Main Content

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.

For conditional mean model tools that support ARIMA model creation and analysis, see Conditional Mean Models.

Apps

Econometric ModelerAnalyze and model econometric time series

Functions

expand all

regARIMACreate regression model with ARIMA time series errors
arimaConvert regression model with ARIMA errors to ARIMAX model
hacHeteroscedasticity and autocorrelation consistent covariance estimators
fglsFeasible generalized least squares
estimateEstimate parameters of regression models with ARIMA errors
inferInfer innovations of regression models with ARIMA errors
summarizeDisplay estimation results of regression model 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

Fit Model to Data

Generate Simulations or Impulse Responses

Generate Minimum Mean Square Error Forecasts

Concepts