Rather, the Federal Reserve, together with all modern central banks, has found that the successful conduct of monetary policy requires painstaking examination of a broad range of economic and financial data, careful consideration of the implications of those data for the likely path of the economy and inflation, and prudent judgment regarding the effects of alternative courses of policy action on prospects for achieving our macroeconomic objectives. In that process, economic models can provide valuable guidance to policymakers, and over the years substantial progress has been made in developing formal models and forecasting techniques. But any model is by necessity a simplification of the real world, and sufficient data are seldom available to measure even the basic relationships with precision. Monetary policymakers must therefore strike a difficult balance--conducting rigorous analysis informed by sound economic theory and empirical methods while keeping an open mind about the many factors, including myriad global influences, at play in a dynamic modern economy like that of the United States.  Amid significant uncertainty, we must formulate a view of the most likely course of the economy under a given policy approach while giving due weight to the potential risks and associated costs to the economy should those judgments turn out to be wrong.

Given our inevitably incomplete knowledge about key structural aspects of an ever-changing economy and the sometimes asymmetric costs or benefits of particular outcomes, the paradigm on which we have settled has come to involve, at its core, crucial elements of risk management. In this approach, a central bank needs to consider not only the most likely future path for the economy but also the distribution of possible outcomes about that path. The decisionmakers then need to reach a judgment about the probabilities, costs, and benefits of various possible outcomes under alternative choices for policy.

The risk-management approach has gained greater traction as a consequence of the step-up in globalization and the technological changes of the 1990s, which found us adjusting to events without the comfort of relevant history to guide us. Forecasts of change in the global economic structure--for that is what we are now required to construct--can usefully be described only in probabalistic terms. In other words, point forecasts need to be supplemented by a clear understanding of the nature and magnitude of the risks that surround them.

Operationally, the risk-management approach differs from the forecast-based policies described in much of the monetary economics literature in only one important respect. For simplicity, researchers have generally analyzed forecast-based policies under the assumption that policymakers care only about average economic outcomes. However, in practice, policymakers are often concerned not only with the average or most likely outcomes but also with the risks to their objectives posed by relatively low-probability events.

Under the rubric of risk management are a number of specific issues that we at the Fed had to address over the past decade and a half and that will likely resurface to confront future monetary policymakers.

Most prominent is the appropriate role of asset prices in policy. In addition to the narrower issue of product price stability, asset prices will remain high on the research agenda of central banks for years to come. As the ratios of gross liabilities and gross assets to GDP continue to rise, owing to expanding domestic and international financial intermediation, the visibility of asset prices relative to product prices will itself rise. There is little dispute that the prices of stocks, bonds, homes, real estate, and exchange rates affect GDP. But most central banks have chosen, at least to date, not to view asset prices as targets of policy, but as economic variables to be considered through the prism of the policy's ultimate objective.

As I indicated earlier, policy has worked off a risk-management paradigm in which the risk and cost-benefit analyses depend on forecasts of probabilities developed from large macromodels, numerous submodels, and judgments based on less mathematically precise regimens. Such judgments, by their nature, are based on bits and pieces of history that cannot formally be associated with an analysis of variance.

The second lesson that I would draw from our experience in combating inflation over the past 30 years is: you cannot rely on any single indicator to guide monetary policy. Just as portfolio diversification is the hallmark of a prudent investment strategy, central bankers need to look at a variety of indicators of economic activity and inflationary pressures..

Over the past three decades, we have seen a number of examples of once-reliable indicators whose performance has been affected by the changing structure of financial markets and the economy. One example is the monetary aggregates. Prior to the mid-1970s, the relationships between inflation and the two primary measures of money—MI and M2—were generally stable. The M1 relationship began to break down in the mid-1970s, and after several attempts to redefine M1 to fix the relationship, in 1987 the Federal Reserve de-emphasized MI in favor of M2. M2’s relationship with inflation lasted for a few more years, but eventually deteriorated also, leading us in 1993 to drop M2 as a formal indicator.

...in discussions of the monetary aggregates, I believe that the key issue is not whether excessive money growth causes inflation. Rather, the key issue is which measure of money is most useful in gauging inflationary pressures. Our experience in recent years confirms the difficulty of finding a single and consistently reliable measure of money in a rapidly changing financial and economic environment.