Most households persistently invest in riskless assets but not stocks, and may do so because they perceive information required for market participation to be costly relative to expected benefits. In a Consumption Capital Asset Pricing Model (CCAPM) increased risk aversion, income risk, and lower resources reduce the information expense sufficient to deter stockholding. Bivariate probit analysis using the 1983–1989 Survey of Consumer Finances shows that households with lower risk aversion, higher education, and greater wealth who were nonstockholders in 1983 had an increased conditional probability of entering by 1989, whereas 1983 stockholders with lower resources, more limited education, and greater risk aversion were more likely to be nonstockholders by 1989.

Many panel data sets encountered in macroeconomics, international economics, regional science, and finance are characterized by cross-sectional or “spatial” dependence. Standard techniques that fail to account for this dependence will result in inconsistently estimated standard errors. In this paper we present conditions under which a simple extension of common nonparametric covariance matrix estimation techniques yields standard error estimates that are robust to very general forms of spatial and temporal dependence as the time dimension becomes large. We illustrate the relevance of this approach using Monte Carlo simulations and a number of empirical examples.

We use unique data from the Medicaid program of the Commonwealth of Kentucky to examine the duration of Medicaid spells. The data set consists of a one-in-ten sample of all Medicaid recipients in Kentucky on July 1, 1986, and a similar sample of all new spells between July 1, 1986, and June 30, 1987. Because the beginning date of Medicaid recipiency is known for all spells, this mixed "stock" and "flow" sample allows us to identify the duration of Medicaid spells for up to twenty years. This is in contrast to other studies using short panels of new spells. We find significant differences in hazard functions across program eligibility categories, suggesting that the cost of expanding Medicaid or the savings from contracting it would vary depending on the eligibility group affected by the change in policy.

In this paper we examine a number of recent studies that claim to have obtained a well-defined liquidity effect using structural VAR models based on broad measures of money. These studies can be distinguished in terms of the identifying restrictions, sample periods, and frequency of data used. We show that estimation of the structural coefficients of all these models can be achieved by instrumental-variable methods, where the instruments are predetermined variables and the estimated structural errors from other equations in the system. Overall, our judgment is that the evidence for a liquidity effect from these studies is much less certain than suggested in the original papers, primarily because of the poor quality of the instruments used in estimation and the sensitivity of the estimates to the sample period used.

This study provides new evidence of the importance of financing constraints for explaining the dramatic cycles in inventory investment. We compare the empirical performance of different financial variables (coverage ratio, cash stocks, and cash flow) used in previous research to test for the presence of financing constraints. The comparison is undertaken in a common framework with an identical sample and high-frequency (quarterly) firm panel data. Cash flow is much more successful than cash stocks or coverage in explaining the facts about inventory investment across firm size, different inventory cycles, and different manufacturing sectors.