Testing unit root based on partially adaptive estimation

This paper proposes unit tests based on partially adaptive estimation. The proposed tests provide an intermediate class of inference procedures that are more efficient than the traditional OLS-based methods and simpler than unit root tests based on fully adptive estimation using nonparametric methods. The limiting distribution of the proposed test is a combination of standard normal and the traditional Dickey-Fuller (DF) distribution, including the traditional ADF test as a special case when using Gaussian density. Taking into a account the well documented characteristic of heavy-tail behavior in economic and financial data, we consider unit root tests coupled with a class of partially adaptive M-estimators based on the student-t distributions, wich includes te normal distribution as a limiting case. Monte Carlo Experiments indicate that, in the presence of heavy tail distributions or innovations that are contaminated by outliers, the proposed test is more powerful than the traditional ADF test. We apply the proposed test to several macroeconomic time series that have heavy-tailed distributions. The unit root hypothesis is rejected in U.S. real GNP, supporting the literature of transitory shocks in output. However, evidence against unit roots is not found in real exchange rate and nominal interest rate even haevy-tail is taken into a account.

A robust poverty profile for Brazil using multiple data sources

This paper presents a poverty profile for Brazil, based on three different sources of household data for 1996. We use PPV consumption data to estimate poverty and indigence lines. “Contagem” data is used to allow for an unprecedented refinement of the country’s poverty map. Poverty measures and shares are also presented for a wide range of population subgroups, based on the PNAD 1996, with new adjustments for imputed rents and spatial differences in cost of living. Robustness of the profile is verified with respect to different poverty lines, spatial price deflators, and equivalence scales. Overall poverty incidence ranges from 23% with respect to an indigence line to 45% with respect to a more generous poverty line. More importantly, however, poverty is found to vary significantly across regions and city sizes, with rural areas, small and medium towns and the metropolitan peripheries of the North and Northeast regions being poorest.