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The **Econometric
Modeler** app enables you to transform time series data based on deterministic
or stochastic trends you see in plots or hypothesis test conclusions. Available
transformations in the app are log, seasonal and nonseasonal difference, and linear
detrend. These examples show how to apply each transformation to time series
data.

This example shows how to stabilize a time series, whose
variability grows with the level of the series, by applying the log
transformation. The data set, which is stored in
`mlr/examples/econ/Data_Airline.mat`

, contains monthly counts of airline
passengers. The folder `mlr`

is the value of
`matlabroot`

.

At the command line, load the `Data_Airline.mat`

data set.

load(fullfile(matlabroot,'examples','econ','Data_Airline.mat'))

At the command line, open the **Econometric Modeler** app.

econometricModeler

Alternatively, open the app from the apps gallery (see **Econometric
Modeler**).

Import `DataTable`

into the app:

On the

**Econometric Modeler**tab, in the**Import**section, click .In the

**Import Data**dialog box, in the**Import?**column, select the check box for the`DataTable`

variable.Click

**Import**.

The variable `PSSG`

appears in the **Data
Browser**, and its time series plot is in the **Time
Series Plot(PSSG)** figure window.

Fit a SARIMA(0,1,1)×(0,1,1)_{12} model to the data in levels:

On the

**Econometric Modeler**tab, in the**Models**section, click the arrow to display the model gallery.In the models gallery, in the

**ARMA/ARIMA Models**section, click**SARIMA**.In the

**SARIMA Model Parameters**dialog box, on the**Lag Order**tab:**Nonseasonal**sectionSet

**Degrees of Integration**to`1`

.Set

**Moving Average Order**to`1`

.Clear the

**Include Constant Term**check box.

**Seasonal**sectionSet

**Period**to`12`

to indicate monthly data.Set

**Moving Average Order**to`1`

.Select the

**Include Seasonal Difference**check box.

Click

**Estimate**.

The model variable `SARIMA_PSSG`

appears in the
**Models** section of the **Data
Browser** and its estimation summary appears in the
**Model Summary(SARIMA_PSSG)** document.

The spread of the residuals increases with the level of the data, which is indicative of heteroscedasticity.

Apply the log transform to `PSSG`

:

In the

**Data Browser**, select`PSSG`

.On the

**Econometric Modeler**tab, in the**Transforms**section, click**Log**.

The transformed variable `PSSGLog`

appears in the
**Data Browser**, and its time series plot appears in the
**Time Series Plot(PSSGLog)** figure window.

The exponential growth appears removed from the series.

With `PSSGLog`

selected in the **Data
Browser**, fit the
SARIMA(0,1,1)×(0,1,1)_{12} model to the logged
series using the same dialog box settings that you used for
`PSSG`

. The estimation summary appears in the
**Model Summary(SARIMA_PSSGLog)** document.

The spread of the residuals does not appear to change systematically with the levels of the data.

This example shows how to stabilize a time series by applying
multiple nonseasonal difference operations. The data set, which is stored in
`Data_USEconModel.mat`

, contains the US gross domestic
product (GDP) measured quarterly, among other series.

At the command line, load the `Data_USEconModel.mat`

data
set.

`load Data_USEconModel`

At the command line, open the **Econometric Modeler** app.

econometricModeler

Alternatively, open the app from the apps gallery (see **Econometric
Modeler**).

Import `DataTable`

into the app:

On the

**Econometric Modeler**tab, in the**Import**section, click .In the

**Import Data**dialog box, in the**Import?**column, select the check box for the`DataTable`

variable.Click

**Import**.

The variables, including `GDP`

, appear in the
**Data Browser**, and a time series plot of all the
series appears in the **Time Series Plot(COE)** figure
window.

In the **Data Browser**, double-click
`GDP`

. A time series plot of
`GDP`

appears in the **Time Series
Plot(GDP)** figure window.

The series appears to grow without bound.

Apply the first difference to `GDP`

. On the
**Econometric Modeler** tab, in the
**Transforms** section, click
**Difference**.

In the **Data Browser**, a variable representing the
differenced GDP (`GDPDiff`

) appears. A time series
plot of the differenced GDP appears in the **Time Series
Plot(GDPDiff)** figure window.

The differenced GDP series appears to grow without bound after 1970.

Apply the second difference to the GDP by differencing the differenced
GDP. With `GDPDiff`

selected in the **Data
Browser**, on the **Econometric Modeler** tab,
in the **Transforms** section, click
**Difference**.

In the **Data Browser**, a variable representing the
transformed differenced GDP (`GDPDiffDiff`

)
appears. A time series plot of the differenced GDP appears in the
**Time Series Plot(GDPDiffDiff)** figure window.

The transformed differenced GDP series appears stationary, although heteroscedastic.

This example shows how to convert a series of prices to
returns. The data set, which is stored in
`Data_USEconModel.mat`

, contains the US GDP measured
quarterly, among other series.

At the command line, load the `Data_USEconModel.mat`

data
set.

`load Data_USEconModel`

At the command line, open the **Econometric Modeler** app.

econometricModeler

Alternatively, open the app from the apps gallery (see **Econometric
Modeler**).

Import `DataTable`

into the app:

On the

**Econometric Modeler**tab, in the**Import**section, click .In the

**Import Data**dialog box, in the**Import?**column, select the check box for the`DataTable`

variable.Click

**Import**.

`GDP`

, among other series, appears in the
**Data Browser**, and a time series plot containing all
series appears in the figure window.

In the **Data Browser**, double-click
`GDP`

. A time series plot of
`GDP`

appears in the **Time Series
Plot(GDP)** figure window.

The GDP series, as a price, appears to grow without bound.

Convert the GDP prices to returns:

With

`GDP`

selected in the**Data Browser**, on the**Econometric Modeler**tab, in the**Transforms**section, click**Log**.In the

**Data Browser**, a variable representing the logged GDP prices (`GDPLog`

) appears.With

`GDPLog`

selected in the**Data Browser**, in the**Transforms**section, click**Difference**.

In the **Data Browser**, a variable representing the GDP
returns (`GDPLogDiff`

) appears. A time series plot
of the GDP returns appears in the **Time Series
Plot(GDPLogDiff)** figure window.

Rename the `GDPLogDiff`

variable to
`GDPReturns`

:

In the

**Data Browser**, right-click`GDPLogDiff`

.In the context menu, select

**Rename**.Enter

`GDPReturns`

.

The app updates the names of all documents associated with the GDP returns.

The series of GDP returns appears stationary, but observations appear serially correlated.

This example shows how to stabilize a time series exhibiting
seasonal integration by applying a seasonal difference. The data set, which is stored in
`mlr/examples/econ/Data_Airline.mat`

, contains monthly counts of airline
passengers. The folder `mlr`

is the value of
`matlabroot`

.

At the command line, load the `Data_Airline.mat`

data set.

load(fullfile(matlabroot,'examples','econ','Data_Airline.mat'))

At the command line, open the **Econometric Modeler** app.

econometricModeler

Alternatively, open the app from the apps gallery (see **Econometric
Modeler**).

Import `DataTable`

into the app:

On the

**Econometric Modeler**tab, in the**Import**section, click .**Import Data**dialog box, in the**Import?**column, select the check box for the`DataTable`

variable.Click

**Import**.

The variable `PSSG`

appears in the **Data
Browser**, and its time series plot appears in the
**Time Series Plot(PSSG)** figure window.

Address the seasonal trend by applying the 12th order seasonal difference.
On the **Econometric Modeler** tab, in the
**Transforms** section, set
**Seasonal** to `12`

. Then, click
**Seasonal**.

The transformed variable `PSSGSeasonalDiff`

appears in the **Data Browser**, and its time series plot
appears in the **Time Series Plot(PSSGSeasonalDiff)**
figure window.

The transformed series appears to have a nonseasonal trend.

Address the nonseasonal trend by applying the first difference. With
`PSSGSeasonalDiff`

selected in the
**Data Browser**, on the **Econometric
Modeler** tab, in the **Transforms** section,
click **Difference**.

The transformed variable `PSSGSeasonalDiffDiff`

appears in the **Data Browser**, and its time series plot
appears in the **Time Series Plot(PSSGSeasonalDiffDiff)**
figure window.

The transformed series appears stationary, but observations appear serially correlated.

Rename the `PSSGSeasonalDiffDiff`

variable to
`PSSGStable`

:

In the

**Data Browser**, right-click`PSSGSeasonalDiffDiff`

.In the context menu, select

**Rename**.Enter

`PSSGStable`

.

The app updates the names of all documents associated with the transformed series.

This example shows how to remove a least-squares-derived
deterministic trend from a nonstationary time series. The data set, which is stored in
`mlr/examples/econ/Data_Airline.mat`

, contains monthly counts of airline
passengers. The folder `mlr`

is the value of
`matlabroot`

.

At the command line, load the `Data_Airline.mat`

data set.

load(fullfile(matlabroot,'examples','econ','Data_Airline.mat'))

At the command line, open the **Econometric Modeler** app.

econometricModeler

Alternatively, open the app from the apps gallery (see **Econometric
Modeler**).

Import `DataTable`

into the app:

On the

**Econometric Modeler**tab, in the**Import**section, click .**Import Data**dialog box, in the**Import?**column, select the check box for the`DataTable`

variable.Click

**Import**.

The variable `PSSG`

appears in the **Data
Browser**, and its time series plot appears in the
**Time Series Plot(PSSG)** figure window.

Apply the log transformation to the series. On the **Econometric
Modeler** tab, in the **Transforms** section,
click **Log**.

The transformed variable `PSSGLog`

appears in the
**Data Browser**, and its time series plot appears in
the **Time Series Plot(PSSGLog)** figure window.

Identify the deterministic trend by using least squares. Then, detrend the
series by removing the identified deterministic trend. On the
**Econometric Modeler** tab, in the
**Transforms** section, click
**Detrend**.

The transformed variable `PSSGLogDetrend`

appears
in the **Data Browser**, and its time series plot appears
in the **Time Series Plot(PSSGLogDetrend)** figure
window.

`PSSGLogDetrend`

does not appear to have a
deterministic trend, although it has a marked cyclic trend.