Using irregularly spaced returns to estimate multi-factor models: application to Brazilian equity data

Multi-factor models constitute a useful tool to explain cross-sectional covariance in equities returns. We propose in this paper the use of irregularly spaced returns in the multi-factor model estimation and provide an empirical example with the 389 most liquid equities in the Brazilian Market. The market index shows itself significant to explain equity returns while the US$/Brazilian Real exchange rate and the Brazilian standard interest rate does not. This example shows the usefulness of the estimation method in further using the model to fill in missing values and to provide interval forecasts.

Using irregularly spaced returns to estimate multi-factor models : application to Brazilian equity data

Multi-factor models constitute a use fui tool to explain cross-sectional covariance in equities retums. We propose in this paper the use of irregularly spaced returns in the multi-factor model estimation and provide an empirical example with the 389 most liquid equities in the Brazilian Market. The market index shows itself significant to explain equity returns while the US$/Brazilian Real exchange rate and the Brazilian standard interest rate does not. This example shows the usefulness of the estimation method in further using the model to fill in missing values and to provide intervaI forecasts.

Semiparametric estimation and inference using doubly robust moment conditions

We study semiparametric two-step estimators which have the same structure as parametric doubly robust estimators in their second step. The key difference is that we do not impose any parametric restriction on the nuisance functions that are estimated in a first stage, but retain a fully nonparametric model instead. We call these estimators semiparametric doubly robust estimators (SDREs), and show that they possess superior theoretical and practical properties compared to generic semiparametric two-step estimators. In particular, our estimators have substantially smaller first-order bias, allow for a wider range of nonparametric first-stage estimates, rate-optimal choices of smoothing parameters and data-driven estimates thereof, and their stochastic behavior can be well-approximated by classical first-order asymptotics. SDREs exist for a wide range of parameters of interest, particularly in semiparametric missing data and causal inference models. We illustrate our method with a simulation exercise.

Sanitation and health: empirical evidence for Brazilian municipalities

The relationship between sanitation policies (access and quality) and health in Brazilian municipalities was estimated from 2003 to 2010 using a panel data model with corrections for missing data. The results suggest a limited effect of sanitation policy on health. Compared with results from the literature, we found that the worsening quality of water appears to be associated with increased rates of mortality and hospitalization for children up to one month of age. Improvements in sewage sanitation have reduced the mortality and morbidity rates in children aged one to four. Improved access to piped water is associated with decreased hospitalization related to dysentery and acute respiratory infections (ARI) and does not have an effect on child mortality. Finally, epidemiological transition is only supported by weak evidence, including a more intense effect of reduced access to sanitation in municipalities with the worst mortality and morbidity indicators. In most models, this theory has been rejected