Regression with ARIMA errors is a statistical technique that combines regression analysis with an ARIMA model to account for autocorrelation in the residuals of the regression. This approach allows researchers to incorporate both predictor variables and time series characteristics, enabling a more accurate analysis of data that may exhibit temporal dependencies or structural breaks.
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