O Impacto do Risco Inflacionário sobre o Juros no Brasil

Esta dissertação analisa a conexão existente entre o mercado de dívida pública e a política monetária no Brasil. Com base em um Vetor Auto-Regressivo (VAR), foram utilizadas duas proxies alternativas de risco inflacionário para mostrar que choques positivos no risco inflacionário elevam tanto as expectativas de inflação do mercado quanto os juros futuros do Swap Pré x DI. Em seguida, com base em modelo de inconsistência dinâmica de Blanchard e Missale (1994) e utilizando a metodologia de Johansen, constatou-se que um aumento nos juros futuros diminui a maturidade da dívida pública, no longo prazo. Os resultados levam a duas conclusões: o risco inflacionário 1) dificulta a colocação de títulos nominais (não-indexados) no mercado pelo governo, gerando um perfil de dívida menos longo do que o ideal e 2) torna a política monetária mais custosa.

O impacto do risco inflacionário sobre os juros no Brasil

Esta dissertação analisa a conexão existente entre o mercado de dívida pública e a política monetária no Brasil. Com base em um Vetor Auto-Regressivo (VAR), foram utilizadas duas proxies alternativas de risco inflacionário para mostrar que choques positivos no risco inflacionário elevam tanto as expectativas de inflação do mercado quanto os juros futuros do Swap Pré x DI. Em seguida, com base em modelo de inconsistência dinâmica de Blanchard e Missale (1994) e utilizando a metodologia de Johansen, constatou-se que um aumento nos juros futuros diminui a maturidade da dívida pública, no longo prazo. Os resultados levam a duas conclusões: o risco inflacionário 1) dificulta a colocação de títulos nominais (não-indexados) no mercado pelo governo, gerando um perfil de dívida menos longo do que o ideal e 2) torna a política monetária mais custosa.

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

Modelos condicionais de apreçamento de ativos com prêmios de risco macroeconômicos variantes no tempo

Analisamos a previsibilidade dos retornos mensais de ativos no mercado brasileiro em um período de 10 anos desde o início do plano Real. Para analisarmos a variação cross-section dos retornos e explicarmos estes retornos em função de prêmios de risco variantes no tempo, condicionados a variáveis de estado macroeconômicas, utilizamos um novo modelo de apreçamento de ativos, combinando dois diferentes tipos de modelos econômicos, um modelo de finanças - condicional e multifatorial, e um modelo estritamente macroeconômico do tipo Vector Auto Regressive. Verificamos que o modelo com betas condicionais não explica adequadamente os retornos dos ativos, porém o modelo com os prêmios de risco (e não os betas) condicionais, produz resultados com interpretação econômica e estatisticamente satis fatórios

Determinantes do nível de caixa das empresas: análise de amostra de países da América Latina

Neste estudo são analisados, através de técnicas de dados em painel, os fatores determinantes dos níveis de ativos líquidos de empresas abertas do Brasil, Argentina, Chile, México e Peru no período de 1995 a 2009. O índice utilizado nas modelagens é denominado de ativo líquido (ou simplesmente caixa), o qual inclui os recursos disponíveis em caixa e as aplicações financeiras de curto prazo, divididos pelo total de ativos da firma. É possível identificar uma tendência crescente de acúmulo de ativos líquidos como proporção do total de ativos ao longo dos anos em praticamente todos os países. São encontradas evidências de que empresas com maiores oportunidades de crescimento, maior tamanho (medido pelo total de ativos), maior nível de pagamento de dividendos e maior nível de lucratividade, acumulam mais caixa na maior parte dos países analisados. Da mesma forma, empresas com maiores níveis de investimento em ativo imobilizado, maior geração de caixa, maior volatilidade do fluxo de caixa, maior alavancagem e maior nível de capital de giro, apresentam menor nível de acúmulo de ativos líquidos. São identificadas semelhanças de fatores determinantes de liquidez em relação a estudos empíricos com empresas de países desenvolvidos, bem como diferenças devido a fenômenos particulares de países emergentes, como por exemplo elevadas taxas de juros internas, diferentes graus de acessibilidade ao mercado de crédito internacional e a linhas de crédito de agências de fomento, equity kicking, entre outros. Em teste para a base de dados das maiores firmas do Brasil, é identificada a presença de níveis-alvo de caixa através de modelo auto-regressivo de primeira ordem (AR1). Variáveis presentes em estudos mais recentes com empresas de países desenvolvidos como aquisições, abertura recente de capital e nível de governança corporativa também são testadas para a base de dados do Brasil.

Forecast comparison with nonlinear methods for Brazilian industrial production

This work assesses the forecasts of three nonlinear methods | Markov Switching Autoregressive Model, Logistic Smooth Transition Auto-regressive Model, and Auto-metrics with Dummy Saturation | for the Brazilian monthly industrial production and tests if they are more accurate than those of naive predictors such as the autoregressive model of order p and the double di erencing device. The results show that the step dummy saturation and the logistic smooth transition autoregressive can be superior to the double di erencing device, but the linear autoregressive model is more accurate than all the other methods analyzed.

What happens after the central bank of Brazil increases the target interbank rate by 1%?

I start presenting an explicit solution to Taylorís (2001) model, in order to illustrate the link between the target interest rate and the overnight interest rate prevailing in the economy. Next, I use Vector Auto Regressions to shed some light on the evolution of key macroeconomic variables after the Central Bank of Brazil increases the target interest rate by 1%. Point estimates show a four-year accumulated output loss ranging from 0:04% (whole sample, 1980 : 1-2004 : 2; quarterly data) to 0:25% (Post-Real data only) with a Örst-year peak output response between 0:04% and 1:0%; respectively. Prices decline between 2% and 4% in a 4-year horizon. The accumulated output response is found to be between 3:5 and 6 times higher after the Real Plan than when the whole sample is considered. The 95% confidence bands obtained using bias-corrected bootstrap always include the null output response when the whole sample is used, but not when the data is restricted to the Post-Real period. Innovations to interest rates explain between 4:9% (whole sample) and 9:2% (post-Real sample) of the forecast error of GDP.

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.

What happens after the Central Bank of Brazil increases the target interbank rate by 1%?

In this paper I use Taylor's (2001) model and Vector Auto Regressions to shed some light on the evolution of some key macroeconomic variables after the Central Bank of Brazil, through the COPOM, increases the target interest rate by 1%. From a quantitative perspective, the best estimate from the empírical analysis, obtained with a 1994 : 2 - 2004 : 2 subsample of the data, is that GDP goes through an accumulated decline, over the next four years, around 0.08%. Innovations to interest rates explain around 9.2% of the forecast erro r of GDP.

Ensaios sobre ciclos de negócios

O objetivo central desse artigo é o de propor e avaliar modelos econométricos de previsão para o PIB industrial brasileiro. Para tanto, foram utilizados diversos modelos de previsão como também combinações de modelos. Foi realizada uma analise criteriosa das séries a serem utilizadas na previsão. Nós concluímos que a utilização de vetores de cointegração melhora substancialmente a performance da previsão. Além disso, os modelos de combinação de previsão, na maioria dos casos, tiveram uma performance superior aos demais modelos, que já apresentavam boa capacidade preditiva.

The impact of OFDI on economic growth countries. An econometric approach using panel data and time-series evidence

The thesis at hand adds to the existing literature by investigating the relationship between economic growth and outward foreign direct investments (OFDI) on a set of 16 emerging countries. Two different econometric techniques are employed: a panel data regression analysis and a time-series causality analysis. Results from the regression analysis indicate a positive and significant correlation between OFDI and economic growth. Additionally, the coefficient for the OFDI variable is robust in the sense specified by the Extreme Bound Analysis (EBA). On the other hand, the findings of the causality analysis are particularly heterogeneous. The vector autoregression (VAR) and the vector error correction model (VECM) approaches identify unidirectional Granger causality running either from OFDI to GDP or from GDP to OFDI in six countries. In four economies causality among the two variables is bidirectional, whereas in five countries no causality relationship between OFDI and GDP seems to be present.

Uncertainty of aid inflows and the growth relationship

This paper contributes to the literature on aid and economic growth. We posit that it is not the levei of aid flows per se but the stability of such flows that determines the impact of aid on economic growth. Three measures of aid instability are employed. One is a simple deviation from trend, and measures overall instability. The other measures are based on auto-regressive estimates to capture deviations from an expected trend. These measures are intended to proxy for uncertainty in aid receipts. We posit that such uncertainty will influence the relationship between aid and investment and how recipient governments respond to aid, and will therefore affect how aid impacts on growth. We estimate a standard cross-country growth regression including the leveI of aid, and find aid to be insignificant (in line with other results in the literature). We then introduce measures of instability. Aid remains insignificant when we account for overall instability. However, when we account for uncertainty (which is negative and significant), we find that aid has a significant positive effect on growth. We conduct stability tests that show that the significance of aid is largely due to its effect on the volume of investment. The finding that uncertainty of aid receipts reduces the effectiveness of aid is robust. When we control for this, aid appears to have a significant positive influence on growth. When the regression is estimated for the sub-sample of African countries these findings hold, although the effectiveness of aid appears weaker than for the full sample.