Shrinkage Estimation Strategies in Generalised Ridge Regression Models: Low/High-Dimension Regime

In this study, we suggest pretest and shrinkage methods based on the generalised ridge regression estimation that is suitable for both multicollinear and high-dimensional problems. We review and develop theoretical results for some of the shrinkage estimators. The relative performance of the shrinkage estimators to some penalty methods is compared and assessed by both simulation and real-data analysis. We show that the suggested methods can be accounted as good competitors to regularisation techniques, by means of a mean squared error of estimation and prediction error. A thorough comparison of pretest and shrinkage estimators based on the maximum likelihood method to the penalty methods. In this paper, we extend the comparison outlined in his work using the least squares method for the generalised ridge regression.     

Climate change adaptation actions by fish farmers: evidence from the Niger Delta Region of Nigeria

This paper examined climate change adaptation strategies in fish farming and the effect of such methods on the profit of fish farmers in the Niger Delta region of Nigeria, Africa’s most populous country. Using cross‐sectional data obtained from 420 fish farmers from the region and applying multivariate probit and instrumental variable regressions, the study found that fish farmers have adopted a broad range of strategies to address climate risk and that these have significantly increased farmers’ profit. Our findings indicated important relationships between certain farm, socio‐economic and institutional characteristics and the adaptation actions. The study provides useful insight into factors that potentially encourage the adoption of livelihood‐enhancing climate risk adaptation strategies by fish farmers in the Niger Delta region and similar contexts.     

Factors Influencing Organic and Fair Trade Coffee Growers Level of Engagement with Cooperatives: The Case of Coffee Farmers in Mexico

We study the factors influencing the percentage of organic and fair trade certified coffee sold through a cooperative by growers of five cooperatives in Mexico. The percentage of coffee sold through the cooperative was used as a proxy of growers’ engagement with a cooperative. Using factor analysis and a fractional probit regression, we evaluated the proposition that the level of engagement can be explained by transaction cost economics, social norms and connections, and farmer and farm business characteristics. We found that farm size, uncertainty regarding cooperative time of payment to the members and cooperative commitment on price to be paid negatively influence the level of engagement. In contrast, asset specificity, relational commitment, and price have a positive impact on engagement. Our results may help cooperatives and policy makers to build strategies aiming to increase this level of engagement. This is relevant because lower grower engagement has been found to be positively correlated with weak performing cooperatives.     

A new multiscale decomposition ensemble approach for forecasting exchange rates

Due to the high complexity and strong nonlinearity nature of foreign exchange rates, how to forecast foreign exchange rate accurately is regarded as a challenging research topic. Therefore, developing highly accurate forecasting method is of great significance to investors and policy makers. A new multiscale decomposition ensemble approach to forecast foreign exchange rates is proposed in this paper. In the approach, the variational mode decomposition (VMD) method is utilized to divide foreign exchange rates into a finite number of subcomponents; the support vector neural network (SVNN) technique is used to model and forecast each subcomponent respectively; another SVNN technique is utilized to integrate the forecasting results of each subcomponent to generate the final forecast results. To verify the superiority of the proposed approach, four major exchange rates were chosen for model comparison and evaluation. The experimental results indicate that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach outperforms some other benchmarks in terms of forecasting accuracy and statistical tests. This demonstrates that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach is promising for forecasting foreign exchange rates.     

不同利率对我国上市银行信用风险的影响

对我国16家上市银行进行的实证检验表明：3个月同业拆借利率与银行信用风险显著正相关，人民币5年期基准存款利率与银行信用风险的关系不确定，人民币1年期基准存款利率与银行信用风险显著正相关，贷款利息收入占贷款比例与银行信用风险显著负相关。     

A Geometrical Interpretation of Collinearity: A Natural Way to Justify Ridge Regression and Its Anomalies

Justifying ridge regression from a geometrical perspective is one of the main contributions of this paper. To the best of our knowledge, this question has not been treated previously. This paper shows that ridge regression is a particular case of raising procedures that provide greater flexibility by transforming the matrix X associated with the model. Thus, raising procedures, based on a geometrical idea of the vectorial space associated with the columns of matrix X , lead naturally to ridge regression and justify the presence of the well-known constant k on the main diagonal of matrix X ^′ X . This paper also analyses and compares different alternatives to raising with respect to collinearity mitigation. The results are illustrated with an empirical application.     

Reluctant Generalised Additive Modelling

Sparse generalised additive models (GAMs) are an extension of sparse generalised linear models that allow a model's prediction to vary non-linearly with an input variable. This enables the data analyst build more accurate models, especially when the linearity assumption is known to be a poor approximation of reality. Motivated by reluctant interaction modelling, we propose a multi-stage algorithm, called reluctant generalised additive modelling (RGAM), that can fit sparse GAMs at scale. It is guided by the principle that, if all else is equal, one should prefer a linear feature over a non-linear feature. Unlike existing methods for sparse GAMs, RGAM can be extended easily to binary, count and survival data. We demonstrate the method's effectiveness on real and simulated examples.     

Separate waste collection in mountain municipalities. A case study in Campania

This paper aims to analyse the impact of land characteristics (i.e. altimetry and housing dispersion) on separate waste collection, with a focus on mountain municipalities. The high spatial heterogeneity of the driving factors of separate waste collection allows traditional techniques, such as OLS, to offer only a partial depiction of the situation, missing important information. In this view, we perform the Geographically Weighted Regression (GWR), which allows to control for the local determinants of waste management. Our data cover 550 municipalities of Campania, in Southern Italy. We control for a set of morphological and socio-economic variables, drawn from official records for 2012. Our results show that the relationships between land characteristics and separate waste collection are not constant over space. Moreover, they suggest that in the presence of non-modifiable factors (such as land characteristics) local governments should act on citizen motivations, promoting awareness on environmental issues, and should implement time-saving collection methods.     

How efficient airways act as role models and in what dimensions? A superefficiency DEA model enhanced by social network analysis

In this empirical study, a five-stage methodology is used to examine the efficiency of 45 worldwide known airline companies from the financial, operation and marketing perspectives. Initially, the superefficient data envelopment model is run with inputs and outputs that are selected based on the literature review. However, because 21 out of 45 airline companies are found to be efficient based on this analysis, a stepwise regression-based mechanism is applied to four reduced models – one for each output variable – for better discrimination. The outputs are, namely, net profit margin (financial output), passengers carried, on-time departure performance (operational outputs), and customer satisfaction (marketing output). In this way, the significant input variables are found for each reduced model. In the third stage, in order to provide even more discrimination, social network-based eigenvector centrality values are used as the weights of the superefficiency scores, and the strengths and weaknesses of efficient airlines for each output are specified in terms of their related significant inputs. The results show that, when net profit margin is taken as an output, Vietnam Airlines has the top weighted superefficiency value and excels in terms of available seat kilometers and liquidity, but it should improve its debt level. Although Norwegian Airlines has the highest efficiency with respect to debt level, it is not the best role model because its eigenvector centrality value is relatively low. However, Norwegian airlines also has the highest weighted superefficiency and acts as a role model in terms of on-time departures with respect to this output. Its main strength is liquidity, and it has no significant weaknesses. On the other hand, in terms of overall satisfaction and passengers carried, Vietnam Airlines and Thai Airways are the leaders, respectively. Vietnam Airlines is the only superefficient company with respect to overall satisfaction, while the basic strengths of Thai Airways in terms of passengers carried are its employee and fleet, and it has no significant weakness. A final aggregation of the results is made by making pairwise comparisons of the relative importance of four outputs for 7 experts selected from different departments of airline companies. According to the results, Net Profit Margin has the highest priority, followed by On-time Departure and Overall Customer Satisfaction, while passengers carried has the lowest importance. Based on these relative priorities, it can be said that Vietnam Airlines can be accepted as the top performing airline company, followed by Norwegian Airlines.     

Threshold effect of economic openness on bank risk-taking: Evidence from emerging markets

This paper investigates the non-linear effects of two aspects of economic openness, namely, trade openness and financial openness, on banking system stability. We use a panel of 42 emerging markets from 2000 to 2014 to test whether bank risk-taking behaviour varies with the level of openness. We find that a higher degree of trade openness promotes bank stability linearly. Conversely, the non-linear effect of financial openness on bank risk-taking is evident. When the financial system is not sufficiently open, the impact of financial openness on bank stability is insignificant. However, as the domestic financial market becomes more open, financial openness can help discipline the behaviour of banks, making them more stable. We also find evidence that these effects are transmitted through the market discipline channel. Our findings highlight the importance of strengthening the domestic regulatory framework and transparency as the economy becomes more integrated.