Public debt indexation and denomination, the case of Brazil: a comment

In this work I analyze the model proposed by Goldfajn (2000) to study the choice of the denomination of the public debt. The main purpose of the analysis is pointing out possible reasons why new empirical evidence provided by Bevilaqua, Garcia and Nechio (2004), regarding a more recent time period, Önds a lower empirical support to the model. I also provide a measure of the overestimation of the welfare gains of hedging the debt led by the simpliÖed time frame of the model. Assuming a time-preference parameter of 0.9, for instance, welfare gains associated with a hedge to the debt that reduces to a half a once-for-all 20%-of-GDP shock to government spending run around 1.43% of GDP under the no-tax-smoothing structure of the model. Under a Ramsey allocation, though, welfare gains amount to just around 0.05% of GDP.

O Processo de Concessão dos Sistemas de Transporte de Massa na Região Metropolitana do Rio de Janeiro: Implicações na Organização e na Gestão de Transporte de Passageiros

O presente estudo procurou descrever e analisar o contexto em que se desenvolveu o processo de concessão dos sistemas de transporte de massa na Região Metropolitana do Rio de Janeiro, promovido pelo Programa Estadual de Desestatização ¿ PED, na gestão governamental compreendida entre os anos de 1995 e 1998, bem como avaliar suas implicações sobre o modelo de organização e gestão do transporte público regional então vigente. Seu desenvolvimento enfatizou três aspectos desse processo: a caracterização do cenário anterior à proposta de mudança, a análise substantiva da política representada pelo programa de concessões e a avaliação do novo cenário criado como conseqüência do programa. Sua metodologia pautou-se em consulta bibliográfica, volumosa análise documental, observação dos fatos e entrevistas desestruturadas com administradores e técnicos envolvidos no processo. Seus resultados evidenciaram as limitações dos modelos de análise e de planejamento tradicionalmente adotados para a formulação das políticas setoriais, a precariedade dos sistemas de transporte de passageiros regionais e a situação pelos sistemas de metrô, trens e barcas, consubstanciando um ambiente propício às propostas de sua transferência à gestão privada. Evidenciaram, ainda, que a iniciativa foi influenciada pelo contexto dos projetos de reforma do Estado patrocinados pelo Banco Mundial (BIRD), desenvolvendo-se sem referências relevantes na comunidade técnica setorial e gerando um cenário institucional frágil diante da tarefa de gerir os contratos dela resultantes. Embora pautado em estratégias de retomada de investimentos condizentes com as diretrizes do Plano de Transporte de Massa ¿ PTM, elaborado em 1994, a insipiência do programa não permite constatar, ainda tendências significativas no desempenho dos sistemas concedidos. São evidentes, entretanto, seus reflexos na desentruturação do modelo de gestão pública do transporte metropolitano sob responsabilidade do Estado.

Discricionariedade vs. comprometimento: a análise de regras ótimas num contexto de regime de metas de inflação

O objetivo desta dissertação é analisar as regras de condução da política monetária em modelos em que os agentes formam suas expectativas de forma racional (forward looking models), no contexto do regime de metas de inflação. As soluções ótimas de pré - comprometimento e discricionária são derivadas e aplicadas a um modelo macroeconômico para a economia brasileira e os resultados são também comparados com os obtidos pela adoção da regra de Taylor. A análise do comportamento do modelo sob diferentes regras é feita através da construção da fronteira do trede-oit da variância do hiato do produto e da inflação e da análise dinâmica frente a ocorrência de choques. A discussão referente à análise dinâmica do modelo é estendida para o caso onde a persistência dos choques é variada.

Comments on public debt indexation and denomination, the case of Brazil

In this work I analyze the model proposed by Goldfajn (2000) to study the choice of the denomination of the public debt. Some potential shortcmomings of the mo dei in explaining the data are discussed. Measures of the overestimation of the welfare gains of reducing distortions from taxation, under the model's simplified time frame, are also provided. Assuming a time-preference parameter of 0.9, for instance, welfare gains associated with a hedge to the debt that reduces to half a once-for-all 20o/o-of-GDP shock to governemnt spending run around 1.43% of GDP under the no-tax-smoothing structure of the model. Under a Ramsey allocation, though, welfare gains amount to just around 0.05% of GDP.

Robustness and stabilization properties of monetary policy rules in Brazil

Based on three versions of a small macroeconomic model for Brazil, this paper presents empirical evidence on the effects of parameter uncertainty on monetary policy rules and on the robustness of optimal and simple rules over different model specifications. By comparing the optimal policy rule under parameter uncertainty with the rule calculated under purely additive uncertainty, we find that parameter uncertainty should make policymakers react less aggressively to the economy's state variables, as suggested by Brainard's "conservatism principIe", although this effect seems to be relatively small. We then informally investigate each rule's robustness by analyzing the performance of policy rules derived from each model under each one of the alternative models. We find that optimal rules derived from each model perform very poorly under alternative models, whereas a simple Taylor rule is relatively robusto We also fmd that even within a specific model, the Taylor rule may perform better than the optimal rule under particularly unfavorable realizations from the policymaker' s loss distribution function.

A model of mixed signals with applications to countersignaling an the GED

We develop a job-market signaling model where signals may convey two pieces of information. This model is employed to study the GED exam and countersignaling (signals non-monotonic in ability). A result of the model is that countersignaling is more expected to occur in jobs that require a combination of skills that differs from the combination used in the schooling process. The model also produces testable implications consistent with evidence on the GED: (i) it signals both high cognitive and low non-cognitive skills and (ii) it does not affect wages. Additionally, it suggests modifications that would make the GED a more signal.

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 for a lack of parsimony, as well as the traditional ones. We suggest a new procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties. In order to compute the fit of each model, we propose an iterative procedure to compute the maximum likelihood estimates of parameters of a VAR model with short-run and long-run restrictions. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank, relative to the commonly used procedure of selecting the lag-length only and then testing for cointegration.

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.

Forecasting multivariate time series under present-value-model short- and long-run co-movement restrictions

It is well known that cointegration between the level of two variables (e.g. prices and dividends) is a necessary condition to assess the empirical validity of a present-value model (PVM) linking them. The work on cointegration,namelyon long-run co-movements, has been so prevalent that it is often over-looked that another necessary condition for the PVM to hold is that the forecast error entailed by the model is orthogonal to the past. This amounts to investigate whether short-run co-movememts steming from common cyclical feature restrictions are also present in such a system. In this paper we test for the presence of such co-movement on long- and short-term interest rates and on price and dividend for the U.S. economy. We focuss on the potential improvement in forecasting accuracies when imposing those two types of restrictions coming from economic theory.

Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions

This paper has two original contributions. First, we show that the present value model (PVM hereafter), which has a wide application in macroeconomics and fi nance, entails common cyclical feature restrictions in the dynamics of the vector error-correction representation (Vahid and Engle, 1993); something that has been already investigated in that VECM context by Johansen and Swensen (1999, 2011) but has not been discussed before with this new emphasis. We also provide the present value reduced rank constraints to be tested within the log-linear model. Our second contribution relates to forecasting time series that are subject to those long and short-run reduced rank restrictions. The reason why appropriate common cyclical feature restrictions might improve forecasting is because it finds natural exclusion restrictions preventing the estimation of useless parameters, which would otherwise contribute to the increase of forecast variance with no expected reduction in bias. We applied the techniques discussed in this paper to data known to be subject to present value restrictions, i.e. the online series maintained and up-dated by Shiller. We focus on three different data sets. The fi rst includes the levels of interest rates with long and short maturities, the second includes the level of real price and dividend for the S&P composite index, and the third includes the logarithmic transformation of prices and dividends. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to them. Moreover, imposing short-run restrictions produce forecast winners 70% of the time for target variables of PVMs and 63.33% of the time when all variables in the system are considered.

Forecasting multivariate time series under present-value-model short- and long-run co-movement restrictions

Using a sequence of nested multivariate models that are VAR-based, we discuss different layers of restrictions imposed by present-value models (PVM hereafter) on the VAR in levels for series that are subject to present-value restrictions. Our focus is novel - we are interested in the short-run restrictions entailed by PVMs (Vahid and Engle, 1993, 1997) and their implications for forecasting. Using a well-known database, kept by Robert Shiller, we implement a forecasting competition that imposes different layers of PVM restrictions. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to the unrestricted VAR. Moreover, imposing short-run restrictions produces forecast winners 70% of the time for the target variables of PVMs and 63.33% of the time when all variables in the system are considered.

Does mixed frequency vector error correction model add relevant information to exchange misalignment calculus? Evidence for United States

Real exchange rate is an important macroeconomic price in the economy and a ects economic activity, interest rates, domestic prices, trade and investiments ows among other variables. Methodologies have been developed in empirical exchange rate misalignment studies to evaluate whether a real e ective exchange is overvalued or undervalued. There is a vast body of literature on the determinants of long-term real exchange rates and on empirical strategies to implement the equilibrium norms obtained from theoretical models. This study seeks to contribute to this literature by showing that it is possible to calculate the misalignment from a mixed ointegrated vector error correction framework. An empirical exercise using United States' real exchange rate data is performed. The results suggest that the model with mixed frequency data is preferred to the models with same frequency variables

Automatic model selection for forecasting brazilian stock returns

This study aims to contribute on the forecasting literature in stock return for emerging markets. We use Autometrics to select relevant predictors among macroeconomic, microeconomic and technical variables. We develop predictive models for the Brazilian market premium, measured as the excess return over Selic interest rate, Itaú SA, Itaú-Unibanco and Bradesco stock returns. We nd that for the market premium, an ADL with error correction is able to outperform the benchmarks in terms of economic performance. For individual stock returns, there is a trade o between statistical properties and out-of-sample performance of the model.