Linear Regression Forecasting Method by Companies

by Andrew Gellert

It can be highly beneficial for companies to develop a forecast of the future values of some important metrics, such as demand for its product or variables that describe the economic climate. There are several different methods of making forecasts, but they all fall into two categories: causal methods and time-series methods. Linear regression is a time-series method that uses basic statistics to project future values for a target variable.

Forecasting Methods

The two main categories of forecasting take very different approaches. Causal forecasting attempts to predict a variable by trying to explain what factors cause it to change. For example, a causal model to predict market demand for a product might use the product's price, competitors' prices and the amount of money spent on advertising to explain what product demand might be six months in the future. Causal forecasting can be insightful, and is useful to illustrate "what if" scenarios, but the models are more difficult to devise and implement than time-series models.

Methods: Time-Series

Time-series analysis uses only the historical performance of the variable to make predictions about its future. There are many ways to decide how to use the historical data, but no other variables will be present in a time series model. The time-series approach can't yield information about how a variable will behave in different conditions, but it can produce a powerful estimate of the variable relatively efficiently.

Linear Regression Approach

Linear regression can be used in both types of forecasting methods. In the case of causal methods, the causal model may consist of a linear regression with several explanatory variables. This method is useful when there is no time component. For example, a company might want to forecast when a material will melt under different conditions of temperature and pressure. For time-series analysis, it is possible to develop a linear regression model that simply fits a line to the variable's historical performance and extrapolates that into the future. This is unable to account for seasonality or other cycles, as well as nonlinearity, but if the variable in question is plausibly linear, using linear regression to forecast it might yield a useful prediction.

Using Linear Regression

Because much economic data has cycles, multiple trends and non-linearity, simple linear regression is often inappropriate for time-series work. On the other hand, linear regression and related statistical approaches are useful for causal models due to their ability to take into account several different factors and evaluate the impact of each one. The proper use of linear regression depends on the data and the goals of the forecaster.

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