Absorptive capacity facilitates adaptation to novel environmental disasters

Absorptive capacity–the ability to learn and apply external knowledge and information to acquire material resources–is an essential but overlooked driver in community adaptation to new and unprecedented disasters. We analyzed data from a representative random sample of 603 individuals from 25 coastal communities in Louisiana affected by the Deepwater Horizon oil spill. We used simultaneous equation models to assess the relationship between absorptive capacity and resource acquisition for affected individuals after the disaster. Results show that the diversity of individuals’ prior knowledge coupled with the community’s external orientation and internal cohesion facilitate resource use. They go beyond simply providing resources and demonstrate individual and community features necessary for absorbing information and knowledge and help devise adaptation strategies to address the dynamics of changing economic, social, and political environment after the disaster.

Estimating Simultaneous Equation Models through an Entropy-Based Incremental Variational Bayes Learning Algorithm

The presence of unaccounted heterogeneity in simultaneous equation models (SEMs) is frequently problematic in many real-life applications. Under the usual assumption of homogeneity, the model can be seriously misspecified, and it can potentially induce an important bias in the parameter estimates. This paper focuses on SEMs in which data are heterogeneous and tend to form clustering structures in the endogenous-variable dataset. Because the identification of different clusters is not straightforward, a two-step strategy that first forms groups among the endogenous observations and then uses the standard simultaneous equation scheme is provided. Methodologically, the proposed approach is based on a variational Bayes learning algorithm and does not need to be executed for varying numbers of groups in order to identify the one that adequately fits the data. We describe the statistical theory, evaluate the performance of the suggested algorithm by using simulated data, and apply the two-step method to a macroeconomic problem.

Estimation of Multilevel Simultaneous Equation Models through Genetic Algorithms

Problems in estimating simultaneous equation models when error terms are not intertemporally uncorrelated has motivated the introduction of a new multivariate model referred to as Multilevel Simultaneous Equation Model (MSEM). The maximum likelihood estimation of the parameters of an MSEM has been set forth. Because of the difficulties associated with the solution of the system of likelihood equations, the maximum likelihood estimator cannot be obtained through exhaustive search procedures. A hybrid metaheuristic that combines a genetic algorithm and an optimization method has been developed to overcome both technical and analytical limitations in the general case when the covariance structure is unknown. The behaviour of the hybrid metaheuristic has been discussed by varying different tuning parameters. A simulation study has been included to evaluate the adequacy of this estimator when error terms are not serially independent. Finally, the performance of this estimation approach has been compared with regard to other alternatives.

Multivariate General Spatial Three-Stage Least Squares Fixed Effect Panel Simultaneous Models and Estimation of Their Parameters

Simultaneous equation models describe a two-way flow of influence among variables. Simultaneous equation models using panel data, especially for fixed effect where there are spatial autoregressive and spatial errors with exact solutions, still require to be developed. In this paper, we develop the new models that it consist of spatial autoregressive and spatial errors. We call it as general spatial. This paper proposes feasible generalized least squares-three-stage least squares (FGLS-3SLS) to find all the estimators with exact solution and the numerical approximation estimators by concentrated log-likelihood formulation with method of forming sequence. All proposed estimators especially for closed-form estimators are proved to be consistent.

ENERGY, ECONOMIC GROWTH, INEQUALITY, AND POVERTY IN IRAN

This paper examines the relationships among energy consumption, economic growth, inequality, and poverty in Iran. We estimate these relationships at both the aggregate and sectoral level using instrumental variables to address endogeneity and simultaneous equation models to enhance efficiency. Results show that decreasing inequality will be beneficial for economic growth, poverty alleviation and energy access. Inequality can negatively affect GDP directly, as well as indirectly through its negative effect on energy consumption. Similarly, inequality can increase poverty both directly as well as indirectly through its negative effect on energy consumption. We also find that increasing energy consumption has multiple benefits: it increases GDP, tends to decrease inequality and decreases poverty. Energy consumption decreases poverty both directly as well as indirectly via its effect on decreasing inequality. Our results therefore suggest that policies to improve energy access are important, and will have the benefits of increasing GDP, decreasing inequality and decreasing poverty.

Improving " Jackknife Instrumental Variable Estimation method" using A class of immun algorithm with practical application

Most of the robust methods based on the idea of ​​sacrificing one side versus promotion of another, the artificial intelligence mechanisms try to balance weakness and strength to make the best solutions in a random search technique. In this paper, a new idea is introduced to improve the estimators of parameters of linear simultaneous equation models that resulting from the Jackknife Instrumental Variable Estimation method (JIVE) by using a class of immune algorithm which called Clonal Selection Algorithm (CSA) and better estimates are obtained using one of the robust criterion which is called Mean Absolut Percentage Error (MAPE). The success of intelligence algorithm mechanisms has been proven that used to improve the parameters of linear simultaneous equation models according to user criterion and real data of size n=48.