Two Approaches to Macroeconomic Forecasting

F ollowing World War II, the quantity and quality of macroeconomic data expanded dramatically. The most important factor was the regular publication of the National Income and Product Accounts, which contained hundreds of consistently defined and measured statistics that summarized overall economic activity. As the data supply expanded, entrepreneurs realized that a market existed for applying that increasingly inexpensive data to the needs of individual firms and government agencies. And as the price of computing power plummeted, it became feasible to use large statistical macroeconomic models to process the data and produce valuable services. Businesses were eager to have forecasts of aggregates like gross domestic product, and even more eager for forecasts of narrowly defined components that were especially relevant for their particular firms. Many government policymakers were also enthusiastic at the prospect of obtaining forecasts that quantified the most likely effects of policy actions. In the 1960s large Keynesian macroeconomic models seemed to be natural tools for meeting the demand for macroeconomic forecasts. Tinbergen (1939) had laid much of the statistical groundwork, and Klein (1950) built an early prototype Keynesian econometric model with 16 equations. By the end of the 1960s there were several competing models, each with hundreds of equations. A few prominent economists questioned the logical foundations of these models, however, and macroeconomic events of the 1970s intensified their concerns. At the time, some economists tried to improve the existing large macroeconomic models, but others argued for altogether different approaches. For example, Sims (1980) first criticized several important aspects of the large models and then suggested using vector autoregressive (VAR) models for macroeconomic forecasting. While many economists today use VAR models, many others continue to forecast with traditional macroeconomic models.