Econometrics Summer School, Cambridge
Our 2022 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 and Vrije Universiteit Amsterdam: Prof. Andrew Harvey, Prof. Sean Holly, Dr. Melvyn Weeks and Dr. Rutger Lit.
The courses forming the 2022 Econometrics Summer School, Cambridge are:
- Course one, 1 - 2 August 2022: Linear and Nonlinear Time Series Models and their Applications (delivered by Prof. Andrew Harvey and Dr Rutger Lit).
- Course two, 3 - 4 August 2022: Macroeconomic Modelling, Machine Learning and Forecasting (delivered by Prof. Sean Holly).
- Course three, 5 - 6 August 2022: Microeconometrics and Methods for Machine Learning (delivered by Dr. Melvyn Weeks).
Always one of our most popular series of courses where participants travel globally to attend, this is a great opportunity for students, academics and professionals to expand their econometrics skills and learn their application from econometricians pioneering research at the forefront of their specialist fields.
Participants can take advantage of the spectacular setting at Wolfson College throughout the duration of the School.
All courses teach econometrics from an applied perspective and demonstrate the techniques in the internationally used econometric software packages of Stata, EViews and OxMetrics (STAMP).
The Econometrics Winter School is a residential course and accommodation is included within the registration fees. Residential accommodation is provided at Harvey Court, Gonville and Caius College, University of Cambridge.
General Information and Student Registrations
- Student registrations: Attendees must provide proof of full time student status at the time of booking to qualify for the student registration rate (valid student ID card or authorised letter of enrolment).
- Additional discounts are available for multiple registrations. Contact us for more information.
- Registration fees include accommodation, breakfast, lunch and refreshments as well as all course materials.
- Delegates are provided with temporary licences for the software(s) used in the course and will be instructed to download and install the software prior to the start of the course. (Alternatively, laptops can be hired for a fee of £10.00 (ex. VAT) per day).
The number of delegates is restricted. Please register early to guarantee your place.
|Attend 1 course (01/08/2022 - 02/08/2022)||£1,710||£840||£840|
|Attend 2 courses (01/08/2022 - 04/08/2022)||£2,610||£1,380||£1,380|
|Attend all 3 courses (01/08/2022 - 06/08/2022)||£3,330||£2,010||£2,010|
Course 1: Linear and Nonlinear Time Series Models and their Applications
Date: 1 - 2 August 2022
Delivered by: Prof. Andrew C. Harvey, Faculty of Economics, University of Cambridge & Rutger Lit, Faculty of Economics, Cambridge and TSL, Amsterdam.
The course will show how economic and financial time series can be modelled and analysed. The aim is to provide understanding and insight into the methods used, as well as explaining the technical details.
Day 1 covers linear time series models and methodology, with applications in a variety of areas. Statistical modelling will be demonstrated using the STAMP computer package and participants will be given the opportunity to use STAMP in class; see http://www.stamp-software.com
Day 2 focuses on the nonlinear models and the way in which they can be handled by the recently developed score-driven approach. The Time Series Lab (TSL) program will be used to model time series, with applications ranging from the analysis of volatility in Financial time series to predicting the spread of coronavirus.
Participants are expected to have taken an introductory course in econometrics or time series analysis. The recently published Dynamic models for volatility and heavy tails are primarily concerned with the topics in Financial econometrics covered in the second day. It will be of particular interest to researchers who work in this area.
Some of the time series theory may be found challenging, but the lectures will stress the concepts and the implications for applied work.
Course 2: Macroeconomic Modelling, Machine Learning and Forecasting
Date: 3 - 4 August 2022
Delivered by: Prof. Sean Holly
This course is designed to cover the elements of economic theory and econometrics and the use of machine learning that are needed to construct a macro-econometric model that can be used for forecasting and for macroeconomic policy analysis.
This course will teach topics from an applied perspective and demonstrate the techniques using EViews.
Course 3: Microeconometrics and Methods for Machine Learning
Date: 5 - 6 August 2022
Delivered by: Dr. Melvyn Weeks, University of Cambridge
This course is designed to introduce participants to a number of concepts and estimators that represent central aspects of microeconometrics.
Topics covered will include: the ordinary linear regression model, instrumental variables, generalised method of moments, fixed and random effects estimators for static panel data, dynamic panel data models, and models of binary choice The course consists will also include an introduction to Machine Learning Methods for Big Data, including the use of regression trees and random forest. If there is time, we will introduce a number of key components of Bayesian Econometrics.
Each session will be accompanied by a hands-on stata exercise using a number of datasets.
As a guide to the level of the course, we will use Introductory Econometrics: A Modern Approach by J. Wooldridge as a point of departure.
The theoretical and empirical components of these lectures are designed to ensure that participants truly understand the material. Previous experience with Stata is advantageous.