We investigate the quantitative behavior of business-cycle models in which the intermediation process acts either as a source of fluctuations or as a propagator of real shocks. In neither case do we find convincing evidence that the intermediation process is an important element of aggregate fluctuations. For an economy driven by intermediation shocks, consumption is not smoother than output, investment is negatively correlated with output, variations in the capital stock are quite large, and interest rates are procyclical. The model economy thus fails to match unconditional moments for the U.S. economy. We also structurally estimate parameters of a model economy in which intermediation and productivity shocks are present, allowing for the intermediation process to propagate the real shock. The unconditional correlations are closer to those observed only when the intermediation shock is relatively unimportant.

This paper addresses the problem of multiple equilibria in a model of time-consistent monetary policy. It suggests that this problem originates in the assumption that agents have rational expectations and proposes several alternative restrictions on expectations that allow the monetary authority to build credibility for a disinflationary policy by demonstrating that it will stick to that policy even if it imposes short-run costs on the economy. Starting with these restrictions, the paper derives conditions that guarantee the uniqueness of the model's steady state; monetary policy in this unique steady state involves the constant deflation advocated by Milton Friedman.

We present a class of stochastic regime-switching models. The time-series models may have periodic transition probabilities and the drifts may be seasonal. In the latter case, the model exhibits seasonal dummy variation that may change with the regime. The processes entail nontrivial interactions between so-called business and seasonal cycles. We discuss the stochastic properties as well as their relationship with periodic ARMA processes. Estimation and testing are also discussed in detail.

We recast Diamond's search equilibrium model into that with a finite number of agents. The state of the model is described by a jump-Markov process, the transition rates of which are functions of the reservation cost, which are endogenously determined by value maximization by rational agents. The existence of stochastic fluctuations causes the fraction of the employed to move from one basin of attraction to the other with positive probabilities when the dynamics have multiple equilibria. Stochastic asymmetric cycles that arise are quite different from the cycles of the set of Diamond–Fudenberg nonlinear deterministic differential equations. By taking the number of agents to infinity, we get a limiting probability distribution over the stationary state equilibria. This provides a natural basis for equilibrium selection in models with multiple equilibria, which is new in the economic literature.

This paper embeds time-varying volatility into a dynamic equilibrium model of returns and trading. The model allows us to ask how time-varying volatility might affect the relation among return autocorrelation, volatility, and trading volume, as opposed to the pairwise relations that have been studied previously. It is shown analytically that, with time-varying volatility, the relationship between volume and stock return autocorrelation is ambiguous even if agents have symmetric information, which may explain the contradictory findings in the empirical literature. In the numerical exercise, the model is simulated in a way that mimics the persistent volatility of high-frequency stock data documented in numerous empirical studies. Specially, the time-varying volatility of stock returns is approximated with a highly persistent chaotic tent map, which is known to have the same autocorrelation coefficients as an AR(1) process. The simulated data can approximate GARCH-type behavior very well. Whereas in the simulated data, no significant relation between volume and return autocorrelation can be found, there is a significantly positive relation between volume and one-step-ahead stock return volatility. The ambiguous volume–persistence and positive volume–volatility relations are confirmed empirically by using four heavily traded individual stocks. Therefore, the data simulated from the highly stylized asset pricing model with deterministic time-varying volatility can mimic well the volume–return dynamics revealed in the observed data in these two respects.

The aggregation of sectoral or regional Phillips curves yields an inflation–unemployment trade-off that is not vertical in the long run if there are mismatches between supply and demand in the regional or sectoral labor markets. This remains true even when the individual Phillips curves are all vertical. This result stems from variations in the slope of the individual short-run Phillips curves, rather than from changes to the equilibrium level of unemployment. It implies a role for the management of the distribution of demand over different sectors or regions, in order to minimize the natural rate of unemployment.

We derive a seminonparametric utility function containing the constant relative risk aversion (CRRA) function as a special case, and we estimate the associated Euler equations with U.S. consumption data. There is strong evidence that the CRRA function is misspecified. The correctly specified function includes lagged effects of durable goods and perhaps nondurable goods, is bounded as required by Arrow's Utility Boundedness Theorem, and has a positive rate of time preference. Constraining sample periods and separability structure to be consistent with the generalized axiom of revealed preference affects estimation results substantially. Using Divisia aggregates instead of the NIPA aggregates also affects results.