Federal Monetary Policy Report: Alan Greenspan - Finance Committee (1988)

Many institutions engage in economic forecasting, including international organisations such as the IMF, World Bank and the OECD, national governments and central banks, and private sector entities, be they think-tanks, banks or others. Some forecasts are produced annually, but many are updated more frequently. Economists select which variables are important to the subject material under discussion. Economists may use statistical analysis of historical data to determine the apparent relationships between particular independent variables and their relationship to the dependent variable under study. For example, to what extent did changes in housing prices affect the net worth of the population overall in the past? This relationship can then be used to forecast the future. That is, if housing prices are expected to change in a particular way, what effect would that have on the future net worth of the population? Forecasts are generally based on sample data rather than a complete population, which introduces uncertainty. The economist conducts statistical tests and develops statistical models (often using regression analysis) to determine which relationships best describe or predict the behavior of the variables under study. Historical data and assumptions about the future are applied to the model in arriving at a forecast for particular variables.[1] The economist typically considers risks (i.e., events or conditions that can cause the result to vary from their initial estimates). These risks help illustrate the reasoning process used in arriving at the final forecast numbers. Economists typically use commentary along with data visualization tools such as tables and charts to communicate their forecast. The process of economic forecasting is similar to data analysis and results in estimated values for key economic variables in the future. An economist applies the techniques of econometrics in their forecasting process. Typical steps may include: Scope: Key economic variables and topics for forecast commentary are determined based on the needs of the forecast audience. Literature review: Commentary from sources with summary-level perspective, such as the IMF, OECD, U.S. Federal Reserve, and CBO helps with identifying key economic trends, issues and risks. Such commentary can also help the forecaster with their own assumptions while also giving them other forecasts to compare against. Obtain data inputs: Historical data is gathered on key economic variables. This data is contained in print as well as electronic sources such as the FRED database or Eurostat, which allow users to query historical values for variables of interest. Determine historical relationships: Historical data is used to determine the relationships between one or more independent variables and the dependent variable under study, often by using regression analysis. Model: Historical data inputs and assumptions are used to develop an econometric model. Models typically apply a computation to a series of inputs to generate an economic forecast for one or more variables. Report: The outputs of the model are included in reports that typically include information graphics and commentary to help the reader understand the forecast. Forecasters may use computational general equilibrium models or dynamic stochastic general equilibrium models. The latter are often used by central banks. Methods of forecasting include Econometric models, Economic base analysis, Shift-share analysis, Input-output model and the Grinold and Kroner Model. See also Land use forecasting, Reference class forecasting, Transportation planning, Calculating Demand Forecast Accuracy and Consensus forecasts The World Bank provides a means for individuals and organizations to run their own simulations and forecasts using its iSimulate platform. https://en.wikipedia.org/wiki/Economic_forecasting