A Roller Coaster Ride: An Empirical Investigation of the Main Drivers of Wheat Price

Over the last decade, commodity prices have registered substantial booms and busts marked by extreme volatility. Wheat in particular, one of the main non-oil commodities, has registered a roller-coaster in price levels which seems to be inconsistent with supply and demand fundamentals. To acutely investigate the drivers of wheat prices and quantify their impact, a Vector Error Correction Model (VECM) has been used. The exogenous variables have been distinguished into four groups: market-specific factors, broad macroeconomic determinants, speculative components, and weather variables. The quadriangulation of the determinants will enable us to better understand the movements in wheat price and identify the specific role of each component. The results show a mix of short and long term factors that are contributing to wheat price movements, and their effect should be taken into account in designing proper policy intervention to mitigate the negative impact of price shocks

Publisher Connection: Export-Led Growth in the UAE: Multivariate Causality Between Primary Exports, Manufactured Exports and Economic Growth

The principal question that this research addresses is the validity of the Export-Led Growth hypothesis (ELG) in the United Arab Emirates (UAE) over the period 1981–2012, focusing on the causality between primary exports, manufactured exports and economic growth. Unit root tests are applied to examine the time-series properties of the variables, while the Johansen cointegration test is performed to confirm or not the existence of a long-run relationship between the variables. Moreover, the multivariate Granger causality test and a modified version of Wald test are applied to examine the direction of the short-run and long-run causality respectively. The cointegration analysis reveals that manufactured exports contribute more to economic growth than primary exports in the long-run. In addition, this research provides evidence to support a bi-directional causality between manufactured exports and economic growth in the short-run, while the Growth-Led Exports (GLE) hypothesis is valid in the long-run for UAE

A Chance Constrained Programming Based Multi-Criteria Decision Making under Uncertainty

Multi-criteria decision making under uncertainty is a common practice followed in industries and academia. Among several types of uncertainty handling techniques, Chance Constrained Programming (CCP) is considered as an efficient and tractable approach provided one has accessibility to distribution of the data for uncertain parameters. However, the assumption that the uncertain parameters must follow some well-behaved probability distribution is a myth for most of the practical applications. This paper proposes a methodology to amalgamate machine learning algorithms with CCP and thereby make it data-driven. A novel fuzzy clustering mechanism is implemented to transcript the uncertain space such that the exact regions of uncertainty are identified. Subsequently, density based boundary point detection and Delaunay triangulation based boundary construction enable intelligent Sobol based sampling in these regions for use in CCP. The Fuzzy clustering mechanism used in the proposed method transforms the existing fuzzy C-means technique such that the decision variables are significantly reduced. This enables evolutionary optimizers to obtain better approximations of the uncertain space by identifying the true clusters. A highly nonlinear real life model for continuous casting from steelmaking industries is considered as a case study for testing the efficiency of data based CCP along with a comprehensive comparison between conventional CCP approach using box uncertainty set and proposed methodology. As the resulting CCP problem is multi-objective in nature, the Pareto solutions are obtained by NSGA II

Simultaneous knowledge discovery and development of smart neuro-fuzzy surrogates for online optimization of computationally expensive models

This work aims at enabling online optimization and control of computationally expensive models by employing Adaptive Neuro Fuzzy Inference System (ANFIS) as surrogates. ANFIS is governed by several parameters whose estimation based on heuristic assumptions degrade its efficiency. A novel surrogate building algorithm is thus proposed, with the aim of designing optimal ANFIS by balancing the aspects of over-estimation and prediction accuracy. It incorporates Sobol sampling plan and takes physics based model/data as the only input while estimating all other parameters simultaneously. A comparison between robust K-fold based Sample Size Determination (SSD) and an innovative fast Hypercube sampling based SSD technique is presented. Proposed algorithm fine-tunes the human experience which is often biased and thus prone to errors. It also enables the discovery of new knowledge from the existing information. ANFIS is built for industrially validated polymer reaction network model which made its optimization using NSGA-II, 9 times faster, thereby enhancing its scope for online implementation. It is then compared with another state-of-the-art surrogate, Kriging Interpolator, for an unbiased justification of robustness of the proposed algorithm

KERNEL: Enabler to Build Smart Surrogates for Online Optimization and Knowledge Discovery

KERNEL – A novel parameter free surrogate building algorithm using Adaptive Neuro Fuzzy Inference System (ANFIS) is presented to provide an intelligent and robust technology to optimally estimate the configuration of ANFIS along with Sobol based fast sample size determination methodology. The proposed algorithm is capable of fine-tuning the existing knowledge base about the physics of the process in terms of human experience. It also enables knowledge discovery through a multi-objective optimization problem solved by NSGA-II, thus presenting machine invented physics of the process. Experimentally validated polymerization reaction network model is considered and ANFIS surrogates are built using KERNEL. Surrogate based optimization was found to be 9 times faster than conventional optimization using the time expensive model thus enabling its online implementation. Comparison of ANFIS with Kriging is also included