What Are the Different Methods of Macroeconomic Forecasting?

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  • Written By: John Lister
  • Edited By: O. Wallace
  • Last Modified Date: 18 October 2019
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Macroeconomic forecasting involves making predictions about the entire economy of a country or even the world. Some such forecasting techniques are described as empirical: they look at the past relationship between different economic data and assume the same pattern of cause and effect will continue. Other types of macroeconomic forecasting involve working on the basis that everyone involved in an economy will make rational choices.

The purpose of macroeconomic forecasting is to look at an entire economy. This is in contrast to microeconomics, which looks at a particular market, for example the way that demand and supply affect the sales and price of widgets, or the labor market for widget makers. Macroeconomics is more complicated as it not only involves numerous individual markets, but also the effects of factors such as interest rates and taxation.

The simplest type of macroeconomic forecasting is theoretical models. These work on basic rules that are held to be true. For example, one such "rule" could be that if interest rates are halved, people's disposable income will increase by 20 percent because of lower mortgage payments, and that this will lead to 10 percent higher sales of goods in the economy with prices rising by five percent. The two main drawbacks of such forecasting is that it is difficult to know how accurate the models are, and that the sheer complexity of a large economy can exaggerate any inaccuracies in the model immensely.


A more complicated variant of macroeconomic forecasting is known as empirical forecasting. This involves looking at actual past data and drawing conclusions. For example, a forecaster could look at the changes in income tax and the changes in total purchasing each year in the past and try to establish a common relationship. This will not necessarily be that which would be expected from a purely theoretical perspective. This past relationship can then be applied to future forecasts. Such models vary immensely in complexity depending on how much data is used and how many factors are accounted for.

Arguably the most complicated type of macroeconomic forecasting are those that are categorized as microfounded, such as dynamic stochastic general equilibrium models. Microfounded forecasting involves breaking the economy into the smallest parts possible, preferably to each person or organization that makes decisions, such as consumers deciding what to buy, manufacturers deciding where to get supplies, or governments deciding the level of sales tax. The technique then involves working out which decisions would most serve self-interest, whether that mean consumers getting value for their money, manufacturers trying to boost profits, or governments trying to maximize tax revenue without hurting the economy. The economists then build this into a complicated model that can forecast the effects of a particular change when every party acts in the most rational way.


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