Democracy and environmental quality

We develop and estimate an econometric model of the relationship between several local and global air pollutants and economic development while allowing for critical aspects of the socio-political-economic regime of a State. We obtain empirical support for our hypothesis that democracy and its associated freedoms provide the conduit through which agents can exercise their preferences for environmental quality more effectively than under an autocratic regime, thus leading to decreased concentrations or emissions of pollution. However, additional factors such as income inequality, age distribution, education, and urbanization may mitigate or exacerbate the net effect of the type of political regime on pollution, depending on the underlying societal preferences and the weights assigned to those preferences by the State.     

Measuring Vulnerability to U.S. Foreign Economic Sanctions

Economic sanctions are an important instrument of U.S. foreign policy. While politicians look favorably on unilateral economic sanctions as a policy instrument, many scholars attribute significant long-term economic costs to the United States and have doubts about their effectiveness. We outline a simple approach to assess the vulnerability of target countries to sanctions in order to develop focused sanctions and reduce unnecessary costs to U.S. business, avoid the imposition of sanctions on countries when sanctions are unlikely to have the desired effects, and determine what other countries must join the United States in imposing sanctions if they are to be effective.JEL Classification K330,F190     

Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange

A new method for forecasting the trend of time series, based on mixture of MLP experts, is presented. In this paper, three neural network combining methods and an Adaptive Network-Based Fuzzy Inference System (ANFIS) are applied to trend forecasting in the Tehran stock exchange. There are two experiments in this study. In experiment I, the time series data are the Kharg petrochemical company’s daily closing prices on the Tehran stock exchange. In this case study, which considers different schemes for forecasting the trend of the time series, the recognition rates are 75.97%, 77.13% and 81.64% for stacked generalization, modified stacked generalization and ANFIS, respectively. Using the mixture of MLP experts (ME) scheme, the recognition rate is strongly increased to 86.35%. A gain and loss analysis is also used, showing the relative forecasting success of the ME method with and without rejection criteria, compared to a simple buy and hold approach. In experiment II, the time series data are the daily closing prices of 37 companies on the Tehran stock exchange. This experiment is conducted to verify the results of experiment I and to show the efficiency of the ME method compared to stacked generalization, modified stacked generalization and ANFIS.     

Conditions for Sustainable Optimal Economic Development

This paper shows that, for dynamic optimizing economies with different types of natural resource, environmental, and human‐made capital stocks, a necessary and sufficient condition for permanently sustaining an optimal utility/consumption level is the stationarity of the current‐value Hamiltonian. For economies whose development is not exogenously and directly affected by time (i.e., time‐autonomous economies), this stationarity condition generalizes Dixit et al.’s (1980 ) “zero‐net‐aggregate‐investment” rule of sustain‐ability, which in turn generalizes Solow‐Hartwick’s sustainability rule. For non‐autonomous economies, the stationarity condition is not generally fulfilled, and the current‐value Hamiltonian under (over) estimates the true welfare level by an amount equal to the discounted value of the net “pure time effect.” For the non‐autonomous case of a time‐dependent utility discount rate, a general condition on the discount rate function (of which the hyperbolic discount rate function is a special case) upholds the results obtained for autonomous cases. The paper concludes with a discussion of policies that promote both optimality and sustainability objectives.     

A COMPLETE THEORY OF COMPARATIVE STATICS FOR DIFFERENTIABLE OPTIMIZATION PROBLEMS

A new comparative statics formalism using generalized compensated derivatives is presented that, in contrast to existing methodologies, directly yields constraint‐free semidefiniteness results for any differentiable, constrained optimization problem. The formalism provides a natural and powerful method of constructing comparative statics results, free of constraints and unrestricted in scope. New results on envelope relations, invariance conditions, rank inequalities and non‐uniqueness are derived that greatly extend their utility and reach. The methodology is illustrated by deriving the comparative statics of multiple linear constraint utility maximization models and the principal‐agent problem with hidden actions, both highly nontrivial and hitherto unsolved problems.     

Forecasting UK consumer price inflation using inflation forecasts

The inflation rate is a key economic indicator for which forecasters are constantly seeking to improve the accuracy of predictions, so as to enable better macroeconomic decision making. Presented in this paper is a novel approach which seeks to exploit auxiliary information contained within inflation forecasts for developing a new and improved forecast for inflation by modeling with Multivariate Singular Spectrum Analysis (MSSA). Unlike other forecast combination techniques, the key feature of the proposed approach is its use of forecasts, i.e. data into the future, within the modeling process and extracting auxiliary information for generating a new and improved forecast. We consider real data on consumer price inflation in UK, obtained via the Office for National Statistics. A variety of parametric and nonparametric models are then used to generate univariate forecasts of inflation. Thereafter, the best univariate forecast is considered as auxiliary information within the MSSA model alongside historical data for UK consumer price inflation, and a new multivariate forecast is generated. We find compelling evidence which shows the benefits of the proposed approach at generating more accurate medium to long term inflation forecasts for UK in relation to the competing models. Finally, through the discussion, we also consider Google Trends forecasts for inflation within the proposed framework.     

A Hybrid Fuzzy-Probabilistic Bargaining Approach for Multi-objective Optimization of Contamination Warning Sensors in Water Distribution Systems

Group Decision and Negotiation - Water Distribution System (WDS) are strategic infrastructures in all countries. In recent decades, several optimization-based frameworks have been developed to...     

Teaching courses in macroeconomics and monetary policy with Bloomberg analytics

In this article, the authors illustrate the use of Bloomberg for analyzing topics in macroeconomics and monetary policy in economics and finance courses. The hands-on experience that students gain from such a course has many benefits, including deeper learning and clearer understanding of data. The authors describe goals and learning objectives, then compare Bloomberg with Federal Reserve Economic Data (FRED). In addition, they provide examples of how to use Bloomberg in the classroom, describe how to have students perform sector analysis, show how Bloomberg tools are useful for analyzing monetary policy, discuss how to use Bloomberg to analyze the financial sector, and illustrate the platform’s use in a case study.