Autoregressive (AR), moving average (MA), ARMA, ARIMA, ARIMAX, and seasonal models

Econometric Modeler | Analyze and model econometric time series |

**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.

Create stationary autoregressive models using `arima`

or the Econometric Modeler app.

Create invertible moving average models using `arima`

or the Econometric Modeler app.

Create stationary and invertible autoregressive moving average models using `arima`

or the Econometric Modeler app.

Create autoregressive integrated moving average models using `arima`

or the Econometric Modeler app.

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.

**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.

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.

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.

**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.

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.

**Econometric Modeler App Overview**

The Econometric Modeler app is an interactive tool for visualizing and analyzing univariate time series data.

**Specifying Lag Operator Polynomials Interactively**

Specify lag operator polynomial terms for time series model estimation using Econometric Modeler.

Learn about the characteristics and forms of conditional mean models.

Learn about autoregressive models.

Learn about moving average models.

**Autoregressive Moving Average Model**

Learn about autoregressive, moving average models.

Learn about autoregressive integrated moving average models.

Learn about addressing seasonality and potential seasonal unit roots using multiplicative ARIMA models.

**ARIMA Model Including Exogenous Covariates**

Learn about ARIMA models that include a linear term for exogenous variables.

**Maximum Likelihood Estimation for Conditional Mean Models**

Learn how maximum likelihood is carried out for conditional mean models.

**Conditional Mean Model Estimation with Equality Constraints**

Constrain the model during estimation using known parameter values.

**Presample Data for Conditional Mean Model Estimation**

Specify presample data to initialize the model.

**Initial Values for Conditional Mean Model Estimation**

Specify initial parameter values for estimation.

**Optimization Settings for Conditional Mean Model Estimation**

Troubleshoot estimation issues by specifying alternative optimization options.

**Monte Carlo Simulation of Conditional Mean Models**

Learn about Monte Carlo simulation.

**Presample Data for Conditional Mean Model Simulation**

Learn about presample requirements for simulation.

**Transient Effects in Conditional Mean Model Simulations**

Learn how to minimize transient effects.

**Monte Carlo Forecasting of Conditional Mean Models**

Learn about Monte Carlo forecasting.

Learn about impulse response functions.

**MMSE Forecasting of Conditional Mean Models**

Learn about MMSE forecasting.