Infant Mortality by Cause of Death: Main and Interaction Effects

We examine infant mortality among the 1980-1982 live birth cohorts in the state of Florida, specific to five categories of underlying cause of death: infections, perinatal conditions, delivery complications, congenital malformations, and sudden infant death syndrome. The gross and net effects of eight categorical and continuous independent variables, along with 11 first-order interactions, are examined with microlevel data through the use of multinomial logit regression. Findings suggest the complexity of variable effects by cause of death and indicate the simultaneous importance of biological and social factors. It is important that the pattern of interactions suggests an overall dependence of infant life chances on social circumstances. It also suggests that these effects are attenuated for some variables and causes of death at lower birth weights, probably due to advances in health care organization, access, and technology.     

Conditionally Efficient Estimation of Long-Run Relationships Using Mixed-Frequency Time Series

I analyze efficient estimation of a cointegrating vector when the regressand and regressor are observed at different frequencies. Previous authors have examined the effects of specific temporal aggregation or sampling schemes, finding conventionally efficient techniques to be efficient only when both the regressand and the regressors are average sampled. Using an alternative method for analyzing aggregation under more general weighting schemes, I derive an efficiency bound that is conditional on the type of aggregation used on the low-frequency series and differs from the unconditional bound defined by the full-information high-frequency data-generating process, which is infeasible due to aggregation of at least one series. I modify a conventional estimator, canonical cointegrating regression (CCR), to accommodate cases in which the aggregation weights are known. The correlation structure may be utilized to offset the potential information loss from aggregation, resulting in a conditionally efficient estimator. In the case of unknown weights, the correlation structure of the error term generally confounds identification of conditionally efficient weights. Efficiency is illustrated using a simulation study and an application to estimating a gasoline demand equation.