while during the 1970s, to astructural vector autoregression models based on a handful of lagged variables that are still employed today. to be sure, the large econometric models have been refined, incorporating the fruits of later theoretical developments, including perhaps most importantly the insight that monetary policy could not permanently influence the level of the unemployment rate. moreover, a larger role was given to a range of financial and expectational variables that earlier practitioners steeped in orthodox keynesian income determination tended to downgrade. liquidity preference functions, to be sure, were included even in the early versions of these large - scale models as necessary building blocks for determining the equilibrium level of interest rates and income. but, overall, financial sector modeling was primitive. indeed, only modest progress has been made in this area since the federal reserve began to produce our own flow of funds accounts in 1955. a further, and perhaps more profound, challenge to the underlying validity of this style of modeling is the possibility that, whereas standard models of the real economy determine a unique level of income, the financial system appears to be capable of reaching myriad equilibria. in addition, the fundamental forces that determine which of these equilibria will be selected may themselves be inherently unpredictable. we have built large - scale models of the united states and global economies at the federal reserve. while recognizing their limitations, we do find them useful in research and analysis. but the experience of the last 40 years underscores a fundamental dilemma of business economics. should we endeavor to continue to refine our techniques of deriving maximum information from an existing body of data? or should we find ways to augment our data library to gain better insight into how our economy is functioning? obviously, we should do both, but i suspect greater payoffs will come from more data than from more technique. certainly, statistical systems in the united states, both public and private, are world class and, indeed, in many respects set the world standard. but given the rapidly changing economic structure, one could readily argue that more statistical resources need to be applied to understanding the complexities of the newer technologies that confront analysts. these newer technologies and the structure of output they have created have surfaced a set of definitional problems that - although evident in a world of steel, fabrics, and grains - were never on the cutting edge of analysis. i refer, of course, to the age - old problem of defining what we mean by a unit of output and

thus the staff is projecting how the economy would evolve if there were no change in the federal funds rate ( which does not even always translate cleanly into no change in monetary policy ). the rationale for this procedure is to separate the forecast process from the policy - making process, and therefore avoid appearing to prejudge the fomc ’ s decisions. this procedure may be modified when there is a strong presumption that conditions will unambiguously call for significant action if the committee is to achieve its objectives. but it does, nevertheless, make the forecast process at the board fundamentally different from that in the private sector where one of the key decisions in the forecast is the direction of monetary policy. it is ironic that, at the board, where the staff is presumably more knowledgeable about the direction of policy than in the private sector, forecasting is constrained from using that information in developing the forecast. on the other hand, the practice at the board may be very well suited to the process of making policy by forcing the fomc to confront the implications of maintaining an unchanged path for the funds rate. a second difference relative to my experience in the private sector has to do with the way in which judgment and model interact in the development of the forecast. my first impression of the process at the board was that the judgmental team made its forecast without a model and the model team made its forecast without judgment, leaving the blending of model and judgment to be worked out in the process of discussion and iteration as the judgmental group looks at the model output and the model group joins the discussion of the forecast. the process is, i have come to appreciate, more complicated and subtle than this caricature. for example, when there have been important shocks ( e. g., unexpected rise in oil prices or an increase in the minimum wage ), model simulations of the effect of the shocks will provide a point of departure for the initial judgmental forecast. but it is, nevertheless, a different way of combining model and judgment than we used at lhm & a where the model played a more central role in the forecast process. an advantage of the board ’ s approach is that it makes the forecast less dependent on a single model ( perhaps desirable given the diversity of views on the fomc ) and forces recognition of uncertainties in the outlook when alternative sector models yield very different forecasts. 2. policy alternatives and alternative scenarios to support fomc policy decisions a second valuable contribution