Training Calendar

Econometrics Summer School, Cambridge

University of Cambridge 6 days (28th July 2019 - 2nd August 2019) Stata, EViews, OxMetrics Advanced, Intermediate
Delivered by: Prof. Andrew Harvey (University of Cambridge); Prof. Sean Holly (University of Cambridge); Dr. Melvyn Weeks (University of Cambridge)
Econometrics, Statistics, Summer School, Various methods
Our 2019 Econometrics Summer School, Cambridge will take place at Wolfson College, University of Cambridge.

The Econometrics Summer School comprises a series of 3x 2-day short courses and are delivered by leading econometricians from the University of Cambridge: Prof. Andrew Harvey, Prof. Sean Holly and Dr. Melvyn Weeks.

The courses forming the 2019 Econometrics Summer School, Cambridge are:

  • Course 1: Microeconometrics and Methods for Machine Learning (delivered by Dr. Melvyn Weeks)
  • Course 2: Modelling Economic and Financial Time Series (delivered by Prof. Andrew Harvey)
  • Course 3: Structural VARS and Dynamic Stochastic General Equilibrium Models: Estimation and Solutions (delivered by Prof. Sean Holly)

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).

Accommodation

The Econometrics Summer 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.

Course 1: Microeconometrics and Methods for Machine Learning

Date: Sunday 28-Monday 29 July 2019
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 and methods for machine learning.

The course consists of lectures dealing with estimation and inference using both cross-section and panel data.

Topics covered will include:
  • The ordinary linear regression model,
  • Instrumental variables,
  • Generalised method of moments,
  • Introduction to Machine Learning Methods for Big Data,
  • Fixed and random effects estimators for static panel data,
  • Dynamic panel data models,
  • Models of binary choice,
  • and if there is time an introduction to Bayesian Econometrics.

Each session will be accompanied by a hands-on Stata exercise using a number of different 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.

Day 1 - Morning Session

09:30 - 11:00: Session 1 - Overview and Using Stata's Resources

  • Stata's resources that are generally available
  • Stata's Community Contributed Commands
  • Stata Programming
  • Learning stata ....

09:30 - 11:00: Session 2 - Policy Evaluation

  1. Causality and Inference
  2. Economic Policy Evaluation
  3. Prediction Policy Problems
  • The Self-Selection Problem
  • Treatment Effects
  • Matching
  • Machine Learning and Heterogenous Treatment Effects
Readings:
  • Wooldridge (2013): Chapter 1

11:15-12:45: Session 3 - The Linear Regression Model

  1. OLS and The Method of Moments
  2. Gauss Markov Assumptions
Readings:
  • Wooldridge (2013): Chapters 2, 3, 4 & 15

Day 1 - Afternoon Session

14:00 - 15:00: Session 4 - Endogeneity and Instrumental Variable Estimation

  1. Conditional and Unconditional Moments
  2. The OLS and the IV Estimator
  3. Instruments
  • The Exogeneity of Instruments
  • The Relevance of instruments
Readings:
  • Wooldridge (2013): Chapters 9, 15, & 16
  • Stata session Internal vs External Instruments and Panel Data
  • Application Estimating the Price Elasticity of Demand for Cigarettes

15:15 - 17:00: Session 5a - Programme Evaluation and Treatment Effects

  1. Overview
  2. Simple Estimators: Differences in Means
  3. The Difference-in-Difference Estimator
  4. Multiple Time Periods and Multiple Treatment
  5. Anticipating Panel Data

Readings:

  • Cameron and Trivedi (2005) Chapter 25.8.5 pages 893-6

15:15 - 17:00: Session 5b: Linear Unobserved Effects Panel Data

  1. Introduction to Panel Data Models
  2. Fixed Effects Models
  3. Random Effects
  4. Hausman Tests
  5. Dynamic Panels

Readings:

  • Wooldridge (2013), Chapters 13 & 14; Cameron and Trivedi (2005), Chapter 21
  • Stata session: Linear Fixed and Random Effects Estimators
  • Application The Responsiveness of Labour Supply to Wages

Day 2 - Morning Session

09:30 - 12:45: Session 6 - Binary Choice Models

  1. The Linear Probability Model (LPM)
  2. Partial Effects
  3. Probit and Logit Models
  4. Endogeneity in Binary Choice: Control Functions, Bivariate Probit, and the LPM
Stata session: Binary Choice Models and Partial Effects
Application: Do Workplace Smoking Bans Reduce Smoking

Day 2 - Afternoon Session

14:00 - 15:45: Session 7 - Machine Learning and Decision Trees

  1. What is Machine Learning
  2. Prediction versus Causal Inference
  3. Training, Testing and Cross Validation
  4. Classi cation and Regression Trees/Forests
  5. Causal Trees/Forests
Application: Time of Use Tariffs and Smart Meter Data

15:30 - 17:00: Session 8 - Fundamentals of Bayesian Inference

  1. From Inverse Probability to Bayesian Inference
  2. Bayes Theorem for Events
  3. Bayes, Probability and Jurisprudence
  4. Natural Conjugate Priors
  5. Application: Posterior Assessment of a Proportion
Readings:
  • Koop (2008), Chapter 1.1; Lancaster (2004), Chapter 1
  • Rossi et al (2009); Chapter 2.0-2.5
  • Thompson (2014); Chapter 2

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Course 2: Modelling Economic and Financial Time Series

Date: Tuesday 30 July-Wednesday 31 July 2019
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. 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 1 covers time series models and methodology, with applications in a variety of areas. Day 2 focusses on the analysis of financial time series.

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 the topics in financial econometrics covered in the second day. It will be of particular interest to researchers who work in this area.

Some participants may find some of the maths difficult, but the taught sessions will stress the concepts and the implications for applied work.

Course outline:

Day 1: Time Series Models and Methodology

Introduction

  • Stationary time series
  • Unobserved components
  • Signal extraction

Time Series Models

  • ARIMA models
  • Structural time series models
  • Explanatory variables and intervention analysis

STAMP package

  • State space models and the Kalman filter
  • Signal extraction
  • Missing observations and other data irregularities
  • Trends and cycles
  • Seasonality
  • Detrending and differencing operations
  • Multivariate time series models
  • Common trends and co-integration
  • Control groups

Day 2: The Analysis of Financial Time Series

  • Nonlinear models and financial econometrics
  • Stochastic volatility and ARCH; intra-day data
  • Dynamic conditional score models. STAMP and other programs
  • Time-varying correlation and association

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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Course 3: Structural VARS and Dynamic Stochastic General Equilibrium Models: Estimation and Solutions

Date: Thursday 1 August-Friday 2 August 2019
Delivered by: Prof. Sean Holly, University of Cambridge

Course Overview:

This course is designed to cover recent developments in Structural vector autoregressive models as well as the use and solution of modern dynamic stochastic models, increasingly used by Central Banks. In particular we look at the rational expectations model of the Federal Reserve Board.

Course outline:

Day 1: VAR Models in EViews

Session 1:

  • Classical VAR models
  • Using and generalising a VAR

Session 2:

  • VARs with I(0) processes
  • Sign restrictions
  • VARs with I(1) processes
  • Cointegration.
  • GVARs

Session 3 & 4:

  • Examples of structural models

Day 2: DSGE Models

Session 1 & 2:

  • DSGE Models
  • Kydland and Prescott real business cycle models to New Keynesian models

Session 3 & 4:

  • The estimation and solution of simple linear DSGE models. Solution of nonlinear models
  • The case of the Federal Reserve Board’s FRBUS model

Pre-course readings:

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. This 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.

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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 accommodation is included within the registration fees. Residential accommodation is provided at Harvey Court, Gonville and Caius College, University of Cambridge.

Registration

Register online of find out more by contacting our sales and training team either by email: training@timberlake.co.uk or phone: +44 (0) 20 8697 3377

The number of delegates is restricted. Please register early to guarantee your place.

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Prerequisites

Course 1: 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 2: Time Series Analysis & Modelling

  • Participants are expected to have taken an introductory course in econometrics or time series analysis. The references to Time Series Models (TSM) in the course outline give an indication of the material covered.
  • The recently published Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series is primarily concerned with the topics in financial econometrics covered on the last day. It will be of particular interest to researchers who work in this area.
  • Some participants may find some of the maths difficult, but the lectures will stress the concepts and the implications for applied work.

Course 3: Structural VARS and Dynamic Stochastic General Equilibrium Models: Estimation and Solutions

  • Intermediate level University training (or equivalent) in econometrics is essential. Basic knowledge of time series concepts such as ARMA models, VARS, stationarity vs. non-stationarity.
  • Familiarity with EViews fundamentals (built-in functions) is required.

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.

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