What is the primary concern if differencing is not performed on serially correlated data?

The primary concern when differencing is not performed on serially correlated data is that the parameters for revenue estimates will be invalid. When data exhibits serial correlation, it implies that the values in the dataset are correlated with their own past values. This can lead to inaccurate estimations and predictions, as traditional regression models assume that the observations are independent.

In the context of revenue estimates, if the model does not account for this dependency through differencing, the statistical assumptions underlying the estimation may be violated. Consequently, the coefficients derived from such a model could be biased and misleading, thereby resulting in flawed forecasts and potential misallocation of resources or poor financial planning.

Using differencing helps to stabilize the mean of the time series and eliminate the correlation between consecutive observations, leading to more reliable parameter estimates and forecasts. This consideration is crucial for public finance officers as accurate revenue projections are essential for effective budget planning and management.