Statistical Forecasting - Categories of Forecasting Methods - Causal / Econometric Forecasting Methods

Causal / Econometric Forecasting Methods

Some forecasting methods use the assumption that it is possible to identify the underlying factors that might influence the variable that is being forecast. For example, including information about climate patterns might improve the ability of a model to predict umbrella sales. This is a model of seasonality which shows a regular pattern of up and down fluctuations. In addition to climate, seasonality can also be due to holidays and customs; for example, one might predict that sales of college football apparel will be higher during the football season than during the off season.

Causal forecasting methods are also subject to the discretion of the forecaster. There are several informal methods which do not have strict algorithms, but rather modest and unstructured guidance. Alternatively, one can forecast based on, for example, linear relationships. If one variable is linearly related to the other for a long enough period of time, it may be beneficial to extrapolate such a relationship into the future.

Causal methods include:

• Regression analysis includes a large group of methods that can be used to predict future values of a variable using information about other variables. These methods include both parametric (linear or non-linear) and non-parametric techniques.

• Autoregressive moving average with exogenous inputs (ARMAX)

Quantitative forecasting models are often judged against each other by comparison of their in-sample or out-of-sample mean square error, although some researchers have advised against its use.