Implications of the Modigliani-Miller Theorem for the Study of Exchange Rate Regimes

We extend the Modigliani-Miller Theorem to the composition of the public debt and show that in a deterministic model the structure of a government's assets and liabilities is undetermined. Hence, a floating exchange rate regime can implement any attainable competitive equilibrium. Concerning stochastic economies, if the government issues nominal bonds of several maturities, then the same result may hold. Thus, a conceivable link between the choice of an exchange rate regime and economic outcomes may be due to factors often not considered in standard macroeconomic models.Modigliani-Miller Theorem, exchange rate regime, indeterminacy

Political competition and the limits of political compromise

We consider an economy where competing political parties alternate in office. Due to rent-seeking motives, incumbents have an incentive to set public expenditures above the socially optimum level. Parties cannot commit to future policies, but they can forge a political compromise where each party curbs excessive spending when in office if they expect future governments to do the same. We find that, if the government cannot manipulate state variables, more intense political competition fosters a compromise that yields better outcomes, potentially even the first best. By contrast, if the government can issue debt, vigorous political competition can render a compromise unsustainable and drive the economy to a low-welfare, high-debt, long-run trap. Our analysis thus suggests a legislative trade-off between restricting political competition and constraining the ability of governments to issue debt

Crescimento Econômico, Mercados e Primado das Leis: Breves Considerações sobre o Caso Brasileiro

The existence of markets that operate smoothly is an important factor influencing a nation’s economic growth. Furthermore, an effective judicial system is a necessary condition for markets to function well. Therefore, it is reasonable to expect a positive relationship to exist between income per capita and adhesion to the rule of law. This conjecture is corroborated here in a sample of 110 countries in 2016. Additionally, the data show that the rule of law is relatively weak in Brazil, suggesting that an improvement of the Brazilian judicial system is a potential growth-enhancing reform.A existência de mercados que operem de forma adequada é um importante fator para a determinação do crescimento econômico de uma nação. Por outro lado, um sistema judicial eficaz é uma condição necessária para o bom funcionamento dos mercados. Assim sendo, é de se esperar que exista uma relação crescente entre a renda per capita e o grau de prevalência do primado das leis (rule of law em inglês). Essa conjectura é ratificada em uma amostra de 110 países para o ano de 2016. Adicionalmente, os dados mostram que o grau de primado das leis é relativamente baixo no Brasil. Essas conclusões sugerem que uma reforma do sistema judicial brasileiro tem o potencial de estimular o crescimento econômico do país

Combining artificial neural networks and evolution to solve multiobjective knapsack problems

The multiobjective knapsack problem (MOKP) is a combinatorial problem that arises in various applications, including resource allocation, computer science and finance. Evolutionary multiobjective optimization algorithms (EMOAs) can be effective in solving MOKPs. Though, they often face difficulties due to the loss of solution diversity and poor scalability. To address those issues, our study [2] proposes to generate candidate solutions by artificial neural networks. This is intended to provide intelligence to the search. As gradient-based learning cannot be used when target values are unknown, neuroevolution is adapted to adjust the neural network parameters. The proposal is implemented within a state-of-the-art EMOA and benchmarked against traditional search operators base on a binary crossover. The obtained experimental results indicate a superior performance of the proposed approach. Furthermore, it is advantageous in terms of scalability and can be readily incorporated into different EMOAs.(undefined

Neuroevolution for solving multiobjective knapsack problems

The multiobjective knapsack problem (MOKP) is an important combinatorial problem that arises in various applications, including resource allocation, computer science and finance. When tackling this problem by evolutionary multiobjective optimization algorithms (EMOAs), it has been demonstrated that traditional recombination operators acting on binary solution representations are susceptible to a loss of diversity and poor scalability. To address those issues, we propose to use artificial neural networks for generating solutions by performing a binary classification of items using the information about their profits and weights. As gradient-based learning cannot be used when target values are unknown, neuroevolution is adapted to adjust the neural network parameters. The main contribution of this study resides in developing a solution encoding and genotype-phenotype mapping for EMOAs to solve MOKPs. The proposal is implemented within a state-of-the-art EMOA and benchmarked against traditional variation operators based on binary crossovers. The obtained experimental results indicate a superior performance of the proposed approach. Furthermore, it is advantageous in terms of scalability and can be readily incorporated into different EMOAs.Portuguese “Fundação para a Ciência e Tecnologia” under grant PEst-C/CTM/LA0025/2013 (Projecto Estratégico - LA 25 - 2013-2014 - Strategic Project - LA 25 - 2013-2014