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Webb, Roy H.. "Two Approaches to Macroeconomic Forecasting.(Statistical Data Included)." Economic Quarterly. Federal Reserve Bank of Richmond. 1999. HighBeam Research. 21 Apr. 2018 <https://www.highbeam.com>.
Webb, Roy H.. "Two Approaches to Macroeconomic Forecasting.(Statistical Data Included)." Economic Quarterly. 1999. HighBeam Research. (April 21, 2018). https://www.highbeam.com/doc/1G1-58499948.html
Webb, Roy H.. "Two Approaches to Macroeconomic Forecasting.(Statistical Data Included)." Economic Quarterly. Federal Reserve Bank of Richmond. 1999. Retrieved April 21, 2018 from HighBeam Research: https://www.highbeam.com/doc/1G1-58499948.html
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Following 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.
This article first describes in more detail the traditional and VAR approaches to forecasting. It then examines why both forecasting methods continue to be used. Briefly, each approach has its own strengths and weaknesses, and even the best practice forecast is inevitably less precise than consumers would like. This acknowledged imprecision of forecasts can be frustrating, since forecasts are necessary for making decisions, and the alternative to a formal forecast is an informal one that is subject to unexamined pitfalls and is thus more likely to prove inaccurate.
1. TRADITIONAL LARGE MACROECONOMIC MODELS
These models are often referred to as Keynesian since their basic design takes as given the idea that prices fail to clear markets, at least in the short run. In accord with that general principle, their exact specification can be thought of as an elaboration of the textbook IS-LM model augmented with a Phillips curve. A simple version of an empirical Keynesian model is given below:
[C.sub.t] = [[alpha].sub.1] + [[beta].sub.11]([Y.sub.t] - [T.sub.t] + [[epsilon].sub.1,t] (1)
[I.sub.t] = [[alpha].sub.2] + [[beta].sub.21]([R.sub.t] - [[[pi].sup.e].sub.t+1]) + [[epsilon].sub.2,t] (2)
[M.sub.t] = [[alpha].sub.3] + [[beta].sub.31][Y.sub.t] + [[beta].sub.32][R.sub.t] + [[epsilon].sub.3,t] (3)
[[pi].sub.t] = [[alpha].sub.4] + [[beta].sub.41] [Y.sub.t]/[[Y.sup.p].sub.t] + [[epsilon].sub.4,t] (4)
[[[pi].sup.e].sub.t+1] = [[theta].sub.51][[pi].sub.t] + [[theta].sub.52][[pi].sub.t-1] (5)
Y [equivalent to] [C.sub.t] + [I.sub.t] + [G.sub.t]. (6)
Equation (1) is the consumption function, in which real consumer spending C depends on real disposable income Y - T. In equation (2), business investment spending I is determined by the real interest rate R - [[pi].sup.e]. Equation (3) represents real money demand M, which is determined by real GDP Y and the nominal interest rate [R.sub.t] [1] In equation (4), inflation is determined by GDP relative to potential GDP ) [Y.sup.p]; in this simple model, this equation plays the role of the Phillips curve. [2] And in equation (5), expected inflation [[pi].sup.e] during the next period is assumed to be a simple weighted average of current inflation and the previous period's inflation. Equation (6) is the identity that defines real GDP as the sum of consumer spending, investment spending, and government spending G. In the stochastic equations, [epsilon] is an error term and [alpha] and [beta] are coefficients that can be estimated from macro data, usually by ordinary least squares regressions. The [theta] coefficients in equation (5) are assumed rather than estimated. [3]
One can easily imagine more elaborate versions of this model. Each major aggregate can be divided several times. Thus consumption could be divided into spending on durables, nondurables, and services, and spending on durables could be further divided into purchases of autos, home appliances, and other items. Also, in large models there would be equations that describe areas omitted from the simple model above, such as imports, exports, labor demand, and wages. None of these additions changes the basic character of the Keynesian model.
To use the model for forecasting, one must first estimate the model's coefficients, usually by ordinary least squares. In practice, estimating the model as written would not produce satisfactory results. This could be seen in several ways, such as low [R.sup.2] statistics for several equations, indicating that the model fits the data poorly. There is an easy way to raise the statistics describing the model's fit, however. Most macroeconomic data series in the United States are strongly serially correlated, so simply including one or more lags of the dependent variable in each equation will substantially boost the reported [R.sup.2] values. For example, estimating equation (2) above from 1983Q1 through 1998Q4 yields an [R.sup.2] of 0.02, but adding the lagged dependent variable raised it to 0.97. What has happened is that investment has grown with the size of the economy. The inclusion of any variable with an upward trend will raise the reported [R.sup.2] statistic. The lagged dependent variable is a convenie nt example of a variable with an upward trend, but many other variables could serve equally well. This example illustrates that simply looking at the statistical fit of an equation may not be informative, and economists now understand that other means are necessary to evaluate an empirical equation or model. At the time the Keynesian models were being developed, however, this point was often not appreciated.
Once the model's coefficients have been estimated, a forecaster would need future time paths for the model's exogenous variables. In this case the exogenous variables are those determined by government policy-G, T, and M-and potential GDP, which is determined outside the model by technology. And although the money supply is ultimately determined by monetary policy, the Federal Reserve's policy actions immediately affect the federal funds rate. Thus rather than specifying a time path for the money supply, analysts would estimate the money demand equation and then rearrange the terms in order to put the interest rate on the left side. The future time path for short-term interest rates then became a key input into the forecasting process, although its source was rarely well documented. …
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