Equity and Efficiency in Education: motivations and targets

The recently released "Educational PAC" attempts to place basic education at the center of the social debate. We have subsidized this debate, offering a diagnosis of how different education levels can impact individuals' lives through broad and easily interpreted indicators. Initially, we analyze how much each educational level reaches the poorest population. For example, how are those in the bottom strata of income distribution benefited by childcare centers, private secondary education, public university or adult education. The next step is to quantify the return of educational actions, such as their effects on employability and an individual's wages, and even health as perceived by the individual, be that individual poor, middle class or elite. The next part of the research presents evidence of how the main characters in education, aka mothers, fathers and children, regard education. The site available with the research presents a broad, user-friendly database, which will allow interested parties to answer their own questions relative to why people do not attend school, the time spent in the educational system and returns to education, which can all be cross-sectioned with a wide array of socio-demographic attributes (gender, income, etc.) and school characteristics (is it public, are school meals offered, etc.) to find answers to: why do young adults of a certain age not attend school? Why do they miss classes? How long is the school day? Aside from the whys and hows of teaching, the research calculates the amount of time spent in school, resulting from a combination between absence rates, evasion raters and length of the school day. The study presents ranks of indicators referring to objective and subjective aspects of education, such as the discussion of the advantages and care in establishing performance based incentives that aim at guiding the states in the race for better educational indicators.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties as well as the traditional ones. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank using our proposed procedure, relative to an unrestricted VAR or a cointegrated VAR estimated by the commonly used procedure of selecting the lag-length only and then testing for cointegration. Two empirical applications forecasting Brazilian inflation and U.S. macroeconomic aggregates growth rates respectively show the usefulness of the model-selection strategy proposed here. The gains in different measures of forecasting accuracy are substantial, especially for short horizons.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties as well as the traditional ones. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank using our proposed procedure, relative to an unrestricted VAR or a cointegrated VAR estimated by the commonly used procedure of selecting the lag-length only and then testing for cointegration. Two empirical applications forecasting Brazilian in ation and U.S. macroeconomic aggregates growth rates respectively show the usefulness of the model-selection strategy proposed here. The gains in di¤erent measures of forecasting accuracy are substantial, especially for short horizons.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. A Monte Carlo study explores the finite sample performance of this procedure and evaluates the forecasting accuracy of models selected by this procedure. Two empirical applications confirm the usefulness of the model selection procedure proposed here for forecasting.

Toward a theory of international currency : a step further

We generalize the two-country, two-currency model of Matsuyama, Kiyotaki and Matsui to resolve two "shortcomings" in their approach. First, we endogenize prices and excb.ange rates. Second, we introduce monetary policy. We then use the model to address the following new questions: How does the fact that a currency circulates intemationally affect its purcb.asing power? Where does an intemational currency purcb.ase more? What are the effects on seignorage and welfare when a currency becomes intemational? How is policy affected by concems of currency substitution? How are national monetary policies connected, and what is the scope for international cooperation?