This statistic tests for autocorrelation in time series where independent variables are lagged by one or more periods.
If Durbin-h is equal to or greater than 1.96, it is likely that autocorrelation exists. The Durbin-h test is suitable for large samples; that is, for samples of 100 or more time series values.
Autocorrelation occurs when the error terms of a regression model are not independent; that is, when the values of historical periods in the forecast model are influencing the values of current periods. Time series with a strong seasonal or cyclical pattern are often highly correlated. High autocorrelation means that MLR using the ordinary least squares method is not a suitable forecasting technique for this data.