Overview
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 and Dr. Melvyn Weeks.
The courses forming the 2024 Econometrics Summer School, Cambridge are:
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).
View the dedicated course FAQ here.
Accommodation
Attendees of the Econometrics Summer School have the option of booking the course either inclusive or non-inclusive of accommodation. The price listed on this page includes accommodation. If you wish to book your own accommodation please reach out to our team at [email protected] and we can provide you with a quote, reduced proportionally to the nightly rate. Residential accommodation is provided at Wolfson College, University of Cambridge. All rooms are single rooms allowing single occupancy only.
The room policy for this school is that residential accommodation is only provided whilst at the course. For example, if someone is attending all three courses, accommodation is provided for check-in on 15 July and check-out on 20 July.
If any participant registered would like to book the evening of the 14 July, then there are rooms available in other nearby Cambridge Colleges, please check University Rooms here to view available options.
If a participant is attending only one course (1 x 2-day course), let's say course 1, they can check in on 15 July and then will need to check out on 16 July.
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.
Course 1: Macroeconomic Modelling, Machine Learning and Forecasting
- Date: 15 - 16 July 2024
- Delivered by Prof. Sean Holly
Course Overview
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 Outline
Day 1
15 July 2024
Session 1:
- Sources of Forecast error
- Comparing and combining forecasts
Session 2:
- Evaluating Forecasts
- Nowcasting
- Types of Macro Model: Structural/Cowles Modelling
- VARs, Structural VARs; GVARs; DSGE
Session 3 and 4
- Machine Learning; general to specific; Macroeconometric model building. Non-stationarity, unit roots, cointegration.
Day 2
16 July 2024
Session 1 & 2:
- Building a Small US model in EViews; model simulation; add factors; uncertainty shocks.
Session 3 & 4:
- Forecasting with Structural models – Fair Model of the US: Developing a forecast; Exogenous assumptions.
Learning ratio: 55% theory; 45% practical application.
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Course 2: Microeconometrics and Methods for Machine Learning
- Date: 17 - 18 July 2024
- Delivered by Dr. Melvyn Weeks, University of Cambridge
Course Overview
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 Jeffrey M. Wooldridge, Introductory Econometrics: A Modern Approach (Cengage Learning, 2013) as a point of departure.
The theoretical and empirical components of these sessions are designed to ensure that participants truly understand the material. Previous experience with Stata is advantageous.
Course Outline
Day 1
17 July 2024
Session 1: Overview & Policy Evaluation
- Causality and Inference
- Estimators: Means, Differences in Means and Matching
- Economic Policy Evaluation
- The Self-Selection Problem
- Treatment Effects
- Prediction Policy Problems
- Machine Learning and Econometrics
Suggested Reading
- Jeffrey M. Wooldridge, Introductory Econometrics: A Modern Approach (Cengage Learning, 2013), Chapter 1
Session 2: The Linear Regression Model
- OLS and The Method of Moments
- Gauss Markov Assumptions
Suggested Reading
- Jeffrey M. Wooldridge, Introductory Econometrics: A Modern Approach (Cengage Learning, 2013), Chapters 2, 3, 4 & 15
Session 3a: Endogeneity and Instrumental Variable Estimation
- Conditional and Unconditional Moments
- The OLS and the IV Estimator
- Instruments
- The Exogeneity of Instruments
- The Relevance of instruments
Suggested Reading
- Jeffrey M. Wooldridge, Introductory Econometrics: A Modern Approach (Cengage Learning, 2013), Chapters 9, 15 & 16
Stata Session
- The application of Estimating the Price Elasticity of Demand for Cigarettes
Session 3b: Programme Evaluation and Treatment Effects
- Overview
- Simple Estimators: Differences in Means
- The Difference-in-Difference Estimator
- Multiple Time Periods and Multiple Treatment
- Anticipating Panel Data
Suggested Reading:
- A. Colin Cameron & Pravin K. Trivedi, Microeconometrics: Methods and Applications (Cambridge University Press, 2005)
Session 4: Linear Unobserved Effects Panel Data
- Introduction to Panel Data Models
- Fixed Effects Models
- Random Effects
- Hausman Tests
- Dynamic Panels
Suggested Reading
- Jeffrey M. Wooldridge, Introductory Econometrics: A Modern Approach (Cengage Learning, 2013), Chapters 13 & 14
- A. Colin Cameron & Pravin K. Trivedi, Microeconometrics: Methods and Applications (Cambridge University Press, 2005), Chapter 21
Stata Session
- Linear Fixed and Random Effects Estimators. The application is on The Responsiveness of Labour Supply to Wages.
Day 2
18 July 2024
Session 1: Binary Choice Models
- The Linear Probability Model (lpm)
- Partial Effects
- Probit and Logit Models
- Endogeneity in Binary Choice:
- Control Functions
- Bivariate Probit
- The lpm
Suggested Reading
- A. Colin Cameron & Pravin K. Trivedi, Microeconometrics: Methods and Applications (Cambridge University Press, 2005), Chapter 25.8.5 pages 893-6
Stata Session
- Do workplace smoking bans reduce smoking?
Session 2: Machine Learning and Decision Trees
- Overview, Prediction and Evaluation
- High-level overview of Machine Learning and ai
- Machine Learning: The Vernacular
- The Nature of Prediction Problems
- Prediction, Evaluation and Causal Inference
- Course Notes: Overview, Prediction and Evaluation
Suggested Reading
- L., Breiman, L., Freidman, J., Olshen, R., & C. Stone. Classification and Regression Trees (Klein-Verlag, 1990)
- Training, validation, and test data sets
- J. Freidman, T. Hastie, R. Tibshirani, The Elements of Statistical Learning, Data Mining, Inference, and Prediction (Springer, 2009)
- Athey, S. The Impact of Machine Learning on Economics (The Economics of Artificial Intelligence: An Agenda, 2018), National Bureau of Economic Research. Link
- Athey, S. & Imbens, G, Machine Learning Methods Economists Should Know About (Working Paper, 2019), Graduate School of Business, Stanford University
Session 3: Machine Learning for Prediction and Causal Inference
- Machine Learning: Terminology and Concepts
- Point of Departure:
- The OLS Estimator
- High Dimensional Methods - Lasso
- Treatment Effects
- An Overview of Regression Trees
- The Bias-Variance Tradeoff
- Training, Testing and Cross Validation
- Regularization: Variance reduction and Ensemble Learning
Suggested Reading
- Athey, S. & G. Imbens. Recursive partitioning for heterogeneous causal effects (Proceedings of the National Academy of Sciences, 113(27):7353–7360, 2016)
- O’Neill, E. & M. Weeks Causal Tree Estimation of Heterogeneous Household Response to Time-Of-Use Electricity Pricing Schemes2018.
Stata Session
- Time of Use Tariffs and Smart Meter Data - Heterogeneous treatment effects
Session 4: Fundamentals of Bayesian Inference
- From Inverse Probability to Bayesian Inference
- Bayes Theorem for Events
- Bayes, Probability and Jurisprudence
- Natural Conjugate Priors
- Application: Posterior Assessment of a Proportion
Suggested Reading
- Koop, G Introduction to Econometrics (Wiley, 2008)
- Lancaster, T Introduction to Modern Bayesian Econometrics (Wiley-Blackwell, 2004), Chapter 1
- Rossi et al (2009), Chapter 2.0-2.5
- Thompson (2014), Chapter 2
Course 3: Linear and Nonlinear Time Series Models and their Applications
- Date: 19 - 20 July 2024
- Delivered by: Prof. Andrew C. Harvey, Faculty of Economics, University of Cambridge
Course Overview:
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 focusses 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 is primarily concerned with topics covered in the second day. It will be of particular interest to practitioners and researchers who work in Financial econometrics.
Some participants may find some of the maths difficult, but the lectures will stress the concepts and the implications for applied work.
Course outline:
Day 1
19 July 2024
Session 1
- Introduction
- Stationary time series
- Unobserved components
- Signal extraction
- ARMA models
Session 2
- State Space Models & the Kalman filter
- Signal Extraction
- Missing Observations and other data Irregularities
- Stamp package
Session 3
- Trends and Cycles
- Seasonality
- Detrending and differencing operations
Session 4
- Explanatory Variables and Intervention Analysis
- STAMP Package
Day 2
20 July 2024
Session 1
- Multivariate time series models
- Common trends and co-integration control groups
Session 2
- Non-linear models
- Dynamic conditional score models
Session 3
- Financial Econometrics
- Stochastic volatility and ARCH
- Intra-day data
- Time-varying correlation and association
Session 4
- Estimation with computer packages
Main Texts:
- Commandeur, J.J.F. and S.J. Koopman. An introduction to state space time series analysis. OUP, 2007.
- Durbin, J. and S.J. Koopman, Time Series Analysis by State Space Methods, 2nd ed. Oxford University Press, Oxford, 2012.
- Harvey, A. C. Dynamic Models for Volatility and Heavy Tails. Cambridge University Press, 2013.
- Harvey, A. C., Time Series Models (TSM), 2nd Edition, Harvester Wheatsheaf, 1993. [Currently out of print]
- Martin, V., Hurn, S. and D. Harris, (MHH) Econometric Modelling with Time Series: Specification, Estimation and Testing, 2013.
- Taylor, S. Asset Price Dynamics, Volatility, and Prediction. Princeton University Press, 2005.
- Taylor, S. Modelling Financial Time Series, 2nd edition. World Scientific, 2008.
- Recommended for preliminary reading
Other references:
- Andersen, T.G., Bollerslev, T., Christoffersen, P.F. and F.X. Diebold. (2006). Volatility and correlation forecasting. Handbook of Economic Forecasting, edited by G Elliot, C Granger and A Timmermann, 777-878. North Holland.
- Creal, D., Koopman, S.J., and A. Lucas (2013). Generalized autoregressive score models with applications. Journal of Applied Econometrics, 28, 777-795.
- Franke, J., Hardle, W.K., Hafner, C.M., Statistics of Financial Markets, Third Edition, Springer, 2011.
- Harvey, A. C., Forecasting, Structural Time Series Models and the Kalman Filter (FSK), Cambridge University Press, 1989
- Harvey, A.C., (2006). Forecasting with Unobserved Components Time Series Models, Handbook of Economic Forecasting, edited by G Elliot, C Granger and A Timmermann, 327-412. North Holland.
- Hautsch, N. Econometrics of Financial High-Frequency Data, Berlin: Springer Verlag, 2012.
- Hamilton, J. D., Time Series Analysis, Princeton University Press, 1994.
- Mills, T. and R.N. Markellos, The Econometric Modelling of Financial Time Series, 3rd ed. Cambridge University Press, 2008
- Tsay, R, Analysis of Financial Time Series, 3rd ed. Wiley, 2010.
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Feedback & Testimonials
Delegate feedback is made available through our training associate profiles. Click on the names below to view their profile and feedback from previous courses:
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Accommodation
The Econometrics Summer School is a residential course and students may choose whether to book the course with accommodation included or book only the course and arrange their own accommodation separately. For those students who choose inclusive accommodation, it is provided at Wolfson College, University of Cambridge. Please note all rooms in Wolfson College are single rooms allowing for single occupancy only.
The Price listed on this page is inclusive of accommodation. For a course-only quote please contact [email protected].
Registration
Register online or find out more by contacting our sales and training team either by email: [email protected] or phone: +44 (0) 20 8697 3377.
The number of delegates is restricted. Please register early to guarantee your place.
Daily Course Timetable
| Time | Session / Description |
|---|
| 09:30 - 11:00 |
Session 1 |
| 11:00-11:30 |
Morning Break |
| 11:30-13:00 |
Session 2 |
| 13:00 - 14:00 |
Lunch Break |
| 14:00 - 15:30 |
Session 3 |
| 15:30 - 16:00 |
Afternoon Break |
| 16:00 - 17:30 |
Session 4 |
*Please note that the daily timetables are subject to minor change.
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Prerequisites
Course 1: Macroeconomic Modelling, Machine Learning and Forecasting - Prerequisites.
- Intermediate level University training (or equivalent) in econometrics is essential. Basic knowledge of time series concepts such as ARMA models, stationarity vs. non-stationarity and forecasting is required.
Pre-course readings: Enders, W., (2012). Applied Econometric Times Series, 3rd Ed., Wiley.
Delegates should associate themselves with the EViews Help Files. The PDF manuals provide detailed information regarding each topic so it is advised that delegates familiarise themselves with the manual readings to be prepared for the course.
Suggested reading:
Textbooks:
- Griffiths, W. E., Hill, R. C., and Judge, G.C., (1993). Learning and Practicing Econometrics, Wiley.
A comprehensive description of econometrics written with the practitioner rather than the theorist in mind
- Charemza, W. W., and Deadman, D. F., (1997). New Directions in Econometric Practice: General to Specific Modelling, Cointegration and Vector Autoregression, 2nd Ed., Edward Elger.
- his book reviews modern approaches to time series econometrics. It is strongest on the ’Hendry’ approach though the section on Vector Autoregressions is also well worth reading.
- Verbeek, M., (2000). A Guide to Modern Econometrics, Wiley.A relatively advanced text that covers a lot of recent material.
- Dougherty, C., (2000). Introduction to Econometrics, 2nd Ed., Oxford University Press.
- A useful basic text. This is pitched at the level of an undergraduate econometrics module.
- Gujarati, D., (1999). Essentials of Econometrics, McGraw-Hill International Editions.
- Similar to Dougherty (see above). This is aimed at the undergraduate market but is also useful reference material for more advanced students.
- Mills, T. C., (1990). Time Series Techniques for Economists, Cambridge University Press.
This text has been around for a number of years now but its account of time series modelling - Box-Jenkins and VAR analysis - is still hard to beat.
- Mills, T. C., (1999). The Econometric Modelling of Financial Time Series, Cambridge University Press.
- An excellent account of time series modelling of financial series from the point of view of the econometrician rather than the financial analyst.
- Campbell, J. W., Lo, A. W., and MacKinlay, A. C., (1997). The Econometrics of Financial Markets, Princeton University Press.
This is perhaps the standard work on the econometrics of financial markets.
Methodology References:
- Kennedy, P. E., (2002). Sinning in the Basement: What are the Rules? The Ten Commandments of Applied Econometrics, Journal of Economic Surveys, 16(4), pp. 569-589.
A useful survey of how applied econometrics is actually done rather than how the textbooks say it should be done. See also the responses by Hendry, Magnus and Smith in the same edition.
- Gilbert, C. L., (1986). Professor Hendry’s Econometric Methodology, Oxford Bulletin of Economics and Statistics, Vol. 48, pp.283-307.
A good review of the general-to-specific methodology and the Hendry approach to econometrics.
Familiarity with EViews fundamentals (built-in functions) is required.
Course 2: Microeconometrics and Methods for Machine Learning
- 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
Course 3: Linear and Nonlinear Time Series Models and their Applications
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 on 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.
Terms & Conditions
- Student registrations: Attendees must provide proof of full time student status at the time of booking to qualify for student registration rate (valid student ID card or authorised letter of enrolment).
- Additional discounts are available for multiple registrations.
- Cost includes course materials, lunch and refreshments.
- 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, we can also provide laptops for a small daily charge).
- Payment of course fees required to reserve your place o the course and are to be paid in full prior to the course start date.
- Registration closes 5-calendar days prior to the start of the course.
- 100% fee returned for cancellations made over 28-calendar days prior to start of the course.
- 50% fee returned for cancellations made 14-calendar days prior to the start of the course.
- No fee returned for cancellations made less than 14-calendar days prior to the start of the course.
The number of delegates is restricted. Please register early to guarantee your place.