Discussions of the goals of monetary policy generally focus on the benefits of price and output stabilization. After formulating a loss function that weights these two objectives, the next step is to examine different policy programs and operating procedures in order to achieve the desired outcomes. But these discussions take for granted our ability to measure the objects of interest, namely, aggregate price inflation and the level of output. Unfortunately, the measurement of aggregate inflation as a monetary phenomenon is difficult, as nonmonetary events, such as sector-specific shocks and measurement errors, can temporarily produce noise in the price data that substantially affects the aggregate price indices at higher frequencies. During periods of poor weather, for example, food prices may rise to reflect decreased supply, thereby producing transitory increases in the aggregate index. Because these price changes do not constitute underlying monetary inflation, the monetary authorities should avoid basing their decisions on them. Solutions to the problem of high-frequency noise in the price data include calculating low-frequency trends over which this noise is reduced. But from a policymaker's perspective, this greatly reduces the timeliness, and therefore

From this evidence, we conclude that the degree of serial correlation in the data could plausibly have been generated by our model.

We study the dynamics of price indices for major U.S. cities using panel econometric methods and find that relative price levels among cities mean revert at an exceptionally slow rate. In a panel of 19 cities from 1918 to 1995, we estimate the half-life of convergence to be approximately nine years. The surprisingly slow rate of convergence can be explained by a combination of the presence of transportation costs, differential speeds of adjustment to small and large shocks, and the inclusion of nontraded goods prices in the overall price index.

ALTHOUGH MOST ECONOMISTS agree that inflation is costly, there is no consensus about why. Many traditionally cited costs, such as deadweight loss from the inflation "tax," seem too small to justify concern about moderate inflation. One approach is to argue that inflation of 10 percent or 15 percent would not be particularly costly if it were constant and fully anticipated, but that a rise in the level of inflation raises uncertainty about future inflation. In the absence of perfect indexation, such uncertainty has significant costs, including arbitrary redistributions, relative price variation, and fewer long-term contracts, such as loans to finance investment. I This view implies that understanding the costs of inflation requires that we understand the connection between the level of inflation and uncertainty. The idea that high inflation leads to greater uncertainty is suggested in Arthur Okun's "The Mirage of Steady Inflation" and in Milton Friedman's Nobel lecture, and many economists treat it as a stylized fact.2 But empirical studies of the inflation-uncertainty relation report conflicting results, and the issue appears unsettled. We are grateful for suggestions from , members of the Brookings Panel, and seminar participants at Princeton University. Cecchetti acknowledges financial support from the National Science Foundation. 1. For discussions of the costs of inflation uncertainty, see Jaffee and Kleiman (1977) and Fischer and Modigliani (1978). 2. Okun (1971); M. Friedman (1977). 215 3. Fischer(1981).To review alternative explanations for a relation between inflation and uncertainty, we focus on the following question. Consider two moderateinflation economies-either different countries or the same country during different periods-with different trend rates of inflation, one high and one low. Is uncertainty about inflation-the variance of errors in optimal forecasts-higher in the economy with the higher trend?We assume that trend inflation is determined by trend money growth, and that inflation varies around its trend because of monetary and other demand and supply shocks. In this framework, there are two reasons for inflation uncertainty to be high when the trend is high. First, inflation might vary more around its trend when the trend is high. Second, a high trend might imply that the trend itself is less stable. These explanations have different implications for the horizon over which inflation raises uncertainty. We discuss the two explanations in turn.Why might inflation vary more around its trend when the trend is high? The answer is not obvious, but several authors present models with this property. In some models, an exogenous rise in trend inflation causes greater variability. Joel Hasbrouck, for example, argues that individuals adjust their cash balances more frequently at high inflation. The implication is that money demand responds more quickly to shocks, which causes inflation to vary more. Ball, Gregory Mankiw, and David Romer argue that high trend inflation reduces nominal price rigidity an...

Aggregate shocks that move output and inflation in opposite directions create a tradeoff between output and inflation variability, forcing central bankers to make a choice. Differences in the degree of accommodation of shocks lead to disparate variability outcomes, revealing national central banker's relative weight on output and inflation variability in their preferences. We use estimates of the structure of 23 industrialized and developing economies, including nine that target inflation explicitly, together with the realized output and inflation patterns in those countries, to infer the degree of policymakers' inflation variability aversion. Our results suggest that both countries that introduced inflation targeting, and nontargeting European Union countries approaching monetary union, increased their revealed aversion to inflation variability, and likely suffered most increases in output volatility as a result.

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We study a Lucas asset-pricing model that is standard in all respects, except that the representative agent's subjective beliefs about endowment growth are distorted. Using constant relative risk-aversion (CRRA) utility, with a CRRA coefficient below 10; fluctuating beliefs that exhibit, on average, excessive pessimism over expansions; and excessive optimism over contractions (both ending more quickly than the data suggest), our model is able to match the first and second moments of the equity premium and risk-free rate, as well as he persistence and predictability of excess returns found in the data.

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. The MIT Press is collaborating with JSTOR to digitize, preserve and extend access to The Review of Economics and Statistics.Abstract-Standard approaches to designing a futures hedge often suffer from two major problems. First, they focus only on minimizing risk, so no account is taken of the impact on expected return. Second, in estimating the hedge ratio, no allowance is made for time variation in the distribution of cash and futures price changes. This paper describes a technique for estimating the optimal futures hedge that corrects these problems, and illustrates its use in hedging Treasury bonds with T-bond futures.