Rational Expectations and Economic Policy (National Bureau of Economic Research Conference Report)

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A related implication is that, if inflation expectations are well anchored, changes in energy and food prices should have relatively little influence on "core" inflation, that is, inflation excluding the prices of food and energy. Although inflation expectations seem much better anchored today than they were a few decades ago, they appear to remain imperfectly anchored. A number of studies confirm that observation.

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Levin, Natalucci, and Piger have shown that some survey measures of inflation expectations in the United States respond to recent changes in the actual rate of inflation, which would not be the case if expectations were perfectly anchored. Models of the term structure of interest rates better fit the data under the assumption that both inflation expectations and beliefs about the central bank's reaction function are evolving Kozicki and Tinsley, ; Rudebusch and Wu, ; Cogley, An indirect but elegant way to make the point that inflation expectations remain imperfectly anchored comes from a statistical analysis of inflation by Stock and Watson Stock and Watson model inflation as having two components, which may be interpreted as the trend and the cycle.

Changes in the trend component are highly persistent whereas shocks to the cyclical component are temporary. That is, unexpected changes in inflation are today much more likely to be transitory than they were before the early s. Because it seems quite unlikely that changes in inflation could persist indefinitely unless long-run expectations of inflation also changed, I interpret the Stock-Watson finding as consistent with the view that inflation expectations have become much more anchored since the early s.

At the same time, that the variability of the trend component of inflation, though modest, remains positive, implies that long-run expectations of inflation are not perfectly anchored today. The policy implications of the much-improved but still imperfect anchoring of inflation expectations are not at all straightforward. To evaluate these implications, we must understand better the historical variation in inflation expectations, the effect of this variation on actual inflation and economic activity, and the relationship between policy actions and the formation of inflation expectations.

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With the hope of promoting progress on these broad topics, I pose three questions to researchers, the answer to any of which would be quite useful for practical policymaking. First, how should the central bank best monitor the public's inflation expectations? Theoretical treatments tend to neglect the fact that in practice many measures of inflation expectations exist, including the forecasts of professional economists, results from surveys of consumers, information extracted from financial markets such as the market for inflation-indexed debt, and limited information on firms' pricing plans.

In a very interesting paper, Mankiw, Reis, and Wolfers compared the available measures, emphasizing in particular that median measures of inflation expectations often obscure substantial cross-sectional dispersion of expectations. Do we need new measures of expectations or new surveys?

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Information on the price expectations of businesses--who are, after all, the price setters in the first instance--as well as information on nominal wage expectations is particularly scarce. Second, how do changes in various measures of inflation expectations feed through to actual pricing behavior? Promising recent research has looked at price changes at very disaggregated levels for insight into the pricing decision Bils and Klenow, ; Nakamura and Steinsson, But this research has not yet linked pricing decisions at the microeconomic level to inflation expectations; undertaking that next step would no doubt be difficult but also very valuable.

Third, what factors affect the level of inflation expectations and the degree to which they are anchored? Answering this question essentially involves estimating the learning rule followed by the public or various components of the public, although one could consider alternative frameworks like Carroll's epidemiological model of the propagation of information among private agents. A fuller understanding of the public's learning rules would improve the central bank's capacity to assess its own credibility, to evaluate the implications of its policy decisions and communications strategy, and perhaps to forecast inflation.

Realistically calibrated models with learning would also inform our thinking about policy and the economy. Inflation Forecasting at the Federal Reserve I would like to shift gears at this point to tell you a bit about how the Federal Reserve Board staff goes about forecasting inflation. Obviously, this activity provides critical inputs into the making of monetary policy, and as I will discuss, the staff's long-term track record in forecasting inflation is quite good by any reasonable benchmark. I hope that my brief description will stimulate your interest in the complex and challenging problems of real-time macroeconomic forecasting.

But, as you will see, the discussion of practical inflation forecasting will bring us back to one theme of my remarks--that our ability to forecast inflation and predict how inflation will respond to policy actions depends very much on our capacity to measure and to understand what determines the public's expectations of inflation.

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The Board staff employs a variety of formal models, both structural and purely statistical, in its forecasting efforts. However, the forecasts of inflation and of other key macroeconomic variables that are provided to the Federal Open Market Committee are developed through an eclectic process that combines model-based projections, anecdotal and other "extra-model" information, and professional judgment.

In short, for all the advances that have been made in modeling and statistical analysis, practical forecasting continues to involve art as well as science. The forecasting procedures used depend importantly on the forecast horizon. For near-term inflation forecasting--say, for the current quarter and the next--the staff relies most heavily on a disaggregated, bottom-up approach that focuses on estimating and forecasting price behavior for the various categories of goods and services that make up the aggregate price index in question. For example, we know from historical experience that the prices of some types of goods and services tend to be quite volatile, including not only as is well known the prices of energy and some types of food but also some "core" prices such as airfares, apparel prices, and hotel rates.

The monthly autocorrelations of price changes in these categories tend to be low or even negative. In contrast, changes in inflation rates in some services categories, such as shelter costs, tend to be more persistent. In assessing what price changes in a particular category imply for future price changes in that category, the staff uses not only various forms of time-series analysis but also specialized knowledge about how the various indexes are constructed--for example, whether certain categories are sampled every month in all localities and how seasonal adjustments are performed.

In making very near-term price forecasts, the staff also uses diverse information from a variety of sources, such as surveys of prices of gasoline and other important items, news reports about price-change announcements, and anecdotal information from our business contacts. Conceptually, one might think of this effort to distinguish transitory from persistent price changes as a more nuanced way of estimating the underlying inflation trend, analogous to the trend measures provided by more mechanical indicators such as trimmed-mean or weighted-median inflation rates.

An accurate forecast of very near-term inflation is important not only for its own sake but also because it provides a better "jumping-off point" for the longer-term forecast. Because inflation continues to exhibit some inertia, improved near-term forecasts translate into more-accurate longer-term projections as well. For forecasting horizons beyond a quarter or two, detailed analyses of individual price components become less useful, and thus the staff's emphasis shifts to inflation's fundamental determinants.

Food and energy inflation are forecasted separately from the core, using information from futures prices and other sources. However, forecasts of core inflation must take into account the extent to which food and energy costs are passed through to other prices. To project core inflation at longer-term horizons, the staff consults a range of econometric models.

Most of the models used are based on versions of the new Keynesian Phillips curve, which links inflation to inflation expectations, the extent of economic slack, and indicators of supply shocks. Despite the common conceptual framework, the model specifications employed differ considerably in their details, including how lagged inflation enters the equation, how resource utilization is measured, and whether a survey-based measure of inflation expectations is included.

In principle, formal econometric tests could determine how much weight should be put on the forecast of each model, but in practice the data do not permit sharp inferences; moreover, estimated forecasting equations may not reflect information about special factors affecting the outlook.

Because of these considerations, as I have already noted, the staff's inflation forecasts inevitably reflect a substantial degree of expert judgment and the use of information not captured by the models. Another reason for the reliance on judgment in the forecasting process is the practical requirement that the forecast for inflation be consistent with the staff's overall view of the economy, including the forecasts for key economic variables such as wages, interest rates, and consumption spending.

Achieving this consistency requires a thoughtful understanding of why sectoral forecasts may be at odds and how best to reconcile those differences. Again, in principle, consistency of sectoral forecasts could be ensured by estimating the inflation equation as part of a general equilibrium system. Indeed, considerable progress has been made in recent years, at the Board and elsewhere, in developing dynamic stochastic general equilibrium DSGE models detailed enough for policy application.

These models have become increasingly useful for policy analysis and for the simulation of alternative scenarios. They are likely to play a more significant role in the forecasting process over time as well, though, like other formal methods, they are unlikely to displace expert judgment. A potential drawback of the simple Phillips curve model for analyzing and forecasting inflation is that it does not explicitly incorporate the possible influence of labor costs on the inflation process.

Interestingly, however, the system approach does not seem to forecast price inflation as well as single-equation Phillips curve models do. This weaker performance appears to reflect, at least in part, the shortcomings of the available data on labor compensation. The two principal quarterly indicators of aggregate hourly compensation are the employment cost index ECI and nonfarm compensation per hour CPH.

Both are imperfect measures of the labor costs relevant to pricing decisions.

For example, the ECI's fixed employment and occupation weights may not reflect changes in the labor market, and the ECI excludes stock options and similar forms of payment. CPH is volatile, perhaps in part because it measures stock options at exercise rather than when granted, and it is subject to substantial revisions. Moreover, these two hourly compensation measures often give contradictory signals. Despite these problems, labor market developments certainly influence how the staff and policymakers view the inflation process and inflation risks, illustrating yet another point in the forecasting process at which judgment must play an important role.

In particular, in evaluating labor-market conditions and trends in labor costs, the staff takes note of a wide range of data, anecdotes, and other qualitative information as well as the official data on compensation.

Overall, the Board staff's inflation forecasting has been remarkably good, at least compared with the available alternatives Romer and Romer, ; Sims, To cite a recent study, Faust and Wright show that real-time staff forecasts of inflation reliably outperform statistical benchmarks at all horizons and that this advantage is not solely the result of the staff's expertise at estimating near-term inflation rates. To link this discussion of forecasting to the first portion of my remarks, I turn to the treatment of inflation expectations in staff forecasts.

As I noted earlier, while inflation expectations doubtless are crucial determinants of observed inflation, measuring expectations and inferring just how they affect inflation are difficult tasks. A popular shortcut is to include lagged inflation terms in the Phillips curve equation; besides being a convenient means of capturing the inertial component in inflation, the estimated coefficients on lagged inflation almost certainly reflect to some degree the formation of inflation expectations and their influence on the inflation process.

However, using lagged inflation as a proxy for inflation expectations has drawbacks, notably its susceptibility to the Lucas critique. One question in choosing among measures of expectations is whether to focus on measures of short-term inflation expectations say, twelve months ahead or of longer-term expectations five to ten years ahead.

The staff also looks at measures derived from comparing yields on nominal and inflation-indexed Treasury securities the breakeven inflation rate. Measures of inflation compensation derived from the market for inflation-indexed securities are influenced by changes in inflation risk premiums and liquidity premiums, and analyses are constrained by the fact that these markets have been operating in the United States for only a relatively short period. Nevertheless, unlike survey measures, breakeven inflation rates are determined in a market in which investors back their views with real money.

Moreover, breakeven measures of inflation expectations provide information on the expectations of a different group of agents--financial-market participants--which can be compared with the views of economists and consumers as represented by surveys. Measurement is only one aspect of understanding inflation expectations. We also need a better understanding of how inflation expectations affect actual inflation and of the factors that determine inflation expectations.

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I will say a few words about the latter issue in the context of the practical problems of forecasting and policy analysis faced by the staff of the Federal Reserve Board. Model-based simulations of the inflation process are useful tools for both forecasting and policy analysis.

Introduction to "Rational Expectations and Economic Policy"

In conducting such simulations, the analyst must specify how inflation expectations are formed--in particular, how they react to actual changes in the economy and in policy. Essentially, this approach assumes that the public updates its inflation expectations in a sensible way based on economic developments but does not assume that the public has full knowledge of the underlying model of the economy, consistent with the structure of learning models Brayton and others, Recent staff work at the Board has analyzed the implications of expanding the set of variables allowed to influence the public's long-term inflation expectations to include, among others, the federal funds rate.

One is that the output costs of disinflation may be lower than those suggested by reduced-form-type Phillips curves. Intuitively, if the Fed attempts to disinflate by raising the federal funds rate, the disinflationary effect will be felt not only through the usual output gap channel but also through a direct restraint on long-term inflation expectations. This interpretation is consistent with some analyses of the Volcker disinflation; although the costs of that disinflation were high, they were perhaps less than economists would have predicted in advance, given conventional estimates of the sacrifice ratio Erceg and Levin, To be sure, this and similar analyses remain speculative.

A good deal more must be done before such work proves a reliable basis for policy choices. University of Chicago Press: About Contact News Giving to the Press. Capitalism Takes Command Michael Zakim. Founding Choices Douglas A. The Response to Industrialism, Samuel P. Most people would agree that stabilization policy ranks with the highest of these.

Continuing inflation and periodic serious acceleration of inflation combined with high and secularly rising unemployment combine to give the area high priority. This book brings us up to date on an extremely lively discussion involving the role of expectations, and more particularly rational expectations, in the conduct of stabilization policy. Anyone interested in the role of government in economics should read this important book. The contributors and commentators are highly distinguished and the editor has usefully collated comments and the ensuing discussion.

Unusually for a conference proceedings the book is well indexed and it is also replete with numerous and up-to-date references. This is the first serious book to examine the rational expectations thesis in any depth, and it will prove invaluable to anyone involved with macroeconomic policy generally and with monetary economics in particular.

Shaw, The Economic Journal.

Acknowledgments Introduction Stanley Fischer 1.

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