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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

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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
estimateFit univariate regression model with ARIMA errors to data
inferInfer residuals of univariate regression model with ARIMA time series errors
summarizeDisplay estimation results of regression model with ARIMA errors
simulateMonte Carlo simulation of univariate regression model with ARIMA time series errors
filterFilter disturbances through regression model with ARIMA errors
impulseGenerate regression model with ARIMA errors impulse response function
forecastForecast responses of univariate regression model with ARIMA time series errors

Examples and How To

Create Model

Fit Model to Data

Generate Simulations or Impulse Responses

Generate Minimum Mean Square Error Forecasts

Concepts