Foreign funding to an emerging market: the Monetary Premium Theory and the Brazilian Case, 1991 - 1998

We develop a framework to explain the private capital flows between the rest of the world and an emerging economy. The model, based on the monetary premium theory, relates an endogenous supply of foreign capitals to an endogenous differential of interest rates; its estimation uses the econometric techniques initiated by Heckman. Four questions regarding the capital flows phenomenon are explored, including the statistical process that governs the events of default and the impact of the probability of default on the interest rate differential. Using the methodology, we analyse the dynamics of foreign capital movements in Brazil during the 1991- 1998 period.

Evidence on the incentive optimality of share contracts

Ever since Adam Smith, economists have argued that share contracts do not provide proper incentives. This paper uses tenancy data from India to assess the existence of missing incentives in this classical example of moral hazard. Sharecroppers are found to be less productive than owners, but as productive as fixed-rent tenants. Also, the productivity gap between owners and both types of tenants is driven by sample-selection issues. An endogenous selection rule matches tenancy contracts with less-skilled farmers and lower-quality lands. Due to complementarity, such a matching affects tenants’ input choices. Controlling for that, the contract form has no effect on the expected output. Next, I explicitly model farmer’s optimal decisions to test the existence of non-contractible inputs being misused. No evidence of missing incentives is found.

Parcerias público-privadas na presença de seleção adversa: uma análise numérica

Esta dissertação propõe-se a analisar a otimalidade de parcerias público-privadas (PPP) em um ambiente de seleção adversa. Mais especi calmente, analiso se as tarefas de construir e operar uma infraestrutura para serviços públicos deve ser realizada por um consórcio ou se devem ser realizadas por fi rmas contratadas separadamente. Para tal, diferentemente da literatura existente para o problema, focadas nos problemas de contratos incompletos e moral hazard, construo um modelo de seleção adversa multidimensional, onde as firrmas possuem informação privada e podem exercer um esforço não observável em cada atividade, com a existência de externalidade entre as tarefas, em uma extensão do modelo de La¤ont e Tirole (1986). Após defi nir algumas condições sob as quais PPP domina os meios usuais de contratação, utilizo de uma análise numérica para melhor compreender a solução do modelo, que se mostra altamente dependente da correlação entre os parâmetros de informação privada das firmas.

Forecasting electricity demand using generalized long memory

This paper studies the electricity hourly load demand in the area covered by a utility situated in the southeast of Brazil. We propose a stochastic model which employs generalized long memory (by means of Gegenbauer processes) to model the seasonal behavior of the load. The model is proposed for sectional data, that is, each hour’s load is studied separately as a single series. This approach avoids modeling the intricate intra-day pattern (load profile) displayed by the load, which varies throughout days of the week and seasons. The forecasting performance of the model is compared with a SARIMA benchmark using the years of 1999 and 2000 as the out-of-sample. The model clearly outperforms the benchmark. We conclude for general long memory in the series.

Forecasting Brazilian inflation by its aggregate and disaggregated data: a test of predictive power by forecast horizon

This work aims to compare the forecast efficiency of different types of methodologies applied to Brazilian Consumer inflation (IPCA). We will compare forecasting models using disaggregated and aggregated data over twelve months ahead. The disaggregated models were estimated by SARIMA and will have different levels of disaggregation. Aggregated models will be estimated by time series techniques such as SARIMA, state-space structural models and Markov-switching. The forecasting accuracy comparison will be made by the selection model procedure known as Model Confidence Set and by Diebold-Mariano procedure. We were able to find evidence of forecast accuracy gains in models using more disaggregated data

On the effects of non-tariff measures on Brazilian exports

This article proposes an alternative methodology for estimating the effects of non-tariff measures on trade flows, based on the recent literature on gravity models. A two-stage Heckman selection model is applied to the case of Brazilian exports, where the second stage gravity equation is theoretically grounded on the seminal Melitz model of heterogeneous firms. This extended gravity equation highlights the role played by zero trade flows as well as firm heterogeneity in explaining bilateral trade among countries, two factors usually omitted in traditional gravity specifications found in previous literature. Last, it also proposes a economic rationale for the effects of NTM on trade flows, helping to shed some light on its main operating channels under a rather simple Cournot’s duopolistic competition framework.

Supremo interesse: protagonismo político-regulatório e a evolução institucional do processo de seleção dos ministros do STF

Partindo da constatação de que o Brasil acompanha hoje um fenômeno global de protagonismo das cortes supremas nas sociedades complexas contemporâneas, notadamente na criação de políticas-públicas e regulação, o estudo procura mapear a evolução – e progressiva democratização – de uma estrutura de freios e contrapesos prevista na Constituição da República Federativa do Brasil de 1988 (“Constituição”), qual seja, o processo de seleção dos ministros do Supremo Tribunal Federal. Ao longo do texto é analisada a arquitetura institucional e constitucional do processo de indicação e aprovação de novos ministros, bem como exemplificadas mudanças no perfil dos atores políticos, no plexo de competências das instituições envolvidas e no contexto social, político, econômico e cultural que forçaram a transformação prática do modelo de seleção institucional, sem alteração, no entanto, da formatação originalmente prevista desde o Século XIX. Mapeando a origem e evolução da fórmula constitucional de colaboração entre o Poder Executivo e o Poder Legislativo para a escolha dos membros da cúpula do Poder Judiciário, o estudo identifica a origem do modelo brasileiro na inspiração da experiência norte-americana, descrevendo esta e os paralelos possíveis com aquele. A partir do marco central da Constituição, o trabalho procura demonstrar uma progressiva mobilização de atores políticos e sociais em relação ao processo de escolha, notadamente em relação ao momento em que os indicados para o Supremo Tribunal Federal são sabatinados pela Comissão de Constituição, Justiça e Cidadania do Senado Federal. Finalmente, são analisadas concretamente as sabatinas e algumas das suas principais discussões, buscando extrair lições que sirvam de norte colaborativo para a evolução da forma de seleção dos ministros do Supremo Tribunal Federal, inclusive como instrumento de controle prévio de seus membros, futuros elaboradores de políticas-públicas.

On the effects of non tariff measures on Brazilian exports

This article proposes an alternative methodology for estimating the effects of non-tariff measures on trade flows, based on the recent literature on gravity models. A two-stage Heckman selection model is applied to the case of Brazilian exports, where the second stage gravity equation is theoretically grounded on the seminal Melitz model of heterogeneous firms. This extended gravity equation highlights the role played by zero trade flows as well as firm heterogeneity in explaining bilateral trade among countries, two factors usually omitted in traditional gravity specifications found in previous literature. Last, it also proposes a economic rationale for the effects of NTM on trade flows, helping to shed some light on its main operating channels under a rather simple Cournot’s duopolistic competition framework

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.

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.

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 find 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.

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.

The role of no-arbitrage on forecasting: lessons from a parametric term structure model

Parametric term structure models have been successfully applied to innumerous problems in fixed income markets, including pricing, hedging, managing risk, as well as studying monetary policy implications. On their turn, dynamic term structure models, equipped with stronger economic structure, have been mainly adopted to price derivatives and explain empirical stylized facts. In this paper, we combine flavors of those two classes of models to test if no-arbitrage affects forecasting. We construct cross section (allowing arbitrages) and arbitrage-free versions of a parametric polynomial model to analyze how well they predict out-of-sample interest rates. Based on U.S. Treasury yield data, we find that no-arbitrage restrictions significantly improve forecasts. Arbitrage-free versions achieve overall smaller biases and Root Mean Square Errors for most maturities and forecasting horizons. Furthermore, a decomposition of forecasts into forward-rates and holding return premia indicates that the superior performance of no-arbitrage versions is due to a better identification of bond risk premium.