Research Database

This paper explains why the risky notes of banks established during the Free Banking Era (1837–63) were demanded even when relatively safe specie (gold and silver coin) was an alternative. Free bank notes were demanded because they were priced to reflect the expected value of their backing. The empirical evidence supports this explanation. Specifically, in New York, Wisconsin, and Indiana the expected value of backing was sufficient for free bank notes to circulate at par, which they did. In Minnesota the backing for notes was very poor: they exchanged well below par, being treated as small-denomination securities.

An examination of the behavior of stock returns around quarterly earnings announcement dates finds a seasonal pattern: small firms show large positive abnormal returns and a sizable increase in the variability of returns around these dates. Only part of the large abnormal returns can be accounted for by the fact that firms with good news tend to announce early. Large firms show no abnormal returns around announcement dates and a much smaller increase in variability.

The consequences of a straightforward monetary targeting scheme are examined for a simple dynamic macro model. The notion of “targeting” used is the strategic one introduced by Rogoff (1985). Numerical calculations are used to demonstrate that for the model under consideration, monetary targeting is likely to lead to a deterioration of policy performance. These examples cast doubt upon the general efficacy of simple targeting schemes in dynamic rational expectations models.

Deaton (1986) has noted that if income is a first-order autoregressive process in first differences, then a simple version of Friedman’s permanent income hypothesis (SPIH) implies that measured U.S. consumption is insufficiently sensitive to innovations in income. This paper argues that this implication of the SPIH is a consequence of the fact that it ignores the role of the substitution effect in the consumption decision. Using a parametric version of the standard model of economic growth, the paper shows that very small movements in interest rates are sufficient to induce an empirically plausible amount of consumption smoothing. Since an overall evaluation of the model’s explanation for the observed smoothness of consumption requires examining its implications for other aspects of the data, the paper also explores some of these.

The statistical significance of variance decompositions and impulse response functions for unrestricted vector autoregressions is questionable. Most previous studies are suspect because they have not provided confidence intervals for variance decompositions and impulse response functions. Here two methods of computing such intervals are developed, one using a normal approximation, the other using bootstrapped resampling. An example from Sims’ work illustrates the importance of computing these confidence intervals. In the example, the 95 percent confidence intervals for variance decompositions span up to 66 percentage points at that usual forecasting horizon.

Doan, Litterman, and Sims (DLS) have suggested using conditional forecasts to do policy analysis with Bayesian vector autoregression (BVAR) models. Their method seems to violate the Lucas critique, which implies that coefficients of a BVAR model will change when there is a change in policy rules. In this paper we construct a BVAR macro model and attempt to determine whether the Lucas critique is important quantitatively. We find evidence following two candidate policy rule changes of significant coefficient instability and of a deterioration in the performance of the DLS method.

A bivariate Granger-causality test on money and output finds statistically significant causality when data are measured in log levels, but not when they are measured in first differences of the logs. Which of these results is right? The answer to that question matters because a finding of no Granger-causality from money to output would substantially embarrass existing business cycle models in which money plays an important role [Eichenbaum and Singleton (1986)]. Monte Carlo simulation experiments indicate that, most probably, the first difference results reflect lack of power, whereas the level results reflect Granger-causality that is actually in the data.