Economic forecasting builds models that seek to predict future economic trends. The central issue is whether a certain constellation of variables inevitably leads to some outcome. Defining usable variables with reliable data is the main task of this field.
Economic forecasting seeks to understand what forces in the economy create growth. Economists want to find out what causes events such as industrialization, exports, inflation or depression. For example, a common forecasting issue concerns the effect of high rates of consumer debt on the chances of any economic recovery. If you take the view that increased consumer spending is the main cause of recovery, then your forecast will be pessimistic. All forecasting models begin with a concept, a theory that is based on a phenomenon — such as debt — that will drive an economy one way or another.
Economists who forecast economic trends rely completely on models. Data itself says nothing unless it is analyzed. Models are based on assumptions that a group of variables cause a phenomenon. For example, most economists subscribe to the basic model that rising interest rates cause an economic slowdown because money becomes more expensive. This is a simple supply and demand model of money that is fundamental because borrowing is crucial to all modern economies. Therefore, such basic models are assumptions that a rise in rates signals the scarcity of available liquidity. This leads to a decline in economic growth. Therefore, a theory is a basic set of assumptions, while a model is a detailed analysis arranged to test such assumptions.
Without variables, neither models nor data make any sense. Variables are conceptual groupings of economic forces. One important problem is overlapping variables, or variables that measure the same thing. In many cases, high interest rates are closely correlated with an increase in bond investments. So if you are making a model that is forecasting money market behavior, separating out bond investments from interest rates as two separate variables might be a problem. Since those two variables are so closely related, they actually might be one variable. Treating them as two will create a skewed and worthless model. Variables must be unique economic entities measuring different — not overlapping — forces. Keeping variables unique is one of the most difficult problems in forecasting.
Data is the eternal problem of economic forecasting. When one is dealing with unemployment, for example, the data can be slippery. There are many models of unemployment that assume different definitions of the term. One view assumes that the unemployed are all those who are receiving unemployment benefits. Of course, there are many who are not receiving, or are no longer receiving, benefits. Then there are those who are only partly employed or employed under the table. There are underemployed due to job scarcity. These are different types of unemployment that all depend on how you define the term. How you define a variable, therefore, sets the stage for the quality of the data and the usability of a model.
- Futurecasts.com: Understanding Economic Forecasts
- EDR Group Working Paper; Models to Predict the Economic Development Impact of Transportation Projects: Historical Experience and New Applications; Glen Weisbrod
- Florida State University; Department of Urban and Regional Planning; Planning Methods III; Forecasting