Documentation

This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English verison of the page.

Note: This page has been translated by MathWorks. Please click here
To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

Autocorrelated and Heteroscedastic Disturbances

Regression models with nonspherical errors, and HAC and FGLS estimators

To explicitly model for serial correlation in the disturbance series, create a regression model with ARIMA errors (regARIMA model object). Alternatively, to acknowledge the presence of nonsphericality, you can estimate a heteroscedastic-and-autocorrelation-consistent (HAC) coefficient covariance matrix, or implement feasible generalized least squares (FGLS). For more details on HAC and FGLS estimators, see Time Series Regression X: Generalized Least Squares and HAC Estimators.

Classes

regARIMACreate regression model with ARIMA time series errors

Functions

regARIMACreate regression model with ARIMA time series errors
arimaConvert regression model with ARIMA errors to ARIMAX model
estimateEstimate parameters of regression models with ARIMA errors
hacHeteroscedasticity and autocorrelation consistent covariance estimators
fglsFeasible generalized least squares
inferInfer innovations of regression models with ARIMA errors
printDisplay estimation results for regression models with ARIMA errors
simulateMonte Carlo simulation of regression model with ARIMA errors
filterFilter disturbances through regression model with ARIMA errors
impulseImpulse response of regression model with ARIMA errors
forecastForecast responses of regression model with ARIMA errors

Examples and How To

Create Model

Create Regression Models with ARIMA Errors

Create a model that represents an econometric phenomenon.

Specify the Default Regression Model with ARIMA Errors

Create a default regression model with ARIMA errors using regARIMA.

Create Regression Models with AR Errors

Create various regression models with AR errors using regARIMA.

Create Regression Models with MA Errors

Create various regression models with MA errors using regARIMA.

Create Regression Models with ARMA Errors

Create various regression models with ARMA errors using regARIMA.

Create Regression Models with ARIMA Errors

Create various regression models with ARIMA errors using regARIMA.

Create Regression Models with SARIMA Errors

Create various regression models with SARIMA errors using regARIMA.

Specify ARIMA Error Model Innovation Distribution

Choose between Gaussian- or t-distributed innovations.

Specify a Regression Model with SARIMA Errors

Create a regression model with multiplicative seasonal ARIMA errors.

Modify regARIMA Model Properties

Change aspects of an existing model.

Plot Impulse Response of Regression Model with ARIMA Errors

Plot impulse response functions of various regression models with ARIMA errors.

Alternative ARIMA Model Representations

Convert between ARMAX and regression models with ARMA errors.

Fit Model to Data

Estimate a Regression Model with ARIMA Errors

Estimate the sensitivity of the US Gross Domestic Product (GDP) to changes in the Consumer Price Index (CPI) using estimate.

Estimate a Regression Model with Multiplicative ARIMA Errors

Fit a regression model with multiplicative ARIMA errors to data using estimate.

Alternative ARIMA Model Representations

Convert between ARMAX and regression models with ARMA errors.

Choose Lags for an ARMA Error Model

To select the nonseasonal autoregressive and moving average lag polynomial degrees for a regression model with ARMA errors, use Akaike Information Criterion (AIC).

Plot a Confidence Band Using HAC Estimates

Plot corrected confidence bands using Newey-West robust standard errors.

Change the Bandwidth of a HAC Estimator

Change the bandwidth when estimating a HAC coefficient covariance, and compare estimates over varying bandwidths and kernels.

Compare Robust Regression Techniques

Address influential outliers using regression models with ARIMA errors, bags of regression trees, and Bayesian linear regression.

Generate Simulations or Impulse Responses

Simulate Regression Models with ARMA Errors

Simulate observations from various regression models with ARMA errors.

Simulate Regression Models with Nonstationary Errors

Simulate regression model with nonstationary and exponential errors.

Simulate Regression Models with Multiplicative Seasonal Errors

Simulate regression model with stationary and difference stationary errors.

Forecast a Regression Model with ARIMA Errors

Forecast a regression model with ARIMA(3,1,2) errors using forecast and simulate.

Generate Minimum Mean Square Error Forecasts

Forecast a Regression Model with ARIMA Errors

Forecast a regression model with ARIMA(3,1,2) errors using forecast and simulate.

Forecast a Regression Model with Multiplicative Seasonal ARIMA Errors

Forecast a multiplicative seasonal ARIMA model using forecast.

Verify Predictive Ability Robustness of a regARIMA Model

Forecast a regression model with ARIMA errors, and check the model predictability robustness.

Concepts

Impulse Response of Regression Models with ARIMA Errors

Learn about impulse response functions of regression models with ARIMA errors.

Nonspherical Models

Learn about innovations that exhibit autocorrelation and heteroscedasticity.

Regression Models with Time Series Errors

What are regression models with ARIMA errors?

Time Series Regression Models

Define different types of time series regression models.

Initial Values for regARIMA Model Estimation

Learn how MATLAB® uses initial parameter values during estimation.

Intercept Identifiability in Regression Models with ARIMA Errors

Learn about intercept identifiability in regression model with ARIMA errors.

Select Regression Model with ARIMA Errors

Learn how to select an appropriate regression model with ARIMA errors.

Maximum Likelihood Estimation of regARIMA Models

Learn about maximum likelihood estimation for regression models with ARIMA errors.

Optimization Settings for regARIMA Model Estimation

Learn about optimization settings for regression model with ARIMA errors estimation.

Presample Values for regARIMA Model Estimation

Learn how MATLAB uses presample values during estimation.

regARIMA Model Estimation Using Equality Constraints

Estimate regression model with ARIMA errors with equality constraints.

Monte Carlo Simulation of Regression Models with ARIMA Errors

Learn about generating independent, random draws from a regression model with ARIMA errors.

Presample Data for regARIMA Model Simulation

Learn about the presample data required to simulate a regression model with ARIMA errors.

Transient Effects in regARIMA Model Simulations

Learn about how presample data affects a simulated path.

Monte Carlo Forecasting of regARIMA Models

Learn about forecasting a regression model with ARIMA errors using many simulated paths.

MMSE Forecasting Regression Models with ARIMA Errors

Learn about minimum mean square error forecasts.

Was this topic helpful?