Regulação ótima sob agência comum e risco de corrupção

Mostramos que separar reguladores não benevolentes é a resposta institucional ótima quando os projetos regulados são grandes , i.e., apresentam benefício mar- ginal alto. Como separação impede que os reguladores se coordenem para se apro- priar de toda a renda informacional do agente quando sabem o tipo desse último, há um trade-o¤ entre poupança de renda informacional e e ciência alocativa, pois o jogo entre os reguladores induzido pela separação gera distorção em relação à alocação sob um único regulador. Quando a renda informacional em questão é grande, vale a pena a divisão de reguladores.

A study on time-varying quantile and its applications

This Thesis is the result of my Master Degree studies at the Graduate School of Economics, Getúlio Vargas Foundation, from January 2004 to August 2006. am indebted to my Thesis Advisor, Professor Luiz Renato Lima, who introduced me to the Econometrics' world. In this Thesis, we study time-varying quantile process and we develop two applications, which are presented here as Part and Part II. Each of these parts was transformed in paper. Both papers were submitted. Part shows that asymmetric persistence induces ARCH effects, but the LMARCH test has power against it. On the other hand, the test for asymmetric dynamics proposed by Koenker and Xiao (2004) has correct size under the presence of ARCH errors. These results suggest that the LM-ARCH and the Koenker-Xiao tests may be used in applied research as complementary tools. In the Part II, we compare four different Value-at-Risk (VaR) methodologies through Monte Cario experiments. Our results indicate that the method based on quantile regression with ARCH effect dominates other methods that require distributional assumption. In particular, we show that the non-robust method ologies have higher probability to predict VaRs with too many violations. We illustrate our findings with an empirical exercise in which we estimate VaR for returns of São Paulo stock exchange index, IBOVESPA, during periods of market turmoil. Our results indicate that the robust method based on quantile regression presents the least number of violations.

Forecasting accuracy and estimation uncertainty using VAR models with short- and long-term economic restrictions: a Monte-Carlo study

Using vector autoregressive (VAR) models and Monte-Carlo simulation methods we investigate the potential gains for forecasting accuracy and estimation uncertainty of two commonly used restrictions arising from economic relationships. The Örst reduces parameter space by imposing long-term restrictions on the behavior of economic variables as discussed by the literature on cointegration, and the second reduces parameter space by imposing short-term restrictions as discussed by the literature on serial-correlation common features (SCCF). Our simulations cover three important issues on model building, estimation, and forecasting. First, we examine the performance of standard and modiÖed information criteria in choosing lag length for cointegrated VARs with SCCF restrictions. Second, we provide a comparison of forecasting accuracy of Ötted VARs when only cointegration restrictions are imposed and when cointegration and SCCF restrictions are jointly imposed. Third, we propose a new estimation algorithm where short- and long-term restrictions interact to estimate the cointegrating and the cofeature spaces respectively. We have three basic results. First, ignoring SCCF restrictions has a high cost in terms of model selection, because standard information criteria chooses too frequently inconsistent models, with too small a lag length. Criteria selecting lag and rank simultaneously have a superior performance in this case. Second, this translates into a superior forecasting performance of the restricted VECM over the VECM, with important improvements in forecasting accuracy ñreaching more than 100% in extreme cases. Third, the new algorithm proposed here fares very well in terms of parameter estimation, even when we consider the estimation of long-term parameters, opening up the discussion of joint estimation of short- and long-term parameters in VAR models.

The welfare cost of macroeconomic uncertainty in the post-war period

Lucas (1987) has shown the surprising result that the welfare cost of business cycles is quite small. Using standard assumptions on preferences and a fully-áedged econometric model we computed the welfare costs of macroeconomic uncertainty for the post-WWII era using the multivariate Beveridge-Nelson decomposition for trends and cycles, which considers not only business-cycle uncertainty but also uncertainty from the stochastic trend in consumption. The post-WWII period is relatively quiet, with the welfare costs of uncertainty being about 0:9% of per-capita consumption. Although changing the decomposition method changed substantially initial results, the welfare cost of uncertainty is qualitatively small in the post-WWII era - about $175.00 a year per-capita in the U.S. We also computed the marginal welfare cost of macroeconomic uncertainty using this same technique. It is about twice as large as the welfare cost ñ$350.00 a year per-capita.

Public debt sustainability and endogenous seignorage in Brazil: time-series evidence from 1947-92

Using national accounts data for the revenue-GDP and expenditure GDP ratios from 1947 to 1992, we examine two central issues in public finance. First, was the path of public debt sustainable during this period? Second, if debt is sustainable, how has the government historically balanced the budget after hocks to either revenues or expenditures? The results show that (i) public deficit is stationary (bounded asymptotic variance), with the budget in Brazil being balanced almost entirely through changes in taxes, regardless of the cause of the initial imbalance. Expenditures are weakly exogenous, but tax revenues are not;(ii) a rational Brazilian consumer can have a behavior consistent with Ricardian Equivalence (iii) seignorage revenues are critical to restore intertemporal budget equilibrium, since, when we exclude them from total revenues, debt is not sustainable in econometric tests.

A panel data approach to economic forecasting: the bias-corrected average forecast

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the (feasible) bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to other techniques in several contexts. In particular, it is asymptotically equivalent to the conditional expectation, i.e., has an optimal limiting mean-squared error. We also develop a zeromean test for the average bias and discuss the forecast-combination puzzle in small and large samples. Monte-Carlo simulations are conducted to evaluate the performance of the feasible bias-corrected average forecast in finite samples. An empirical exercise based upon data from a well known survey is also presented. Overall, theoretical and empirical results show promise for the feasible bias-corrected average forecast.

Growth, increasing returns, and public infrastructure : time series evidence

Initial endogenous growth models emphasized the importance of external effects and increasing retums in explaining growth. Empirically, this hypothesis can be confumed if the coefficient of physical capital per hour is unity in the aggregate production function. Previous estimates using time series data rejected this hypothesis, although cross-country estimates did nol The problem lies with the techniques employed, which are unable to capture low-frequency movements of high-frequency data. Using cointegration, new time series evidence confum the theory and conform to cross-country evidence. The implied Solow residual, which takes into account externaI effects to aggregate capital, has its behavior analyzed. The hypothesis that it is explained by government expenditures on infrasttucture is confIrmed. This suggests a supply-side role for government affecting productivity.

A panel data approach to economic forecasting: the bias-corrected average forecast

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to other techniques in several contexts. In particular it delivers a zero-limiting mean-squared error if the number of forecasts and the number of post-sample time periods is sufficiently large. We also develop a zero-mean test for the average bias. Monte-Carlo simulations are conducted to evaluate the performance of this new technique in finite samples. An empirical exercise, based upon data from well known surveys is also presented. Overall, these results show promise for the bias-corrected average forecast.

A panel data approach to economic forecasting: the bias-corrected average forecast

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the (feasible) bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to other techniques in several contexts. In particular, it is asymptotically equivalent to the conditional expectation, i.e., has an optimal limiting mean-squared error. We also develop a zeromean test for the average bias and discuss the forecast-combination puzzle in small and large samples. Monte-Carlo simulations are conducted to evaluate the performance of the feasible bias-corrected average forecast in finite samples. An empirical exercise, based upon data from a well known survey is also presented. Overall, these results show promise for the feasible bias-corrected average forecast.

Um experimento para o modelo do holdout do vendedor

Essa dissertação apresenta uma análise experimental do modelo de holdout apresentado em Menezes e Pitchford (2004), no qual o aumento na complementeri- dade entre os bens dos vendedores implica maior probabilidade de ocorrência de holdout, ou atraso do vendedor, na negociação entre os vendedores e um com- prador. O comportamento observado no laboratório corrobora essa previsão do modelo teórico. Observou-se, ainda, que os jogadores com maiores ganhos no ex- perimento atrasaram menos a entrada na negociação.

Prevendo inflação usando séries temporais e combinações de previsões

O propósito deste estudo é analisar a capacidade dos modelos econométricos ARMA, ADL, VAR e VECM de prever inflação, a fim de verificar qual modelagem é capaz de realizar as melhores previsões num período de até 12 meses, além de estudar os efeitos da combinação de previsões. Dentre as categorias de modelos analisados, o ARMA (univariado) e o ADL (bivariado e multivariado), foram testados com várias combinações de defasagens. Foram realizadas previsões fora-da-amostra utilizando 3 períodos distintos da economia brasileira e os valores foram comparados ao IPCA realizado, a fim de verificar os desvios medidos através do EQM (erro quadrático médio). Combinações das previsões usando média aritmética, um método de média ponderada proposto por Bates e Granger (1969) e média ponderada através de regressão linear múltipla foram realizadas. As previsões também foram combinadas com a previsão do boletim FOCUS do Banco Central. O método de Bates e Granger minimiza a variância do erro da combinação e encontra uma previsão com variância do erro menor ou igual à menor variância dos erros das previsões individuais, se as previsões individuais forem não viesadas. A conclusão é que, com as técnicas de séries temporais utilizadas, alguns modelos individuais fornecem previsões com EQM relativamente baixos. Destacando-se, dentre eles, os modelos VAR e VECM. Porém, com a combinação de previsões os EQM obtidos são menores do que os das previsões individuais usadas para combinação. Na maioria dos casos, a combinação de previsões com o boletim FOCUS também melhorou significativamente os resultados e forneceu previsões com EQM menores do que os das previsões individuais, destacando-se, dentre os métodos de combinações utilizados, a combinação via regressão linear múltipla.

Técnicas econométricas para a delimitação de mercados relevantes geográficos, aplicação para a petroquímica

Este trabalho apresenta técnicas econométricas de análise de séries temporais que permitem testar, empiricamente, hipóteses sobre a definição da dimensão geográfica de mercados relevantes. Estas técnicas são aplicadas ao mercado brasileiro de resinas termoplásticas polietilenos e polipropileno com o objetivo de obter subsídios para a correta caracterização de sua dimensão geográfica. Os resultados obtidos adicionam evidências no sentido de que estes mercados relevantes geográficos podem ser definidos como internacionais. A técnica da cointegração indica que existe uma relação estável de longo prazo entre os preços das resinas produzidas domesticamente e das resinas internalizadas. Desvios desta relação têm caráter transitório. Por intermédio da análise da decomposição de variância percebe-se que os erros de previsão dos valores das resinas domésticas passam a ser rapidamente explicados pelas variações ocorridas nos preços das resinas internalizadas, sendo que o oposto não ocorre, ou seja, os preços das resinas internalizadas são explicados primordialmente por seus próprios desvios. Por fim, a Técnica da causalidade de Granger aponta que apenas o preço das resinas internalizadas causa , no sentido de Granger, o preço das resinas domésticas. O oposto não é valido e as variações nos preços das resinas domésticas não adicionam capacidade de previsão dos preços das resinas internalizadas.