Which well-accepted methods should be used to provide benchmark forecasts?
The simplest forecasting method for time series is the random walk. It assumes that the future value of a time series will be equal to the current value. In other words, one does not have useful information about the future changes in the series: it is equally likely to go up or down. The random walk provides relatively accurate forecasts in many circumstances, and should serve as one basis for comparing performance. Forecasts from simple exponential smoothing methods are also frequently used as benchmarks. For cross-sectional data, one can use the group average (or base rate) as a forecast. When available, markets offer good benchmarks. Market prices are based on large numbers of people who put money on their forecasts. So, for example, studies since the 1930s have shown that, without inside knowledge, it has been impossible to improve upon current prices in forecasting the stock market. In the past decade, a renewed interest in markets shows how they can use a groups information much
