,1,1) model to the daily close of the NYSE Composite Index. Specify initial parameter values obtained from an analysis of a pilot sample.

**Load Data**

Load the US equity index data set `Data_EquityIdx`

.

`load Data_EquityIdx`

The table `DataTable`

includes the time series variable `NYSE`

, which contains daily NYSE composite closing prices from January 1990 through December 1995.

Convert the table to a timetable.

dt = datetime(dates,'ConvertFrom','datenum','Format','yyyy-MM-dd'); TT = table2timetable(DataTable,'RowTimes',dt);

**Fit Model to Pilot Sample**

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.

Treat the first two years as a pilot sample for obtaining initial parameter values when fitting the model to the remaining three years of data. Fit the model to the pilot sample.

endPilot = datetime(1991,12,31); pilottr = timerange(TT.Time(1),endPilot,'days'); EstMdl0 = estimate(Mdl,TT{pilottr,"NYSE"},'Display','off');

`EstMdl0`

is a fully specified, estimated `arima`

model object.

**Estimate Model**

Fit an ARIMA(1,1,1) model to the estimation sample. Specify the estimated parameters from the pilot sample fit as initial values for optimization.

esttr = timerange(endPilot + days(1),TT.Time(end),'days'); c0 = EstMdl0.Constant; ar0 = EstMdl0.AR; ma0 = EstMdl0.MA; var0 = EstMdl0.Variance; EstMdl = estimate(Mdl,TT{esttr,"NYSE"},'Constant0',c0,'AR0',ar0,... 'MA0',ma0,'Variance0',var0);

ARIMA(1,1,1) Model (Gaussian Distribution): Value StandardError TStatistic PValue _______ _____________ __________ _______ Constant 0.17424 0.11648 1.4959 0.13468 AR{1} -0.2262 0.18587 -1.217 0.22362 MA{1} 0.29047 0.18276 1.5893 0.11199 Variance 20.053 0.27603 72.65 0

`EstMdl`

is a fully specified, estimated `arima`

model object.

Fit an ARIMAX model to simulated time series data.

**Simulate Predictor and Response Data**

Create the ARIMAX(2,1,0) model for the DGP, represented by ${\mathit{y}}_{\mathit{t}}$ in the equation

$$(1-0.5L+0.3{L}^{2})(1-L{)}^{1}{y}_{t}=2+1.5{x}_{1,t}+2.6{x}_{2,t}-0.3{x}_{3,t}+{\epsilon}_{t},$$

where $${\epsilon}_{t}$$ is a series of iid Gaussian random variables with mean 0 and variance 0.1.

DGP = arima('AR',{0.5,-0.3},'D',1,'Constant',2,... 'Variance',0.1,'Beta',[1.5 2.6 -0.3]);

Assume that the exogenous variables ${\mathit{x}}_{1,\mathit{t}}$, ${\mathit{x}}_{2,\mathit{t}}$, and ${\mathit{x}}_{3,\mathit{t}}$ are represented by the AR(1) processes

$$\begin{array}{c}{x}_{1,t}=0.1{x}_{1,t-1}+{\eta}_{1,t}\\ {x}_{2,t}=0.2{x}_{2,t-1}+{\eta}_{2,t}\\ {x}_{3,t}=0.3{x}_{3,t-1}+{\eta}_{3,t},\end{array}$$

where $${\eta}_{i,t}$$ follows a Gaussian distribution with mean 0 and variance 0.01 for $\mathit{i}\in \left\{1,2,3\right\}$. Create ARIMA models that represent the exogenous variables.

MdlX1 = arima('AR',0.1,'Constant',0,'Variance',0.01); MdlX2 = arima('AR',0.2,'Constant',0,'Variance',0.01); MdlX3 = arima('AR',0.3,'Constant',0,'Variance',0.01);

Simulate length 1000 exogenous series from the AR models. Store the simulated data in a matrix.

```
T = 1000;
rng(10); % For reproducibility
x1 = simulate(MdlX1,T);
x2 = simulate(MdlX2,T);
x3 = simulate(MdlX3,T);
X = [x1 x2 x3];
```

`X`

is a 1000-by-3 matrix of simulated time series data. Each row corresponds to an observation in the time series, and each column corresponds to an exogenous variable.

Simulate a length 1000 series from the DGP. Specify the simulated exogenous data.

`y = simulate(DGP,T,'X',X);`

`y`

is a 1000-by-1 vector of response data.

**Estimate Model**

Create an ARIMA(2,1,0) model template for estimation.

Mdl = arima(2,1,0)

Mdl = arima with properties: Description: "ARIMA(2,1,0) Model (Gaussian Distribution)" Distribution: Name = "Gaussian" P: 3 D: 1 Q: 0 Constant: NaN AR: {NaN NaN} at lags [1 2] SAR: {} MA: {} SMA: {} Seasonality: 0 Beta: [1×0] Variance: NaN

The model description (`Description`

property) and value of `Beta`

suggest that the partially specified `arima`

model object `Mdl`

is agnostic of the exogenous predictors.

Estimate the ARIMAX(2,1,0) model; specify the exogenous predictor data. Because `estimate`

backcasts for presample responses (a process that requires presample predictor data for ARIMAX models), fit the model to the latest `T – Mdl.P`

responses. (Alternatively, you can specify presample responses by using the `'Y0'`

name-value pair argument.)

`EstMdl = estimate(Mdl,y((Mdl.P + 1):T),'X',X);`

ARIMAX(2,1,0) Model (Gaussian Distribution): Value StandardError TStatistic PValue ________ _____________ __________ ___________ Constant 1.7519 0.021143 82.859 0 AR{1} 0.56076 0.016511 33.963 7.9439e-253 AR{2} -0.26625 0.015966 -16.676 1.9633e-62 Beta(1) 1.4764 0.10157 14.536 7.1231e-48 Beta(2) 2.5638 0.10445 24.547 4.6651e-133 Beta(3) -0.34422 0.098623 -3.4903 0.00048249 Variance 0.10673 0.0047273 22.577 7.3169e-113

`EstMdl`

is a fully specified, estimated `arima`

model object.

When you estimate the model by using `estimate`

and supply the exogenous data by specifying the `'X'`

name-value pair argument, MATLAB® recognizes the model as an ARIMAX(2,1,0) model and includes a linear regression component for the exogenous variables.

The estimated model is

$$\left(1-0.56\mathit{L}+0.27{\mathit{L}}^{2}\right){\left(1-\mathit{L}\right)}^{1}{\mathit{y}}_{\mathit{t}}=1.75+{1.48x}_{1,\mathit{t}}+2.56{x}_{2,\mathit{t}}-0.34{x}_{3,\mathit{t}}+{\epsilon}_{\mathit{t}},$$

which resembles the DGP represented by `Mdl0`

. Because MATLAB returns the AR coefficients of the model expressed in difference-equation notation, their signs are opposite in the equation.

Load the US equity index data set `Data_EquityIdx`

.

`load Data_EquityIdx`

The table `DataTable`

includes the time series variable `NYSE`

, which contains daily NYSE composite closing prices from January 1990 through December 1995.

Convert the table to a timetable.

dt = datetime(dates,'ConvertFrom','datenum','Format','yyyy-MM-dd'); TT = table2timetable(DataTable,'RowTimes',dt);

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

Fit an ARIMA(1,1,1) model to the data, and return the estimated parameter covariance matrix.

```
Mdl = arima(1,1,1);
[EstMdl,EstParamCov] = estimate(Mdl,TT{:,"NYSE"});
```

ARIMA(1,1,1) Model (Gaussian Distribution): Value StandardError TStatistic PValue ________ _____________ __________ ________ Constant 0.15745 0.097831 1.6094 0.10753 AR{1} -0.21996 0.15642 -1.4062 0.15965 MA{1} 0.2854 0.15382 1.8554 0.063533 Variance 17.159 0.20038 85.632 0

EstParamCov

`EstParamCov = `*4×4*
0.0096 -0.0002 0.0002 0.0023
-0.0002 0.0245 -0.0240 -0.0060
0.0002 -0.0240 0.0237 0.0057
0.0023 -0.0060 0.0057 0.0402

`EstMdl`

is a fully specified, estimated `arima`

model object. Rows and columns of `EstParamCov`

correspond to the rows in the table of estimates and inferences; for example, $$\underset{}{\overset{\u02c6}{Cov}}({\underset{}{\overset{\u02c6}{\varphi}}}_{1},{\underset{}{\overset{\u02c6}{\theta}}}_{1})=-0.024$$.

Compute estimated parameter standard errors by taking the square root of the diagonal elements of the covariance matrix.

estParamSE = sqrt(diag(EstParamCov))

`estParamSE = `*4×1*
0.0978
0.1564
0.1538
0.2004

Compute a Wald-based 95% confidence interval on $\varphi $.

```
T = size(TT,1); % Effective sample size
phihat = EstMdl.AR{1};
sephihat = estParamSE(2);
ciphi = phihat + tinv([0.025 0.975],T - 3)*sephihat
```

`ciphi = `*1×2*
-0.5267 0.0867

The interval contains 0, which suggests that $\varphi $ is insignificant.

Load the US equity index data set `Data_EquityIdx`

.

`load Data_EquityIdx`

`DataTable`

includes the time series variable `NYSE`

, which contains daily NYSE composite closing prices from January 1990 through December 1995.

Convert the table to a timetable.

dt = datetime(dates,'ConvertFrom','datenum','Format','yyyy-MM-dd'); TT = table2timetable(DataTable,'RowTimes',dt); T = size(TT,1);

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

Fit an ARIMA(1,1,1) model to the data. Specify the required presample and turn off the estimation display.

Mdl = arima(1,1,1); preidx = 1:Mdl.P; estidx = (Mdl.P + 1):T; EstMdl = estimate(Mdl,TT{estidx,"NYSE"},... 'Y0',TT{preidx,"NYSE"},'Display','off');

Infer residuals $\stackrel{\u02c6}{{\epsilon}_{\mathit{t}}}$ from the estimated model, specify the required presample.

resid = infer(EstMdl,TT{estidx,"NYSE"},... 'Y0',TT{preidx,"NYSE"});

`resid`

is a (`T – Mdl.P`

)-by-1 vector of residuals.

Compute the fitted values $\stackrel{\u02c6}{\text{\hspace{0.17em}}{\mathit{y}}_{\mathit{t}}}$.

`yhat = TT{estidx,"NYSE"} - resid;`

Plot the observations and the fitted values on the same graph.

plot(TT.Time(estidx),TT{estidx,"NYSE"},'r',TT.Time(estidx),yhat,'b--','LineWidth',2)

The fitted values closely track the observations.

Plot the residuals versus the fitted values.

plot(yhat,resid,'.') ylabel('Residuals') xlabel('Fitted values')

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

`Mdl`

— Partially specified ARIMA model`arima`

model objectPartially specified ARIMA model used to indicate constrained and estimable model parameters, specified as an `arima`

model object returned by `arima`

or `estimate`

. Properties of `Mdl`

describe the model structure and specify the parameters.

`estimate`

fits unspecified (`NaN`

-valued) parameters to the data `y`

.

`estimate`

treats specified parameters as equality constraints during estimation.

`y`

— Single path of response datanumeric column vector

Single path of response data to which the model `Mdl`

is fit, specified as a numeric column vector. The last observation of `y`

is the latest observation.

**Data Types: **`double`

Specify optional
comma-separated pairs of `Name,Value`

arguments. `Name`

is
the argument name and `Value`

is the corresponding value.
`Name`

must appear inside quotes. You can specify several name and value
pair arguments in any order as
`Name1,Value1,...,NameN,ValueN`

.

`'Y0',Y0,'X',X`

uses the vector `Y0`

as presample responses required for estimation, and includes a linear regression component for the exogenous predictor data in `X`

.`'X'`

— Exogenous predictor datamatrix

Exogenous predictor data for the linear regression component, specified as the comma-separated pair consisting of `'X'`

and a matrix.

The columns of `X`

are separate, synchronized time series. The last row contains the latest observations.

If you do not specify presample response data using the `'Y0'`

name-value pair argument, the number of rows of `X`

must be at least `numel(y) + Mdl.P`

. Otherwise, the number of rows of `X`

must be at least the length of `y`

.

If the number of rows of `X`

exceeds the number needed, `estimate`

uses the latest observations only.

`estimate`

synchronizes `X`

and `y`

so that the latest observations (last rows) occur simultaneously.

By default, `estimate`

does not estimate the regression coefficients, regardless of their presence in `Mdl`

.

**Data Types: **`double`

`'Options'`

— Optimization options`optimoptions`

optimization controllerOptimization options, specified as the comma-separated pair consisting of `'Options'`

and an `optimoptions`

optimization controller. For details on modifying the default values of the optimizer, see `optimoptions`

or `fmincon`

in Optimization Toolbox™.

For example, to change the constraint tolerance to `1e-6`

, set `Options = optimoptions(@fmincon,'ConstraintTolerance',1e-6,'Algorithm','sqp')`

. Then, pass `Options`

into `estimate`

using `'Options',Options`

.

By default, `estimate`

uses the same default options as `fmincon`

, except `Algorithm`

is `'sqp'`

and `ConstraintTolerance`

is `1e-7`

.

`'Display'`

— Command Window display option`'params'`

(default) | `'diagnostics'`

| `'full'`

| `'iter'`

| `'off'`

| string vector | cell vector of character vectorsCommand Window display option, specified as the comma-separated pair consisting of `'Display'`

and one or more of the values in this table.

Value | Information Displayed |
---|---|

`'diagnostics'` | Optimization diagnostics |

`'full'` | Maximum likelihood parameter estimates, standard errors, t statistics, iterative optimization information, and optimization diagnostics |

`'iter'` | Iterative optimization information |

`'off'` | None |

`'params'` | Maximum likelihood parameter estimates, standard errors, and t statistics |

**Example: **`'Display','off'`

is well suited for running a simulation that estimates many models.

**Example: **`'Display',{'params','diagnostics'}`

displays all estimation results and the optimization diagnostics.

**Data Types: **`char`

| `cell`

| `string`

`'Y0'`

— Presample response datanumeric column vector

Presample response data for initializing the model, specified as the comma-separated pair consisting of `'Y0'`

and a numeric column vector.

The length of `Y0`

must be at least `Mdl.P`

. If `Y0`

has extra rows, `estimate`

uses only the latest `Mdl.P`

presample responses. The last row contains the latest presample responses.

By default, `estimate`

backward forecasts (backcasts) for the necessary amount of presample responses.

For details on partitioning data for estimation, see Time Base Partitions for ARIMA Model Estimation.

**Data Types: **`double`

`'E0'`

— Presample innovationsnumeric column vector

Presample innovations *ε*_{t} for initializing the model, specified as the comma-separated pair consisting of `'E0'`

and a numeric column vector.

The length of `E0`

must be at least `Mdl.Q`

. If `E0`

has extra rows, `estimate`

uses only the latest `Mdl.Q`

presample innovations. The last row contains the latest presample innovation.

If `Mdl.Variance`

is a conditional variance model object, such as a `garch`

model, `estimate`

can require more than `Mdl.Q`

presample innovations.

By default, `estimate`

sets all required presample innovations to `0`

, which is their mean.

**Data Types: **`double`

`'V0'`

— Presample conditional variancesnumeric positive column vector

Presample conditional variances *σ*^{2}_{t} for initializing any conditional variance model, specified as the comma-separated pair consisting of `'V0'`

and a numeric positive column vector.

The length of `V0`

must be at least the number of observations required to initialize the conditional variance model (see `estimate`

). If `V0`

has extra rows, `estimate`

uses only the latest observations. The last row contains the latest observation.

If the variance is constant, `estimate`

ignores `V0`

.

By default, `estimate`

sets the necessary presample conditional variances to the average of the squared inferred innovations.

**Data Types: **`double`

`'Constant0'`

— Initial estimate of model constantnumeric scalar

Initial estimate of the model constant *c*, specified as the comma-separated pair consisting of `'Constant0'`

and a numeric scalar.

By default, `estimate`

derives initial estimates using standard time series techniques.

**Data Types: **`double`

`'AR0'`

— Initial estimates of nonseasonal AR polynomial coefficientsnumeric vector

Initial estimates of the nonseasonal AR polynomial coefficients $$\varphi (L)$$, specified as the comma-separated pair consisting of `'AR0'`

and a numeric vector.

The length of `AR0`

must equal the number of lags associated with nonzero coefficients in the nonseasonal AR polynomial. Elements of `AR0`

correspond to elements of `Mdl.AR`

.

By default, `estimate`

derives initial estimates using standard time series techniques.

**Data Types: **`double`

`'SAR0'`

— Initial estimates of seasonal autoregressive polynomial coefficientsnumeric vector

Initial estimates of the seasonal autoregressive polynomial coefficients $$\Phi (L)$$, specified as the comma-separated pair consisting of `'SAR0'`

and a numeric vector.

The length of `SAR0`

must equal the number of lags associated with nonzero coefficients in the seasonal autoregressive polynomial `SARLags`

. Elements of `SAR0`

correspond to elements of `Mdl.SAR`

.

By default, `estimate`

derives initial estimates using standard time series techniques.

**Data Types: **`double`

`'MA0'`

— Initial estimates of nonseasonal moving average polynomial coefficientsnumeric vector

Initial estimates of the nonseasonal moving average polynomial coefficients $$\theta (L)$$, specified as the comma-separated pair consisting of `'MA0'`

and a numeric vector.

The length of `MA0`

must equal the number of lags associated with nonzero coefficients in the nonseasonal moving average polynomial `MALags`

. Elements of `MA0`

correspond to elements of `Mdl.MA`

.

By default, `estimate`

derives initial estimates using standard time series techniques.

**Data Types: **`double`

`'SMA0'`

— Initial estimates of seasonal moving average polynomial coefficientsnumeric vector

Initial estimates of the seasonal moving average polynomial coefficients $$\Theta (L)$$, specified as the comma-separated pair consisting of `'SMA0'`

and a numeric vector.

The length of `SMA0`

must equal the number of lags associated with nonzero coefficients in the seasonal moving average polynomial `SMALags`

. Elements of `SMA0`

correspond to elements of `Mdl.SMA`

.

By default, `estimate`

derives initial estimates using standard time series techniques.

**Data Types: **`double`

`'Beta0'`

— Initial estimates of regression coefficientsnumeric vector

Initial estimates of the regression coefficients *β*, specified as the comma-separated pair consisting of `'Beta0'`

and a numeric vector.

The length of `Beta0`

must equal the number of columns of `X`

. Elements of `Beta0`

correspond to the predictor variables represented by the columns of `X`

.

By default, `estimate`

derives initial estimates using standard time series techniques.

**Data Types: **`double`

`'DoF0'`

— Initial estimate of `10`

(default) | positive scalarInitial estimate of the *t*-distribution degrees-of-freedom parameter *ν*, specified as the comma-separated pair consisting of `'DoF0'`

and a positive scalar. `DoF0`

must exceed 2.

**Data Types: **`double`

`'Variance0'`

— Initial estimates of variances of innovationspositive scalar | cell vector of name-value pair arguments

Initial estimates of variances of innovations, specified as the comma-separated pair consisting of `'Variance0'`

and a positive scalar or a cell vector of name-value pair arguments.

`Mdl.Variance` Value | Description | `'Variance0'` Value |
---|---|---|

Numeric scalar or `NaN` | Constant variance | Positive scalar |

`garch` , `egarch` , or `gjr` model object | Conditional variance model | Cell vector of name-value pair arguments for specifying initial estimates, see the `estimate` function of the conditional variance model objects |

By default, `estimate`

derives initial estimates using standard time series techniques.

**Example: **For a model with a constant variance, set `'Variance0',2`

to specify an initial variance estimate of `2`

.

**Example: **For a composite conditional mean and variance model, set `'Variance0',{'Constant0',2,'ARCH0',0.1}`

to specify an initial estimate of `2`

for the conditional variance model constant, and an initial estimate of `0.1`

for the lag 1 coefficient in the ARCH polynomial.

**Data Types: **`double`

| `cell`

**Note**

`NaN`

s in input data indicate missing values. `estimate`

uses *listwise deletion* to delete all sampled times (rows) in the input data containing at least one missing value. Specifically, `estimate`

performs these steps:

Synchronize, or merge, the presample data sets

`E0`

,`V0`

, and`Y0`

and the effective sample data`X`

and`y`

to create the separate sets`Presample`

and`EffectiveSample`

.Remove all rows from

`Presample`

and`EffectiveSample`

containing at least one`NaN`

.

Listwise deletion reduces the sample size and can create irregular time series.

`EstParamCov`

— Estimated covariance matrix of maximum likelihood estimatespositive semidefinite numeric matrix

Estimated covariance matrix of maximum likelihood estimates known to the optimizer, returned as a positive semidefinite numeric matrix.

The rows and columns contain the covariances of the parameter estimates. The standard error of each parameter estimate is the square root of the main diagonal entries.

The rows and columns corresponding to any parameters held fixed as equality constraints are zero vectors.

Parameters corresponding to the rows and columns of `EstParamCov`

appear in the following order:

Constant

Nonzero

`AR`

coefficients at positive lags, from the smallest to largest lagNonzero

`SAR`

coefficients at positive lags, from the smallest to largest lagNonzero

`MA`

coefficients at positive lags, from the smallest to largest lagNonzero

`SMA`

coefficients at positive lags, from the smallest to largest lagRegression coefficients (when you specify exogenous data

`X`

), ordered by the columns of`X`

Variance parameters, a scalar for constant variance models and vector for conditional variance models (see

`estimate`

for the order of parameters)Degrees of freedom (

*t*-innovation distribution only)

**Data Types: **`double`

`logL`

— Optimized loglikelihood objective function valuenumeric scalar

Optimized loglikelihood objective function value, returned as a numeric scalar.

**Data Types: **`double`

`info`

— Optimization summarystructure array

Optimization summary, returned as a structure array with the fields described in this table.

Field | Description |
---|---|

`exitflag` | Optimization exit flag (see `fmincon` in Optimization Toolbox) |

`options` | Optimization options controller (see `optimoptions` and `fmincon` in Optimization Toolbox) |

`X` | Vector of final parameter estimates |

`X0` | Vector of initial parameter estimates |

For example, you can display the vector of final estimates by entering `info.X`

in the Command Window.

**Data Types: **`struct`

`estimate`

infers innovations and conditional variances (when present) of the underlying response series, and then uses constrained maximum likelihood to fit the model`Mdl`

to the response data`y`

.Because you can specify presample data inputs

`Y0`

,`E0`

, and`V0`

of differing lengths,`estimate`

assumes that all specified sets have these characteristics:The final observation (row) in each set occurs simultaneously.

The first observation in the estimation sample immediately follows the last observation in the presample, with respect to the sampling frequency.

If you specify the

`'Display'`

name-value pair argument, the value overrides the`Diagnostics`

and`Display`

settings of the`'Options'`

name-value pair argument. Otherwise,`estimate`

displays optimization information using`'Options'`

settings.`estimate`

uses the outer product of gradients (OPG) method to perform covariance matrix estimation.

[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] Greene, William. H. *Econometric Analysis*. 6th ed. Upper Saddle River, NJ: Prentice Hall, 2008.

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

- Time Base Partitions for ARIMA Model Estimation
- Estimate Multiplicative ARIMA Model
- Estimate Conditional Mean and Variance Model
- Model Seasonal Lag Effects Using Indicator Variables
- Maximum Likelihood Estimation for Conditional Mean Models
- Conditional Mean Model Estimation with Equality Constraints
- Presample Data for Conditional Mean Model Estimation
- Initial Values for Conditional Mean Model Estimation
- Optimization Settings for Conditional Mean Model Estimation

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