Testing,Estimation in GMM and CUE with Nearly-Weak Identification

In this article, we analyze Generalized Method of Moments (GMM) and Continuous Updating Estimator (CUE) with strong, nearly-weak, and weak identification. We show that with this mixed system, the limits of the estimators are nonstandard. In the subcase of GMM estimator with only nearly-weak instruments, the correlation between the instruments and the first order conditions decline at a slower rate than root T. We find an important difference between the nearly-weak case and the weak case. Inference with point estimates is possible with the Wald, likelihood ratio (LR), and Lagrange multiplier (LM) tests in GMM estimator with only nearly-weak instruments present in the system. The limit is the standard χ² limit. This is important from an applied perspective, since tests on the weak case do depend on the true value and can only test simple null. We also show this in the more realistic case of mixed type of strong, weak, and nearly-weak instruments, Anderson and Rubin (1949 Anderson , T. W. , Rubin , H. ( 1949 ). Estimation of the parameters of a single equation in a complete system of stochastic equations . Annals of Mathematical Statistics 20 : 46 – 63 .[Crossref] , [Google Scholar]) and Kleibergen (2005 Kleibergen , F. ( 2005 ). Testing parameters in GMM without assuming that they are identified . Econometrica Forthcoming . [Google Scholar]) type of tests are asymptotically pivotal and have χ² limit.     

Variable selection in linear regression analysis with alternative Bayesian information criteria using differential evaluation algorithm

In statistical analysis, one of the most important subjects is to select relevant exploratory variables that perfectly explain the dependent variable. Variable selection methods are usually performed within regression analysis. Variable selection is implemented so as to minimize the information criteria (IC) in regression models. Information criteria directly affect the power of prediction and the estimation of selected models. There are numerous information criteria in literature such as Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC). These criteria are modified for to improve the performance of the selected models. BIC is extended with alternative modifications towards the usage of prior and information matrix. Information matrix-based BIC (IBIC) and scaled unit information prior BIC (SPBIC) are efficient criteria for this modification. In this article, we proposed a combination to perform variable selection via differential evolution (DE) algorithm for minimizing IBIC and SPBIC in linear regression analysis. We concluded that these alternative criteria are very useful for variable selection. We also illustrated the efficiency of this combination with various simulation and application studies.     

Energy performance evaluation of OECD countries using Bayesian stochastic frontier analysis and Bayesian network classifiers

More recently a large amount of interest has been devoted to the use of Bayesian methods for deriving parameter estimates of the stochastic frontier analysis. Bayesian stochastic frontier analysis (BSFA) seems to be a useful method to assess the efficiency in energy sector. However, BSFA results do not expose the multiple relationships between input and output variables and energy efficiency. This study proposes a framework to make inferences about BSFA efficiencies, recognizing the underlying relationships between variables and efficiency, using Bayesian network (BN) approach. BN classifiers are proposed as a method to analyze the results obtained from BSFA.     

Kuznets Curve for the US: A Reconsideration Using Cosummability

Social Indicators Research - The relationship between income inequality and long-run economic growth has gained a growing attention in economic research for over decades. This study employed...     

Variable selection in gamma regression models via artificial bee colony algorithm

Variable selection is an important task in regression analysis. Performance of the statistical model highly depends on the determination of the subset of predictors. There are several methods to select most relevant variables to construct a good model. However in practice, the dependent variable may have positive continuous values and not normally distributed. In such situations, gamma distribution is more suitable than normal for building a regression model. This paper introduces an heuristic approach to perform variable selection using artificial bee colony optimization for gamma regression models. We evaluated the proposed method against with classical selection methods such as backward and stepwise. Both simulation studies and real data set examples proved the accuracy of our selection procedure.     

Human Rights,Income and International Migration

This study contributes to the literature of migration studies by addressing the question: why does international migration persist despite welfare improvements in migrant‐sending countries? We propose that the human rights condition of the origin countries is an important determinant of global migration. Although the human rights issue is not new to researchers in migration studies, the concern is primarily about the rights of migrants, refugees, asylum seekers or migrant workers in a host country. We undertake a bilateral panel data analysis to examine the pattern of global bilateral migration between 1995 and 2010. We find that international migration is positively associated with human rights conditions and income. Similar results are also obtained when we control for multilateral resistance and possible sample selection biases in a panel context. Our study implies that efforts to promote human rights may also be assessed in relation to their contribution to migration flows.     

Convex transformation on survival functions and related dependence concepts

Some concepts of stochastic dependence for continuous bivariate distribution functions are investigated by defining a convex transformation on their reliability or survival functions. We also study notions of bivariate hazard rate and hazard dependence. Some dependence orderings are characterized by using convex transformation. To clarify the discussions, illustrative examples are given.     

With or Without Forecast Sharing: Competition and Credibility under Information Asymmetry

Forecast sharing among trading partners lies at the heart of many collaborative and contractual supply chain management efforts. Even though it has been praised in both academic and practitioner circles for its critical role in increasing demand visibility, some concerns remain: The first one is related to the credibility of forecast sharing, and the second is the fear that it may turn into a competitive disadvantage and induce suppliers to increase their price offerings. In this study, we explore the validity of these concerns under a supply chain with a competitive upstream structure, focusing specifically on (i) when and how a credible forecast sharing can be sustainable, and (ii) how it impacts on the intensity of price competition. To address these issues, we develop a supply chain model with a buyer facing a demand risk and two heterogeneous suppliers competing for order allocation from the buyer. The extent of demand is known only to the buyer. The buyer submits a buying request to the suppliers via a commonly used procurement mechanism called request for quotation (RFQ). We consider two variants of RFQ. In the first type, the buyer simply shares the estimated order quantity with no further specifications. In the second one, in addition to this, the buyer also specifies minimum and/or maximum order quantities. We fully characterize equilibrium decisions and profits associated with them under symmetric and asymmetric information scenarios. Our main findings are that the buyer can use a RFQ with quantity restrictions as a credible signal for forecast sharing as long as the degree of demand information asymmetry is not too high, and that, contrary to above concerns, the equilibrium prices that emerge between competing suppliers under asymmetric information may indeed increase if the buyer cannot share forecast information credibly with its upstream partners.     

Inference in multivariate linear regression models with elliptically distributed errors

In this study we investigate the problem of estimation and testing of hypotheses in multivariate linear regression models when the errors involved are assumed to be non-normally distributed. We consider the class of heavy-tailed distributions for this purpose. Although our method is applicable for any distribution in this class, we take the multivariate t-distribution for illustration. This distribution has applications in many fields of applied research such as Economics, Business, and Finance. For estimation purpose, we use the modified maximum likelihood method in order to get the so-called modified maximum likelihood estimates that are obtained in a closed form. We show that these estimates are substantially more efficient than least-square estimates. They are also found to be robust to reasonable deviations from the assumed distribution and also many data anomalies such as the presence of outliers in the sample, etc. We further provide test statistics for testing the relevant hypothesis regarding the regression coefficients.     

Latino Muslims and Radical Extremism: Why There Is No Daesh (ISIS) Threat in Latin America

Despite much talk about infiltration of ISIS into Latin America and participation from Latino Muslims, there is no concrete evidence yet. Latin America still represents the safest continent on the earth from ISIS threat. When we look at the recruitment strategy of ISIS, social media comes as a surprisingly first way of attracting young people to its ideology. Latin American Muslims are extremely well connected to social media, through which many of them do not only learn about Islam; but also feel close to Arabic or Middle Eastern culture. Yet, the strong presence of Latino Muslims in online spaces did not yield a success attracting Latinos to ISIS via these channels. Are Latino Muslims immune from ISIS’s attractiveness? What makes them keep away from ISIS’s social media messages and recruitment strategies? This article argues that this can only be explained with the nature of the socio-political context in which young and social media addicted Latino Muslims live.