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SUMMARY:Modelling Big Data using Autometrics
DESCRIPTION:This event is part of the Specialist Training Series on Machine Learning and Data Science delivered by the South & East Network for Social Sciences Doctoral Training Partnership (SENSS). \n  \nSENSS Specialist Training: Modelling Big Data using Autometrics\nInstructor: Dr Elisabetta Pellini (Bayes Business School)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \nIn the age of big data\, researchers and analysts increasingly face challenges of model selection\, overfitting\, and interpreting complex high-dimensional datasets. This session introduces Autometrics\, a powerful econometric modelling tool designed to automate model selection in large datasets while maintaining strong statistical foundations. \nAutometrics is part of the OxMetrics suite and builds on the general-to-specific (Gets) modelling philosophy. It enables users to extract robust models from large sets of candidate variables\, making it especially suitable for empirical work in economics\, finance\, and the social sciences where theory may not fully specify the data-generating process. \nThis practical session will combine core theoretical principles with hands-on demonstrations\, using real-world data examples to illustrate how Autometrics addresses the challenges of modelling with many variables. \n  \nPrerequisites \nStudents are expected to have a knowledge of statistics (descriptive and inference) and basic econometrics. \n  \nSoftware \nParticipants will use OxMetrics during the session. Instructions for installation will be provided in advance. \n  \nContent Outline \n\nGeneral-to-specific (Gets) modelling and the problem of model selection\nAutometrics algorithm: overview and logic\nDiagnostic testing and model evaluation\nHands-on modelling with real-world big data using Autometrics\n\n  \nLearning Objectives \nBy the end of the session\, participants will be able to: \n\nUnderstand the principles behind general-to-specific model selection and how Autometrics operationalises these.\nRecognise the challenges of modelling large datasets\, including overfitting\, multicollinearity\, and irrelevant variables.\nCritically evaluate the strengths and limitations of automated model selection tools in empirical research.\n\nMain References \n\nDoornik\, J.A.\, 2009a. Autometrics\, in: J.L. Castle\, N. Shephard (Eds.)\, The Methodology and Practice of Econometrics\, Oxford University Press\, Oxford (2009)\, pp. 88–121.\nHendry\, D.F.\, and J.A. Doornik (2014). Empirical Model Discovery and Theory Evaluation. MIT Press.\nHendry\, D.F.\, and J.A. Doornik(2018). Empirical Econometric Modelling – PcGive 16: Volume I. Timberlake Consultants Press\, London.\n\nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_9Zit5afuGNbuIfk \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/97349/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
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