ECONOMIC & CONSUMER CREDIT ANALYTICS April 2015 U.S. Macro Model Methodology Prepared by Abstract Mark Zandi [email protected] The Moody’s Analytics economic, financial and demographic projections for the Chief Economist U.S. are produced each month using a large-scale econometric model. This article Scott Hoyt describes the specification of the U.S. national model. [email protected] Senior Director Contact Us Email [email protected] U.S./Canada +1.866.275.3266 Europe +44.20.7772.5454 Asia/Pacific +85.2.3551.3077 All Others +1.610.235.5299 Web www.economy.com ANALYSIS �� U.S. Macro Model Methodology In the broadest sense, aggregate econom- » Nominal interest rates are determined with empirical evidence, lies the tradi- ic activity is determined by the intersection both by monetary policy and by pri- tional approach of building large-scale, of the economy’s aggregate demand and vate demand for credit, both of which multi-equation structural models of supply functions. In the short run, fluctua- are influenced by GDP; the economy. tions in economic activity are primarily de- » Inflation is determined by firm The Moody’s Analytics U.S. Macro Model termined by shifts in aggregate demand. The price-setting choices, which depend relies most on the third approach: specify- level of resources and technology available on the level of real activity and ing, estimating, and then solving simultane- for production is taken as given. Prices and inflation expectations. ously a large set of equations that mirror the wages adjust slowly to equate aggregate de- Mathematically, this describes a system structural workings of the U.S. economy. On mand with the level of activity the economy of three equations that can be solved for the occasion, however, this approach is comple- can potentially supply. three unknowns—real GDP, nominal interest mented by alternative modeling approaches. In the longer term, changes in aggregate rates, and inflation—conditional on given ex- Just as there is no best tool in a carpenter’s supply determine the economy’s growth pectations of future income and inflation. toolbox, there is no best model to employ potential. The two principal determinants of The classical long-run equilibrium is in forecasting: Each approach has its own long-run economic growth are the rate of achieved at the point where expectations are strengths and weaknesses, and whether or expansion of labor and capital, and changes consistent with reality; when this occurs, the not it is appropriate to use depends on the in technology, which allow those inputs to level of real output, interest rates and inflation task at hand. Understanding why and when be transformed into economic output more remain stable at equilibrium values governed one modeling approach may be favored over efficiently. The U.S. Macro Model is specified entirely by the supply side of the economy. another requires an understanding of the to reflect the interaction between aggregate In the short run, however, a shock to any trade-offs inherent in each approach. demand and supply. part of this system can cause spending and The model contains more than 1,800 inflation to depart from expectations; this, Weighing the trade-offs variables, including unpublished intermedi- accordingly, causes departures in current The vector autoregression, or VAR, model ate variables, and is designed to produce growth, interest and inflation rates from is the most common example of the first forecasts that run 30 years. In addition to their long-run equilibrium values, giving rise pure time-series approach to macroeco- producing good cyclical near-term fore- to the business cycle. nomic forecasting. A VAR forecast is obtained casts and stable long-run equilibrium, the through a simple projection of future values forecast is designed to allow for scenario Theory vs. data on past information. Unlike in a structural construction. Moody’s Analytics produces a The modern consensus view does not ex- model, where theoretical reasoning would number of alternative scenarios each month, tend to a consensus in econometric practice. determine how the relationships between scenarios provided by regulators for bank This is because a fundamental difficulty pre- GDP, interest rates and inflation rates are stress-testing purposes, and clients produce vents a direct application of the consensus specified, in a VAR these three variables many more. story to the data: Expectations are central, would simply be regressed on their own and these are difficult to quantify and to lagged values and those of the other vari- Theory in brief forecast. As a result, there is not one, but ables, with no attempt to impose or infer any The macroeconomics profession con- three, distinct approaches to modeling the type of causal explanation for empirically ob- tinues to enjoy spirited methodological de- macroeconomy, all in common use today: served correlations among the variables. bates, but over the last few decades, heated » At one end of the spectrum are pure This lack of theoretical motivation is both arguments over the most appropriate way to time-series methods that require few, the strength and weakness of the VAR. By model the economy have evolved toward a if any, assumptions from economic emphasizing a close fit of historical relation- consensus view best described as “Keynes- theory. These methods rely on highly ships in the data over a priori reasoning, ian in the short run, and classical in the long flexible, reduced form specifications VARs are relatively immune to criticisms of run.” that “let the data speak.” “misspecification” from incorrect theory. In this view, the state of the economy is » On the opposite end is a set of models VARs also tend to produce very accurate determined through the simultaneous rela- that are built up from strict founda- forecasts over short sample periods, as well tionship between three key variables: GDP tions in microeconomic theory and as predict the dynamic responses of multiple growth, price inflation and interest rates. draw insights by imposing strict variables in response to a common shock. Specifically: assumptions of economic theory This method suffers from three limita- » GDP depends on aggregate spending, upon the data rather trying to “fit” tions, however. First, the forecasts are dif- which in turn depends on the expected that data. ficult to explain intuitively; the lack of theory real rate of interest, or the nominal » In the middle of these extremes, and large number of regressors make the interest rate less future inflation; balancing theoretical assumptions model largely a black box. Second, the high MOODY’S ANALYTICS / Copyright© 2015 1 ANALYSIS �� U.S. Macro Model Methodology degree of parameterization in a VAR reduces curve relationship determining aggregate rely more heavily on exogenous forecasts the efficiency of the resulting parameter es- supply. These textbook equations are made and assumptions introduced from outside timates, and it limits the number of variables operational as forecasting tools by econo- the model. Examples include demographic that can be forecast practically. A typical metrically estimating the parameters in the projections, assumptions regarding the pace VAR incorporates only a few endogenous theoretical relationship to find the right “fit” of technological change, fiscal and monetary variables, providing a very limited view of in the observed data. policy action, and global oil prices. These as- the economy compared with the many hun- By taking a middle ground between theo- sumptions allow forecasters to incorporate dreds of endogenous variables forecast in the ry and data, this approach attains neither the information that is known, but not internal Moody’s Analytics U.S. Macro Model. Third, theoretical elegance of the DSGE approach to the model, far more easily than in VARs prioritizing experience over theory makes or the empirical flexibility of a VAR. At the and DSGEs. VARs less capable of incorporating possibili- same time, however, it manages to avoid ties outside the scope of experience (for ex- the shortcomings of either one; imposing Selecting the right tool ample, so-called black swan events). theory to restrict the flexibility of econo- Macroeconomic models are built to serve The two most common examples of the metric specifications allows more efficient three basic functions: producing useful fore- second, “microfoundations” approach in- estimation and greater explanatory power casts, calculating counterfactuals to answer clude deterministic real business cycle mod- than a VAR can achieve. However, structural hypothetical (“what if”) questions, and pro- els and, more recently, dynamic stochastic macroeconomic models do not require some viding a transparent understanding of the general equilibrium models. In these, model of the extreme and somewhat unrealistic current and future state of the economy. equations are derived from equilibrium assumptions that render DSGEs susceptible Each of the three approaches to mac- expressions that relate observed aggregate to misspecification. roeconomic modeling detailed above have outcomes to the solutions to the multiple in- Nevertheless, the greatest advantage distinct strengths and weaknesses, which ter-temporal dynamic optimization problems of these models is the great detail they can make each more appropriate for some tasks of individual consumers and firms. These provide. Though VARs and DSGEs can in- and less for others. Unaided by human input, models are theoretically elegant,