Instructor: Prof Lorenzo Trapani
This course focuses on advanced time series modelling for non-stationary variables
By the end, participants can confidently model non-stationary variables using EViews in real-world scenarios.
Instructor: Prof Lorenzo Trapani
This course focuses on advanced time series modelling for non-stationary variables
By the end, participants can confidently model non-stationary variables using EViews in real-world scenarios.
Instructor: Dr Giovanni Cerulli, National Research Council of Italy
This course is a primer to machine learning techniques using Stata. Stata owns various packages to perform machine learning, which are poorly known to many Stata users. This course fills this gap by familiarising participants with (and knowledgeable of) Stata's potential to draw knowledge and value from rows of large and possibly noisy data.
After the course, participants are expected to have an improved understanding of Stata potential to perform some of the most used marching learning techniques, thus becoming able to master research tasks including, among others, the above.
Instructor: Prof Lorenzo Trapani
This course offers a foundational exploration of EViews, a leading econometric software.
The course will teach econometrics from an applied perspective and demonstrate techniques using EViews 13 software.
Instructor: Prof. Christophe Hurlin
The objective of the course is to learn how to model an ARDL and to understand the advantages of this model for the implementation of forecasting models..
At the end of the course, participants will be able to implement cointegration tests in the framework of ARDL models by respecting a rigorous specification approach, to derive an ECM representation of an ARDL, to interpret it economically, and to build forecasts using an ARDL model.
Instructor: Francesco Saverio Stentella Lopes, University of Roma Tre, Rome, Italy
The course will run online using Zoom.
Building tools to access and retrieve data stored in known locations online has always been an important resource for any analyst, non more so than now with vast amounts of data being available. However, accessing this data in formats that is easy to examine is not so straightforward. This course will cover the fundamentals of data scraping using Python libraries and how this data can be brought into Stata for analysis.
Instructor: Prof Lorenzo Trapani
This course delves into advanced econometric techniques, covering (G)ARCH models for volatility analysis and Panel Data Models.
By the end, participants gain practical skills for real-world application, enhancing their ability to model volatility and analyze diverse datasets effectively.
Our 2024 Econometrics Summer School will be held at Wolfson College, University of Cambridge. The School comprises 3x 2-day econometrics short courses delivered by leading Econometricians from the University of Cambridge: Prof. Andrew Harvey, Prof. Sean Holly & Dr. Melvyn Weeks.
The three courses comprising the School are:
Instructor: Prof Lorenzo Trapani
This course focuses on advanced time series analysis using EViews, emphasizing ARMA and VAR models.
The course will teach econometrics from an applied perspective and demonstrate techniques using EViews 13 software.
Instructor: Dr. Giovanni Cerulli
This course provides an introduction to the econometrics of program evaluation using Stata. After an introduction to counterfactual causality, the course will cover these approaches: Regression adjustment, Matching, Reweighting and Double-robust methods, and Difference-in-differences methods. The course will provide various instructional examples on real datasets using Stata.
Instructor: Prof. Philippe Van Kerm, Dept. of Social Sciences, University of Luxembourg and Luxembourg Institute of Socio-Economic Research.
The course is designed to provide participants with a comprehensive understanding of Mata, a powerful matrix programming language integrated within Stata.
This course aims to equip participants with the skills to leverage Mata's capabilities for data analysis, complex operations, ascii file processing and optimization tasks. Through a combination of theoretical explanations, practical examples, and hands-on exercises, participants will gain a solid foundation in using Mata effectively within the Stata environment.
Instructor: Prof Lorenzo Trapani
This comprehensive course delves into advanced topics in data analysis, focusing on two key aspects: Models for Panel Data and Models for Discrete Choice.
Participants will gain a nuanced understanding of panel data dynamics and discrete choice modelling, equipping them with valuable skills for informed decision-making in various fields.