Instead of getting into the details or specifics the factors that affect the unemployment rate, I'll describe the basic process of forecasting an economic variable such as the unemployment rate.
The unemployment rate is a measure of how an economy is doing -- a falling unemployment rate indicates an expanding economy in which job seekers are finding work, and a rising unemployment rate indicates a struggling economy in which people with jobs are being laid off and job seekers are having difficulties finding work. As such unemployment rate predictions are typically generated from models of how the economy functions. The first step in economic modeling is to use economic theory (and logic) to identify possible factors (or explanatory variables) that are associated with (or which explain) movements in the unemployment rate. The second step is to determine which of these factors are important and to identify and quantify their relationship with the unemployment rate (i.e., how do they affect the unemployment rate and by how much?). This is done through statistical analysis of available historical data series, and more specifically, through multiple regression analysis. Economic models can be very complex.
Forecasting basically projects the trends and relationships identified by the economic model into the future. However, to do this one must make assumptions about how the factors (or explanatory variables) that affect the unemployment rate will behave in the future. In many cases, one might conclude recently observed trends will continue into the future (for example, demographic variables). Other aspects of forecasting are more difficult. For example, how might economic policies that influence the unemployment rate change during the life of the forecast (for example, Federal Reserve interest rate policy)? How will more volatile factors behave over time (for example, energy prices)?
Because economic forecasts depend upon assumptions about how the future will be, economists often say that forecasting is as much art as it is science. Following are a few general observations one can make about the accuracy of economic forecasting and/or predictions of a variable such as the unemployment rate.
- The more complete and up-to-date are the data one inputs into the economic model, the more accurate and reliable the forecast will be.
- Forecasts are only as reliable as the assumptions one makes about the future.
- The accuracy of a forecast or predicted unemployment rate diminishes the farther out in time one goes. While one may conclude with a reasonable degree of confidence that trends observed in the last few months will continue over the next few months, over time there is an increasingly greater likelihood that the model's explanatory variables will not behave as one thought they would, or an unanticipated event or shock will occur that affects the economy.