Economic indicators are statistics that are used to judge the way the economy is performing. There are three types of economic indicators: procyclic, countercyclic and acyclic. Procyclic indicators move in the same direction as the economy. Countercyclic indicators will move in the opposite direction to the economy: when the economy weakens, a countercyclic indicator will strengthen, and vice versa. Acyclic indicators reflect no indication on how the economy is performing.
In addition, the timing of the economic indicators can be leading, lagging or coincidental. Leading indicators will reflect a change in the econmy prior to the economic change. The stock market is an example of a procyclic leading economic indicator: the stock market often weakens before an economic recession and strengthens before an economic boom. Lagging indicators will reflect a change in the economy after the economic change. The bank prime rate is considered a procyclic lagging indicator:
In addition, the timing of the economic indicators can be leading, lagging or coincidental. Leading indicators will reflect a change in the economy prior to the economic change. The stock market is an example of a procyclic leading economic indicator: the stock market often weakens before an economic banks raise their prime rate when the economy is performing well and reduce their prime rate when the economy is struggling. A coincidental indicator moves at the same time as the economy. Real and nominal GDP are procyclic coincidental economic indicators: the GDP rises and lowers at the same time as the economy.
MEASUREMENTS OF FORECAST ACCURACY
The goal of any forecasting system is to be as accurate as possible. One measure of accuracy is to minimize error, which includes two components: bias and magnitude. We suggest that managers use at least one measure of bias and one measure of magnitude on an ongoing basis to assess the forecasting accuracy.
Bias is defined as the systematic difference between the forecast and the actual result over a period of time. Examples of forecast error measurements of bias include: mean error (ME), cumulative forecast error (CFE) and mean per cent error (MPE). A large positive or negative bias suggests that the forecast is consistently too high or too low, respectively. When a bias has been identified, management can correct future forecasts by adding or subtracting the amount of bias from the predicted forecast.
Magnitude indicates the variance between the forecast and the actual result. Examples of forecast error measurements of magnitude include: mean absolute deviation (MAD), standard deviation, mean squared error (MSE) and mean absolute per cent error (MAPE). When the magnitude of the error indicates that the forecast is highly inaccurate, future forecasts cannot be adjusted as easily as with a bias inaccuracy. A high magnitude of the error indicates that there is a problem with the forecasting system: the forecasting system needs to be reviewed and revised. FORECASTING SYSTEM
In developing managerial forecasting systems, there may be a trade-off between accuracy and cost. The executive opinion method is considered one of the most expensive forms of forecasting, yet most managers are more comfortable using their own judgments for forecast development rather than using statistical methods (Makridakis, 1986). In addition, most managers prefer the judgment method because they believe that the forecast will be more accurate, and the data required for statistical methods may be difficult to obtain (Sanders and Manrodt, 1994). When using statistical methods, managers may believe that more expensive and complicated statistical forecasting methods are more accurate; however, these methods were not found to be more accurate than less complicated statistical forecasting methods in terms of bias and magnitude (Sanders and...