Create Model

Specify Conditional Mean Models

Create conditional mean models using arima or the Econometric Modeler app.

Modify Properties of Conditional Mean Model Objects

Change modifiable model properties using dot notation.

Specify Conditional Mean Model Innovation Distribution

Specify Gaussian or t distributed innovations process, or a conditional variance model for the variance process.

Specify t Innovation Distribution Using Econometric Modeler App

Interactively specify a t innovation distribution for an ARIMA model.

AR Model Specifications

Create stationary autoregressive models using arima or the Econometric Modeler app.

MA Model Specifications

Create invertible moving average models using arima or the Econometric Modeler app.

ARMA Model Specifications

Create stationary and invertible autoregressive moving average models using arima or the Econometric Modeler app.

ARIMA Model Specifications

Create autoregressive integrated moving average models using arima or the Econometric Modeler app.

ARIMAX Model Specifications

Create ARIMAX models using arima or the Econometric Modeler app.

Multiplicative ARIMA Model Specifications

Create multiplicative ARIMA models using arima or the Econometric Modeler app.

Specify Multiplicative ARIMA Model

Create a seasonal ARIMA model.

Specify Conditional Mean and Variance Models

Create a composite conditional mean and variance model.

Fit Model to Data

Time Base Partitions for ARIMA Model Estimation

When you fit a time series model to data, lagged terms in the model require initialization, usually with observations at the beginning of the sample.

Implement Box-Jenkins Model Selection and Estimation Using Econometric Modeler App

Interactively implement the Box-Jenkins methodology to select the appropriate number of lags for a conditional mean model. Then, fit the model to data and export the estimated model to the command line to generate forecasts.

Box-Jenkins Differencing vs. ARIMA Estimation

Compare Box-Jenkins and ARIMA estimation.

Choose ARMA Lags Using BIC

Select ARMA model using information criteria.

Estimate Multiplicative ARIMA Model Using Econometric Modeler App

Interactively estimate a multiplicative seasonal ARIMA model.

Estimate Multiplicative ARIMA Model

Estimate a multiplicative seasonal ARIMA model.

Model Seasonal Lag Effects Using Indicator Variables

Estimate a seasonal ARIMA model by specifying a multiplicative model or using seasonal dummies.

Estimate ARIMAX Model Using Econometric Modeler App

Interactively specify and estimate an ARIMAX model.

Estimate Conditional Mean and Variance Model

Estimate a composite conditional mean and variance model.

Perform ARIMA Model Residual Diagnostics Using Econometric Modeler App

Interactively evaluate model assumptions after fitting data to an ARIMA model by performing residual diagnostics.

Infer Residuals for Diagnostic Checking

Infer residuals from a fitted ARIMA model.

Share Results of Econometric Modeler App Session

Export variables to the MATLAB^® Workspace, generate plain text and live functions that return a model estimated in an app session, or generate a report recording your activities on time series and estimated models in an Econometric Modeler app session.

Generate Simulations or Impulse Responses

Simulate Stationary Processes

Simulate stationary autoregressive models and moving average models.

Simulate Trend-Stationary and Difference-Stationary Processes

Illustrate the distinction between trend-stationary and difference-stationary processes by simulation.

Simulate Multiplicative ARIMA Models

Simulate sample paths from a multiplicative seasonal ARIMA model.

Simulate Conditional Mean and Variance Models

Simulate responses and conditional variances from a composite conditional mean and variance model.

Plot the Impulse Response Function

Plot the impulse response function for various models.

Generate Minimum Mean Square Error Forecasts

Compare Predictive Performance After Creating Models Using Econometric Modeler App

Interactively choose lags for an ARIMA model by comparing the AIC values of estimated models. Then, export several models to the command line to compare their predictive performance.

Forecast Multiplicative ARIMA Model

Forecast a multiplicative seasonal ARIMA model.

Convergence of AR Forecasts

Evaluate the asymptotic convergence of forecasts from an AR model, and compare forecasts made with and without using presample data.

Forecast Conditional Mean and Variance Model

Forecast responses and conditional variances from a composite conditional mean and variance model.

Forecast IGD Rate from ARX Model

Forecast an ARIMAX model by computing MMSE forecasts or using Monte Carlo simulation.

Specify Presample and Forecast Period Data To Forecast ARIMAX Model

This example shows how to partition a timeline into presample, estimation, and forecast periods, and it shows how to supply the appropriate number of observations to initialize a dynamic model for estimation and forecasting.