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BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251002T140000
DTEND;TZID=Europe/London:20251002T150000
DTSTAMP:20260506T200917
CREATED:20250819T141247Z
LAST-MODIFIED:20250819T141247Z
UID:10000526-1759413600-1759417200@www.swdtp.ac.uk
SUMMARY:Clean Python for Reproducible Research
DESCRIPTION:This live\, practical session introduces core techniques for improving the quality and transparency of research code.\nParticipants will learn how to: \n\nWrite clearer\, modular Python scripts\nUse Git for version tracking and collaboration\nSet up reusable Jupyter Notebooks for consistent workflows\n\nNo prior experience is required. The workshop is suitable for all doctoral students using (or planning to use) Python in their research. \nPlaces will be allocated on a first-come\, first-served basis\, and once places are full\, we will maintain a waiting list. \nPlease only register if you are certain of your availability and commitment to attend. \nThis event is not delivered by the SWDTP. For enquiries\, please contact granduniondtp@socsci.ox.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/clean-python-for-reproducible-research/
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251003T090000
DTEND;TZID=Europe/London:20251003T160000
DTSTAMP:20260506T200917
CREATED:20250619T084542Z
LAST-MODIFIED:20250619T084542Z
UID:10000510-1759482000-1759507200@www.swdtp.ac.uk
SUMMARY:Mediation and Moderation Analysis Using R
DESCRIPTION:This one-day course introduces the concepts and techniques of moderation and mediation analysis using R\, with a focus on social science applications. Participants will learn how to test for interaction effects (moderation) using regression models\, conduct mediation analysis using structural equation modelling and combine both approaches to explore mediated moderation. Emphasis is placed on interpretation\, visualization\, and reproducible R workflows using real-world data examples. \n\nThe course covers: \n\nA brief refresher on linear regression in R\nModeration analysis using interaction terms in regression\nProbing and visualizing interaction effects\nMediation analysis using path models\nEstimating indirect effects\nMediated moderation\nApplied examples in social sciences\nPractical coding sessions using real datasets in R\n\n\nBy the end of the course participants will: \n\nUnderstand the conceptual foundations of moderation and mediation\nConduct moderation analysis using interaction terms in R\nInterpret and visualize interaction effects\nSpecify and estimate mediation models using path analysis\nTest indirect effects and interpret mediation results\nUse SEM to explore differences in mediation across groups\nApply techniques to real-world social science datasets\n\n\nThe course includes hands-on computer workshops. Participants will use the R programming language.
URL:https://www.swdtp.ac.uk/event-calendar/mediation-and-moderation-analysis-using-r/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251009T110000
DTEND;TZID=Europe/London:20251009T180000
DTSTAMP:20260506T200917
CREATED:20250905T163924Z
LAST-MODIFIED:20250905T163924Z
UID:10000543-1760007600-1760032800@www.swdtp.ac.uk
SUMMARY:Education-focused Quantitative Research Methods Workshops Using R
DESCRIPTION:The Centre for Multilevel Modelling (CMM) at Bristol is running two education-focused quantitative research method workshops using R at the School of Education. “R Progamming for Beginners” and “An Introduction to Rasch Modelling Using R”. \nFurther details and registration instructions can be found here:  https://www.eventbrite.co.uk/e/two-cmm-hosted-quantitative-research-methods-workshops-using-r-tickets-1656870958639?aff=oddtdtcreator&_gl=1%2A1r1e1il%2A_up%2AMQ..%2A_ga%2AMTcyMTE2NzAyMC4xNzU3MDY0OTA2%2A_ga_TQVES5V6SH%2AczE3NTcwNjQ5MDYkbzEkZzAkdDE3NTcwNjQ5MDYkajYwJGwwJGgw\n\nPlease send enquiries to: ed-events@bristol.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/education-focused-quantitative-research-methods-workshops-using-r/
LOCATION:35 Berkeley Square\, Bristol\, 35 Berkeley Square\, Bristol\, BS81JA\, United Kingdom
CATEGORIES:Higher Level Training,Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251010T093000
DTEND;TZID=Europe/London:20251010T123000
DTSTAMP:20260506T200917
CREATED:20250829T085505Z
LAST-MODIFIED:20250829T090648Z
UID:10000530-1760088600-1760099400@www.swdtp.ac.uk
SUMMARY:Introduction to Machine Learning and Data Science
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). \nSENSS Specialist Training: Introduction to Machine Learning and Data Science\nInstructor: Dr Elisabetta Pellini (Bayes Business School)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \nIn today’s global and digital economic environment\, we have unprecedented access to vast amounts of data across all types of industries. It has become crucial for researchers and professionals to possess analytical skills that enable data-driven decision-making. Data science is the practice of collecting\, analysing\, and interpreting data that empowers decision-makers to make informed choices. \nThis module provides both theoretical foundations and practical skills necessary to apply machine learning and data science methods to problems in economics and finance. Theoretical concepts will be introduced in an intuitive and accessible manner. Emphasis will be placed on selecting appropriate methods for specific problems\, implementing methods correctly and presenting and interpreting analytical results \nLectures will include practical\, computer-based exercises using real-world datasets. Students will learn to use Python to carry out these tasks. \n  \nPrerequisites \nStudents are expected to have a knowledge of statistics (descriptive and inference) and basic Python knowledge (e.g.\, libraries NumPy\, Pandas\, statsmodels). \n  \nSoftware \nParticipants will use Jupyter Notebooks during the sessions for hands-on modelling demonstrations. The easiest way to access Jupyter is by installing the Anaconda Distribution\, which includes Python\, Jupyter\, and most of the required libraries. Anaconda can be downloaded here: https://www.anaconda.com/products/distribution \n  \nContent Outline \n\nLinear Regression\n\nModel assumptions\nGoodness of fit\nPrediction\nDiagnostic analysis\n\n\nLinear Model Selection\n\nBias-variance trade-off\nCross-validation\nSubset selection\nRidge Regression\nLASSO\n\n\nDimensionality Reduction\n\nPrincipal Component Analysis (PCA)\nPCA in regression contexts\n\n\nApplications in Python\n\nHands-on implementation using Python libraries such as pandas\, scikit-learn\, and statsmodels\n\n\n\nLearning Objectives \nBy the end of this module\, you will be able to: \n\nExplain the fundamental principles of linear regression\, including model assumptions\, coefficient estimation\, and diagnostic analysis.\nCompare and apply model selection techniques\, including subset selection\, Ridge Regression\, and LASSO.\nUnderstand and implement dimensionality reduction techniques\, particularly Principal Component Analysis\, and assess their impact on regression models.\nCritically assess the suitability of different machine learning methods for solving specific problems and justify methodological choices.\nUse Python programming tools and libraries (e.g.\, pandas\, scikit-learn\, statsmodels) to perform data analysis and apply machine learning methods to real-world datasets.\n\nMain References \n\nJames\, G.\, Witten\, D.\, Hastie\, T.\, & Tibshirani\, R. (2023). An Introduction to Statistical Learning with Applications in Python. Springer.\nMcKinney\, W. (2018). Python for Data Analysis: Data Wrangling with Pandas\, NumPy\, and IPython (2nd ed.). O’Reilly Media.\n\n  \nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_2n2VMaHX4Ahh7wy \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/introduction-to-machine-learning-and-data-science/2025-10-10/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251015T180000
DTEND;TZID=Europe/London:20251015T193000
DTSTAMP:20260506T200917
CREATED:20250901T172248Z
LAST-MODIFIED:20250901T172248Z
UID:10000542-1760551200-1760556600@www.swdtp.ac.uk
SUMMARY:Tackling the World-Wide Gambling Epidemic. What Can We Learn from Africa?
DESCRIPTION:The lecture is themed around the important topic: “Tackling the World-Wide Gambling Epidemic. What Can We Learn from Africa?”\nThis is a Public Lecture hosted by the Bristol Hub for Gambling Harms Research at the University of Bristol.\nGambling is a growing public health issue across the globe.\nThe global gambling industry is worth around $580bn – that’s bigger than the GDP of 86% of countries on our planet. But its human cost is vast. Millions of people worldwide experience serious harms\, from financial hardship and relationship breakdown to mental health problems and suicide. Smartphones have turned gambling into a 24/7 activity across the globe.\nJoin us for an insightful evening exploring innovative\, community-driven approaches to reducing gambling harm\, with a focus on powerful learnings from African research projects funded by the Bristol Hub.\nThe evening will start with a vibrant music video co-created and produced with young people affected by gambling harms in Uganda\, followed by a talk on the effects it has had on youth communities.\nThis will be followed by a panel discussion\, featuring researchers from Uganda\, Nigeria\, Namibia\, Kenya\, and South Africa\, alongside Professor Agnes Nairn from the University of Bristol.\nThis event will uncover:\n\n\nHow creative and digital tools can engage young people in awareness of gambling harms\n\n\nThe role of gambling in migration and university settings\n\n\nAdvertising’s influence in African sport and media\n\n\nDissemination of gambling messaging\n\n\nWhat the UK can learn from these diverse African perspectives\n\n\n\nWe’d love for you to join us for this important conversation. We anticipate high interest\, so please register as soon as possible to secure a place.\n\nMore details on the event can be found here\nFor queries\, email: gambling-harms@bristol.ac.uk\n\n\nThis event is kindly sponsored by Bristol Collegiate Research Society: The Bristol Collegiate Research Society is a charity committed to supporting an annual academic research symposium in the University of Bristol. The Society was founded in 1899 by a group of Bristol citizens who wished to assist University College Bristol gain a Royal Charter and become the University of Bristol. 
URL:https://www.swdtp.ac.uk/event-calendar/tackling-the-world-wide-gambling-epidemic-what-can-we-learn-from-africa/
LOCATION:Great Hall\, Wills Memorial Buildng\, University of Bristol\, Queen's Road\, Bristol\, BS8 1RJ
CATEGORIES:Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251016T143000
DTEND;TZID=Europe/London:20251016T173000
DTSTAMP:20260506T200917
CREATED:20250829T085505Z
LAST-MODIFIED:20250829T090648Z
UID:10000531-1760625000-1760635800@www.swdtp.ac.uk
SUMMARY:Introduction to Machine Learning and Data Science
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). \nSENSS Specialist Training: Introduction to Machine Learning and Data Science\nInstructor: Dr Elisabetta Pellini (Bayes Business School)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \nIn today’s global and digital economic environment\, we have unprecedented access to vast amounts of data across all types of industries. It has become crucial for researchers and professionals to possess analytical skills that enable data-driven decision-making. Data science is the practice of collecting\, analysing\, and interpreting data that empowers decision-makers to make informed choices. \nThis module provides both theoretical foundations and practical skills necessary to apply machine learning and data science methods to problems in economics and finance. Theoretical concepts will be introduced in an intuitive and accessible manner. Emphasis will be placed on selecting appropriate methods for specific problems\, implementing methods correctly and presenting and interpreting analytical results \nLectures will include practical\, computer-based exercises using real-world datasets. Students will learn to use Python to carry out these tasks. \n  \nPrerequisites \nStudents are expected to have a knowledge of statistics (descriptive and inference) and basic Python knowledge (e.g.\, libraries NumPy\, Pandas\, statsmodels). \n  \nSoftware \nParticipants will use Jupyter Notebooks during the sessions for hands-on modelling demonstrations. The easiest way to access Jupyter is by installing the Anaconda Distribution\, which includes Python\, Jupyter\, and most of the required libraries. Anaconda can be downloaded here: https://www.anaconda.com/products/distribution \n  \nContent Outline \n\nLinear Regression\n\nModel assumptions\nGoodness of fit\nPrediction\nDiagnostic analysis\n\n\nLinear Model Selection\n\nBias-variance trade-off\nCross-validation\nSubset selection\nRidge Regression\nLASSO\n\n\nDimensionality Reduction\n\nPrincipal Component Analysis (PCA)\nPCA in regression contexts\n\n\nApplications in Python\n\nHands-on implementation using Python libraries such as pandas\, scikit-learn\, and statsmodels\n\n\n\nLearning Objectives \nBy the end of this module\, you will be able to: \n\nExplain the fundamental principles of linear regression\, including model assumptions\, coefficient estimation\, and diagnostic analysis.\nCompare and apply model selection techniques\, including subset selection\, Ridge Regression\, and LASSO.\nUnderstand and implement dimensionality reduction techniques\, particularly Principal Component Analysis\, and assess their impact on regression models.\nCritically assess the suitability of different machine learning methods for solving specific problems and justify methodological choices.\nUse Python programming tools and libraries (e.g.\, pandas\, scikit-learn\, statsmodels) to perform data analysis and apply machine learning methods to real-world datasets.\n\nMain References \n\nJames\, G.\, Witten\, D.\, Hastie\, T.\, & Tibshirani\, R. (2023). An Introduction to Statistical Learning with Applications in Python. Springer.\nMcKinney\, W. (2018). Python for Data Analysis: Data Wrangling with Pandas\, NumPy\, and IPython (2nd ed.). O’Reilly Media.\n\n  \nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_2n2VMaHX4Ahh7wy \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/introduction-to-machine-learning-and-data-science/2025-10-16/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251023T100000
DTEND;TZID=Europe/London:20251023T130000
DTSTAMP:20260506T200917
CREATED:20250819T142058Z
LAST-MODIFIED:20250819T142058Z
UID:10000527-1761213600-1761224400@www.swdtp.ac.uk
SUMMARY:Logical Foundations of Critical Thinking
DESCRIPTION:At the heart of critical thinking is the ability to reason well. When we reason\, our aim is to identify beliefs or actions which are supported by current mental states of ours. This course will consider different ways in which beliefs can be supported by other beliefs as well as different ways in which actions can be supported by beliefs and other mental states. \nWe will look at two types of reasoning about what to believe: deductive reasoning and inductive reasoning. In good deductive reasoning\, the beliefs functioning as premises provide total support for the conclusion; the truth of the premises guarantees the truth of the conclusion. In contrast\, in good inductive reasoning\, the premises only provide a high degree of support for the conclusion; the conclusion could be false even if the premises are true. We will discuss different conceptions of good deductive and inductive inferences. \nIn the area of reasoning about what to do\, we will consider means-end reasoning\, decision theory and moral reasoning. We will ask what legitimate role mental states like desires or emotions can play in such reasoning. \n  \nPlaces will be allocated on a first-come\, first-served basis\, and once places are full\, we will maintain a waiting list. \nPlease only register if you are certain of your availability and commitment to attend. \n  \nThis event is not delivered by the SWDTP. For enquiries\, please contact granduniondtp@socsci.ox.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/logical-foundations-of-critical-thinking/
LOCATION:6 Worcester St\, Oxford OX1 2BX
CATEGORIES:Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251024T093000
DTEND;TZID=Europe/London:20251024T123000
DTSTAMP:20260506T200917
CREATED:20250829T085505Z
LAST-MODIFIED:20250829T090648Z
UID:10000532-1761298200-1761309000@www.swdtp.ac.uk
SUMMARY:Introduction to Machine Learning and Data Science
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). \nSENSS Specialist Training: Introduction to Machine Learning and Data Science\nInstructor: Dr Elisabetta Pellini (Bayes Business School)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \nIn today’s global and digital economic environment\, we have unprecedented access to vast amounts of data across all types of industries. It has become crucial for researchers and professionals to possess analytical skills that enable data-driven decision-making. Data science is the practice of collecting\, analysing\, and interpreting data that empowers decision-makers to make informed choices. \nThis module provides both theoretical foundations and practical skills necessary to apply machine learning and data science methods to problems in economics and finance. Theoretical concepts will be introduced in an intuitive and accessible manner. Emphasis will be placed on selecting appropriate methods for specific problems\, implementing methods correctly and presenting and interpreting analytical results \nLectures will include practical\, computer-based exercises using real-world datasets. Students will learn to use Python to carry out these tasks. \n  \nPrerequisites \nStudents are expected to have a knowledge of statistics (descriptive and inference) and basic Python knowledge (e.g.\, libraries NumPy\, Pandas\, statsmodels). \n  \nSoftware \nParticipants will use Jupyter Notebooks during the sessions for hands-on modelling demonstrations. The easiest way to access Jupyter is by installing the Anaconda Distribution\, which includes Python\, Jupyter\, and most of the required libraries. Anaconda can be downloaded here: https://www.anaconda.com/products/distribution \n  \nContent Outline \n\nLinear Regression\n\nModel assumptions\nGoodness of fit\nPrediction\nDiagnostic analysis\n\n\nLinear Model Selection\n\nBias-variance trade-off\nCross-validation\nSubset selection\nRidge Regression\nLASSO\n\n\nDimensionality Reduction\n\nPrincipal Component Analysis (PCA)\nPCA in regression contexts\n\n\nApplications in Python\n\nHands-on implementation using Python libraries such as pandas\, scikit-learn\, and statsmodels\n\n\n\nLearning Objectives \nBy the end of this module\, you will be able to: \n\nExplain the fundamental principles of linear regression\, including model assumptions\, coefficient estimation\, and diagnostic analysis.\nCompare and apply model selection techniques\, including subset selection\, Ridge Regression\, and LASSO.\nUnderstand and implement dimensionality reduction techniques\, particularly Principal Component Analysis\, and assess their impact on regression models.\nCritically assess the suitability of different machine learning methods for solving specific problems and justify methodological choices.\nUse Python programming tools and libraries (e.g.\, pandas\, scikit-learn\, statsmodels) to perform data analysis and apply machine learning methods to real-world datasets.\n\nMain References \n\nJames\, G.\, Witten\, D.\, Hastie\, T.\, & Tibshirani\, R. (2023). An Introduction to Statistical Learning with Applications in Python. Springer.\nMcKinney\, W. (2018). Python for Data Analysis: Data Wrangling with Pandas\, NumPy\, and IPython (2nd ed.). O’Reilly Media.\n\n  \nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_2n2VMaHX4Ahh7wy \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/introduction-to-machine-learning-and-data-science/2025-10-24/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251029T090000
DTEND;TZID=Europe/London:20251029T170000
DTSTAMP:20260506T200917
CREATED:20250918T100518Z
LAST-MODIFIED:20250918T100518Z
UID:10000547-1761728400-1761757200@www.swdtp.ac.uk
SUMMARY:Justice beyond Criminal Justice
DESCRIPTION:A one-day\, in-person symposium bringing together academics from across the social sciences to explore the justice processes that sit outside of the formal criminal justice system\, used in response to serious harm and injury.\n\nThis event is a collaboration between the Criminology Research Group at the University of Bath\, the SWDTP\, and the British Society of Criminology (South West). The keynote speaker is Professor Richard Moorhead (University of Exeter)\, who will be sharing his research on the Post Office IT Horizon Inquiry. The event also incorporates panels for oral presentations\, as well as a parallel poster and art-work exhibition session. There will be an optional training session for attendees to learn more about the ethics and practicalities of using images in and for research\, as well as their role in engaging non-academic audiences. There is a fund available to support those who are interested in producing high-quality photographic images to display at this event.\n\nThis event is organised by colleagues at the University of Bath\, and supported in part by the SWDTP academic-led higher level training fund.\n\nKey dates:\nAbstract submission deadline: 30 September\nAttendee registration deadline: 6 October\nSymposium: 29 October\n\nFurther information\, abstract submission instructions and registration: https://www.eventbrite.co.uk/e/justice-beyond-criminal-justice-tickets-1606523477969?aff=oddtdtcreator\nPlease direct enquiries to the symposium organisers.
URL:https://www.swdtp.ac.uk/event-calendar/justice-beyond-criminal-justice/
LOCATION:University of Bath\, 8 West Room 2.23\, Bath\, BA2 7AY\, United Kingdom
CATEGORIES:Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251030T100000
DTEND;TZID=Europe/London:20251030T170000
DTSTAMP:20260506T200917
CREATED:20250916T150754Z
LAST-MODIFIED:20250916T150754Z
UID:10000545-1761818400-1761843600@www.swdtp.ac.uk
SUMMARY:SWDTP Conference - Everyday and Beyond: Encountering\, Sharing\, Caring
DESCRIPTION:The SWDTP Annual Student Conference is an event created for students\, by students. It presents a fantastic opportunity to engage in a dynamic and inclusive platform that promotes innovative ideas and critical reflections on the future of social sciences. \n  \nThis year’s conference theme is: \nEveryday and Beyond- Encountering\, Sharing\, Caring. \n  \nThis conference involves plenary and breakout sessions. Please navigate to the Conference Agenda to read more about the sessions\, as you will have to indicate your preferences as part of the registration form. \n  \nAgenda: https://www.swdtp.ac.uk/swdtp-student-conference-agenda-2025/ \n  \nWe are pleased to invite all social science research students (whether funded by the SWDTP or not) to register to this free event\, being held in the Commons Building of Bath Spa University on Thursday October 30th 2025.
URL:https://www.swdtp.ac.uk/event-calendar/swdtp-conference-everyday-and-beyond-encountering-sharing-caring/
CATEGORIES:Conference
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251031T093000
DTEND;TZID=Europe/London:20251031T123000
DTSTAMP:20260506T200917
CREATED:20250829T090215Z
LAST-MODIFIED:20250829T090736Z
UID:10000533-1761903000-1761913800@www.swdtp.ac.uk
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
END:VEVENT
END:VCALENDAR