Dynamics of Price Transmission in the Presence of a Major Food Safety Shock: Impact of H5N1 Avian Influenza on the Turkish Poultry Sector

This article addresses the dynamic impact of the 2005 H5N1 avian influenza outbreak on the Turkish poultry sector. Contemporary time-series analyses with historical decomposition graphs are used to address differences in monthly price adjustments between market levels along the Turkish poultry supply channel. The empirical results show that price adjustments are asymmetric with respect to both speed and magnitude along the marketing channel. Results also reveal a differential impact of the exogenous shock on producers and retailers. The findings have critical efficiency and equity implications for the supply-chain participants.avian influenza, chicken, food safety shock, price transmission dynamics, supply chain, Turkey, Agribusiness, Demand and Price Analysis, Food Consumption/Nutrition/Food Safety, International Development, Livestock Production/Industries, Q11, Q13,

Detecting and Explaining Causes From Text For a Time Series Event

Explaining underlying causes or effects about events is a challenging but valuable task. We define a novel problem of generating explanations of a time series event by (1) searching cause and effect relationships of the time series with textual data and (2) constructing a connecting chain between them to generate an explanation. To detect causal features from text, we propose a novel method based on the Granger causality of time series between features extracted from text such as N-grams, topics, sentiments, and their composition. The generation of the sequence of causal entities requires a commonsense causative knowledge base with efficient reasoning. To ensure good interpretability and appropriate lexical usage we combine symbolic and neural representations, using a neural reasoning algorithm trained on commonsense causal tuples to predict the next cause step. Our quantitative and human analysis show empirical evidence that our method successfully extracts meaningful causality relationships between time series with textual features and generates appropriate explanation between them.Comment: Accepted at EMNLP 201

Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches

In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this work, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area