The cost to your firm of unanticipated changes can be enormous. Standard time series methods simply extrapolate trends, projecting the recent past into the future. That may be adequate, until the unexpected occurs. Our forecasts rely on economic indicators to anticipate cyclical turning points several quarters in advance. Forecast accuracy is also enhanced by accounting for the unique characteristics of a given geographical area.
Location quotients assist in finding high concentrations of particular industries.
The area's shift shares and growth rates are used to find sectors that are growing or shrinking. Our competitiveness measures indicate whether an industry's growth is exceptional with respect to the nation. All these elements are used as explanatory variables in our forecasts. The purpose of a forecast is to anticipate changes in both the magnitude and the direction of the series of interest. While the estimation procedures of standard time series methods often provide a good fit to historical data, they are incapable of predicting turning points in time series data because they do not account for factors that can cause a change in direction .