Seasonal fluctuations are as large as cyclical fluctuations. Monetary policy in the United States has dealt with seasonality by smoothing nominal rates of interest. The original motivation for this was that seasonality in nominal interest rates put recurring strain on the banking system. We build a model of monetary policy in the presence of seasonality that puts financial market conditions in the foreground. Our findings are as follows. Insulating nominal rates of interest or rates of inflation from seasonal variation are the basis for a formula for generating indeterminacy of equilibria and excess volatility. Preventing seasonality in the rate of monetary growth does not suffer from this problem. Moreover, under any method for conducting monetary policy, the setting of the target value of the monetary instrument will affect the degree of seasonal variability in most endogenous variables. This is a new channel by which monetary policy can have real effects. The case for eliminating seasonality in nominal rates of interest is strongest when seasonal impulses derive from shifts in money demand. It is weakest when seasonal impulses derive from the real sector.

The authors are indebted to the anonymous referee for a very detailed and insightful report on an earlier version of this paper that has led to many improvements in the current version. Address correspondence to: Tony He, School of Finance and Economics, University of Technology, Sydney, P.O. Box 123 Broadway, NSW 2007, Australia; e-mail: [email protected]. This paper studies the dynamics of a simple discounted present-value asset-pricing model where agents have different risk attitudes and follow different expectation formation schemes for the price distribution. A market-maker scenario is used as the market-clearing mechanism, in contrast to the more usual Walrasian scenario. In particular, the paper concentrates on models of fundamentalists and trend followers who follow recursive geometric-decay (learning) processes (GDP) with both finite and infinite memory. The analysis depicts how the dynamics are affected by various key elements (or parameters) of the model, such as the adjustment speed of the market maker, the extrapolation rate of the trend followers, the decay rate of the GDP, the lag length used in the learning GDP, and external random factors.

For a sample of U.S. industries, nominal wage and price inflation follow aggregate price inflation closely during economic expansions. Hence, fluctuations in profit markup and real output are moderate in the face of expansionary demand shocks. In contrast, workers' real standard of living decreases during recessions, while producers attempt to maintain, or even increase, industrial real-price inflation. The increase in profit markup correlates with larger output contraction during recessions.

We test the expectations theory of the term structure of U.S. interest rates in nonlinear systems. These models allow the response of the change in short rates to past values of the spread to depend upon the level of the spread. The nonlinear system is tested against a linear system, and the results of testing the expectations theory in both models are contrasted. We find that the results of tests of the implications of the expectations theory depend on the size and sign of the spread. The long maturity spread predicts future changes of the short rate only when it is high.

We develop a simple open-economy growth model in which productivity growth and education influence each other, and use it to empirically address issues of causality between them. Technology adoption fostered by human capital and economic openness determines productivity growth; the framework allows us to evaluate whether the effects of these two variables on growth documented in previous cross-country regression studies carry over to a simultaneous system closely allied to a model. This model implies that an increase in openness will stimulate technical change but, for empirically plausible values of the intertemporal elasticity of substitution, it will cause a decrease in the level of education. The empirical analysis specifies productivity growth and human capital as endogenous variables and finds evidence broadly consistent with the theory—openness and education stimulate productivity growth, and there is a negative effect of this growth on human capital accumulation.

This paper shows that high-frequency, irregularly spaced, foreign exchange (FX) data can generate nonnormality, conditional heteroskedasticity, and leptokurtosis when aggregated into fixed-interval calendar time, even when these features are absent in the original DGP. Furthermore, we introduce a new approach to modeling these high-frequency irregularly spaced data based on the Poisson regression model. The new model is called the autoregressive conditional intensity model and it has the advantage of being simple and of maintaining the calendar timescale. To illustrate the virtues of this approach, we examine a classical issue in FX microstructure: the variation in information content as a function of fluctuations in the intensity of activity levels.

One usually assumes that the joint probability distribution is known or that agents will use Bayesian updating to estimate the true probabilities after a number of trials when the states of nature are finite in classical decision theory under uncertainty. If there are important states that have very low probabilities of occurrence, then each agent must make a subjective assessment of the probability distribution until a sufficient number of outcomes are observed in order to generate a precise estimate of the probability distribution. If one assumes that all agents know the states and their payoffs, the probability distribution is stationary, and they observe all outcomes that unfold over time, then it will take at least 10 times the mean time between occurrences of the lowest probability event in order to generate enough outcomes that all agents share the same objective knowledge of the distribution. The mean time of recurrence depends on both the probability distribution and the time unit used between recordings of the observations. If at least one state has a low probability of occurrence, then the time to convergence will exceed the period of stationarity of the process.