This paper measures the effect on the federal funds rate of an open-market operation. The paper deals with simultaneous-equations bias by developing a proxy for the errors the Federal Reserve makes in forecasting the extent to which Treasury operations will add or drain reserves available to private banks. These errors induce fluctuations in bank reserves which have measurable consequences for the federal funds rate. The paper estimates that a reduction in nonborrowed reserves of $30 million, if sustained for an entire 14-day reserve maintenance period, will cause the federal funds rate to rise by 10 basis points.

Futures prices were well above spot prices for most commodities during most of the Great Depression; evidently the spectacular declines in agricultural prices caught many people by surprise. Based on the historical correlations between commodity prices and consumer prices, commodity markets anticipated stable consumer prices during the first year of the Great Depression. The dramatic drop in nominal Treasury-bill yields thus should be read as a drop in ex ante real rates. Later in the Great Depression, markets anticipated deflation, but not as severe as actually occurred.

This paper reports overwhelming evidence against the hypothesis that the federal funds rate follows a martingale over the 2-week reserve maintenance period, establishing that banks do not regard reserves held on different days of the week to be perfect substitutes. A theoretical model of the federal funds market is proposed that could account for these empirical regularities as the result of line limits, transaction costs, and weekend accounting conventions. The paper concludes that such transaction costs lie at the heart of the liquidity effect that enables the Federal Reserve to change the interest rate on a daily basis.

This paper investigates a general equilibrium model of unemployment and the business cycle in which specialization of labor plays a key role. A rational expectations equilibrium with fully flexible wages and prices can exhibit unemployment in which the marginal product of employed workers exceeds the reservation wage of those who are without jobs. Workers are unemployed either because they are in the process of relocating for a better job or because they are waiting for conditions in the depressed sector to improve. Moreover, seemingly small disruptions in the supplies of primary commodities such as energy could be the source of fluctuations in aggregate employment and can exert surprisingly large effects on real output.

All but one of the U.S. recessions since World War II have been preceded, typically with a lag of around three-fourths of a year, by a dramatic increase in the price of crude petroleum. This does not mean that oil shocks caused these recessions. Evidence is presented, however, that even over the period 1948-72 this correlation is statistically significant and nonspurious, supporting the proposition that oil shocks were a contributing factor in at least some of the U.S. recessions prior to 1972. By extension, energy price increases may account for much of post-OPEC macroeconomic performance.

A consensus has recently emerged that variables beyond the level, slope, and curvature of the yield curve can help predict bond returns. This paper shows that the statistical tests underlying this evidence are subject to serious small-sample distortions. We propose more robust tests, including a novel bootstrap procedure specifically designed to test the spanning hypothesis. We revisit the analysis in six published studies and find that the evidence against the spanning hypothesis is much weaker than it originally appeared. Our results pose a serious challenge to the prevailing consensus.

The value of the dollar appears to move in one direction for long periods of time. We develop a new statistical model of exchange rate dynamics as a sequence of stochastic, segmented time trends. We reject the null hypothesis that exchange rates follow a random walk in favor of our model of long swings. Our model also generates better forecasts than a random walk. The specification is a natural framework for assessing the importance of the "peso problem" for the dollar. We nonetheless reject uncovered interest parity.

This paper proposes a new framework for determining whether a given relationship is nonlinear, what the nonlinearity looks like, and whether it is adequately described by a particular parametric model. The paper studies a regression or forecasting model of the form yt=μ(xt)+εt where the functional form of μ(⋅) is unknown. We propose viewing μ(⋅) itself as the outcome of a random process. The paper introduces a new stationary random field m(⋅) that generalizes finite-differenced Brownian motion to a vector field and whose realizations could represent a broad class of possible forms for μ(⋅). We view the parameters that characterize the relation between a given realization of m(⋅) and the particular value of μ(⋅) for a given sample as population parameters to be estimated by maximum likelihood or Bayesian methods. We show that the resulting inference about the functional relation also yields consistent estimates for a broad class of deterministic functions μ(⋅). The paper further develops a new test of the null hypothesis of linearity based on the Lagrange multiplier principle and small-sample confidence intervals based on numerical Bayesian methods. An empirical application suggests that properly accounting for the nonlinearity of the inflation-unemployment trade-off may explain the previously reported uneven empirical success of the Phillips Curve.