This paper improves on the issues of extreme data points and heterogeneity found in the linear programming data envelopment analysis (DEA) by presenting a cluster-adjusted DEA model (DEA with cluster approach). This analysis, based on efficiency, determines the number of clusters via Gap statistic and Elbow methods. We use the December quarterly panel data consisting of 122 U.S agricultural banks across 37 states from 2000 to 2017 to estimate the cluster-adjusted DEA model. Empirical results show differences in the estimated DEA efficiency measures with and without a clustering approach. Furthermore, using non-parametric tests, the results of Ansari-Bradley, Kruskal Wallis, and Wilcoxon Rank Sum tests suggest that the cluster-adjusted DEA model provides statistically better efficiency measures in comparison to the DEA model without a clustering approach.

A sustainable energy system is key for addressing the world’s environmental and social challenges. The U.S is the second largest consumer of energy, with increased energy consumption in the previous half-century. To curb energy demand, it is essential to understand the relative price elasticities among the 4 main U.S energy consumption sectors; residential, industrial, commercial and transportation. The aim of this study is to present a theory-based comparative analysis of U.S sectoral energy price elasticities using the pooled mean group model. The speed of adjustment for the 4 sectors were -0.43, -0.41, -0.55 and -0.37, suggesting the existence of long-run relationships. The short-run own-price elasticities were -0.17, -0.39 and -0.27 for the commercial, industrial and residential sectors while the long-run own-price elasticities were -0.33, -0.45 and -0.20 for the commercial, residential and transportation sectors. We conclude that the residential sector readjusts to long-run equilibrium at a faster rate than the three other sectors. In the long run, this sector will yield a higher response to a price change. We suggest that price policies aimed at reducing energy demand should primarily target the residential sector, followed by the commercial and transportation sectors.

Limitations of the capital asset pricing model (CAPM) continue to present inconsistent empirical results despite CAPM’s firm mathematical foundations provided in numerous studies. In this article, the authors examine how estimation errors of the CAPM can be minimized using the cross-validation technique, a concept that is widely applied in machine learning (ML-CAPM). They apply their approach to test the assumption that the CAPM is a well-diversified portfolio model by using data from the S&P 500 and the Dow Jones Industrial Average. The results from the ML-CAPM validate both market indexes as well diversified, with statistically insignificant variation in unsystematic risks during and after the 2007 financial crisis. Furthermore, the ML-CAPM provides smaller root mean square error and mean absolute deviations compared to the traditional CAPM.

This paper contributes to the sparse debate on the effect of capital adequacy requirements on banks' economic efficiency measures. Precisely, we evaluate the out-of-sample predictability of capital adequacy requirements on banks' economic efficiency measures using Support Vector Regression (SVR) model with Linear, Polynomial and Radial Basis Function kernels and ordinary least squares (OLS) model. This analysis is important because a prediction of economic efficiency measures allows for an untangle view of bank's progress that is useful for management as it gains a high degree of transparency in the evaluation of future events. Our framework adapts optimization of h-block cross-validation to account for serial correlation of economic variables to produce robust sets of tuning parameters for SVR model. Using a total of 10,380 December quarterly observations of U.S Commercial and Domestic banks spanning from 2008 through 2019, empirical results show that SVR model provides better benchmarking in-sights in the evaluation of economic efficiency measures compared to the OLS model. Furthermore, in contrast to previous approaches identifying a single "best" model among competing models, the results of Model Confidence Test suggests that the out-of-sample forecasting confidently identifies superior predictive accuracy of SVR model-based forecasts over OLS model.