The process of economic forecasting is at heart the process of attempting to predict the future of an economy. Generally this refers to the economy of a country, but it may also refer to municipalities within a state.
An accurate economic forecasting model enables companies and governments to capitalize on economic opportunities. Further, and at least as important, it enables these entities to avoid financial risks entirely or just avoid the greatest fallout of unavoidable downturns.
Important internal data for a forecasting model includes quarterly or yearly revenue, employment numbers, payments or debts, and so on. This data is truly internal for businesses as they come from within the companies. For the government, however, these numbers bleed into the category of external data as they must be reported by individuals and companies operating within the country’s borders.
Crucial external data includes surveys and economic indicators (such as GDP or balance of trade). Forecasters use these indicators to calculate Diffusion Indexes (also called Composite Indexes) and to build their forecasting models.
These models go by many names. Some restrict themselves to short-term forecasts or to only some aspect of the economy while others are broader in scope. Among these models are consensus forecasts, economic base analysis, shift-share analysis, and econometric models.
Additional external data forecasters may use include tax policies, potential tax policies, politics, and other data. Academic studies also provide very useful information for forecasting models.
Naturally, one of the main challenges of forecasting is the impossibility of seeing into the future, making no prediction truly reliable. There is also a danger of the calculations in models taking so long that results are not complete before new data arrives.
In addition, economic forecasting comes with a greater than average tendency toward subjectivity. This may be due to the fact that, while economics is technically a science, it is not one in which economists can run experiments. Thus, there are varying schools of thought that emphasize some aspect of the economy as more influential than another and the only test for veracity is to wait and see whether the results of a forecast were accurate.
Decision Analyst: Time-Series Econometric Forecasting: Global Forecast of the Price of a Raw Material
International Journal of Forecasting: A composite approach to forecasting state government revenues:Case study of the Idaho sales tax
Greenwood, Hanson, and colleagues have identified the signs that potentially signal trouble. Three years of rapid growth in credit and asset prices increased the odds of a crisis to 40 percent, up from 7 percent during typical conditions, they reported.
The team studied trends in outstanding credit, stock market values, and home prices from 1950 to 2016 for 42 countries. They found the potential for a financial crisis was highest in years when both stock prices and non-financial business borrowing where rising rapidly, or when both home prices and household debt were growing quickly.
Quadrant – Point of Interest Data measures physical store and website visits so companies can evaluate their performance. Quadrant provides nineteen different PoI categories; they also update their data regularly to provide the most efficient analysis.
African Financial & Economic Data Sector Focus provides in-depth coverage in any sector to evaluate and track economies in Africa.
African Financial & Economic Data Hub provides African economic data through data points.
IBM PAIRS Services provides queryable geospatial and temporal data in the form of maps, satellite images, weather data, drone data, and other data.
CoreLogic ListSource provides property leads to builders and insurers. The ListSource bases most of their data on homeowner and demographic data sources.