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Econometrics Summer School, Cambridge

  • Location: Cambridge
  • Duration: 6 days
  • Software: Stata, EViews, OxMetrics
  • Level: Advanced, Intermediate
  • Delivered By: Prof. Andrew C. Harvey, University of Cambridge; Prof. Sean Holly, University of Cambridge; Dr. Melvyn Weeks, University of Cambridge
  • Topic: Econometrics, Statistics, Summer School, Various methods
 Econometrics Summer School, Cambridge

COURSE DATES:
24th - 29th July 2018

Our 2018 Econometrics Summer School, Cambridge will take place at Wolfson College, University of Cambridge.

The econometrics summer school comprises a series of three, 2-day 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 2018 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).

Accommodation

Accommodation within the college is included in the price and the details are to be confirmed

  • 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, 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 using Stata

Date: Tuesday 24 - Wednesday 25 July

Delivered by: Dr. Melvyn Weeks, University of Cambridge
Learning ratio: 65% theory and 35% practical

This course is designed to introduce participants to a number of concepts and estimators that represent central aspects of microeconometrics.

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, instrumen- tal variables, generalised method of moments, and 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 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.

Morning Session

09:30 - 11:00 - Session 1: Overview

-STATA Resources

09:30 - 11:00 - Session 2: Causality and Inference

  1. Causality and Inference
  2. Economic Policy Evaluation
  • Using Stata’s help facilities
  • Working with do-files
  • Loading data and checking for errors

Readings: Wooldridge (2013): Chapter 1

11:15 - 12:45 - Session 3: Estimation

  1. OLS and The Method of Moments
  2. Gauss Markov Assumptions

Readings: Wooldridge (2013): Chapters 2, 3, 4 & 15

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 5: 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 5a: 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

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
Binary Choice Models and Partial Effects
Do Workplace Smoking Bans Reduce Smoking

Afternoon Session

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

  1. What is Machine Learning
  2. Training, Testing and Cross Validation
  3. Classification and Regression Trees
  4. Causal Trees
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:
  • WKoop (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: Time Series Analysis & Modelling

Date: Thursday 26 July - Friday 27 July

Delivered by: Prof. Andrew C. Harvey, University of Cambridge
Learning ratio: 50% theory and 50% practical

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.

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 Ofind some of the maths difficult, but the lectures will stress the concepts and the implications for applied work.

Course outline:

Day 1:

  • Introduction. Stationary time series. Unobserved components and 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.
  • Spectral analysis. Spectra of ARMA processes; stochastic cycles; estimation of spectrum.

Day 2:

  • Trends and cycles. Seasonality. Detrending and differencing operations.
  • Multivariate time series models. Common trends and co-integration; control groups.
  • STAMP package.
  • Nonlinear models and financial econometrics. Stochastic volatility and ARCH; intra-day data.
  • Dynamic conditional score models.
  • Time-varying correlation.

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: Macroeconomic Modelling & Forecasting

Date: Saturday 28th July - Sunday 29 July

Delivered by: Prof. Sean Holly, University of Cambridge
Learning ratio: 55% theory and 45% practical

This course is designed to cover the elements of economic theory and econometrics that are needed to construct a macroeconometric model that can be used for forecasting and for macroeconomic policy analysis.

Course outline:

Day 1

Session 1:

  • Sources of Forecast error
  • Comparing and combining forecasts

Session 2:

  • Evaluating Forecasts
  • Nowcasting
  • Alternative Forecast Criteria
  • Types of Macro Model: Structural/Cowles Modelling
  • VARs, Structural VARs; GVARs; DSGE

Session 3 & 4:

  • Non-stationarity, unit roots, cointegration. Stepwise regression.
Day 2

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.

    Pre-requisites:

    • 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. Familiarity with EViews fundamentals (built-in functions) 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.
    • Download the EViews 9 Manuals via the EViews website (48mb) here

    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

    Accommodation within the college is included in the price and the details are to be confirmed

    • 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, 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.

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    Registration

    Find out more and register for the Summer School by contacting our sales and training team either by email: training@timberlake.co.uk or phone: +44 (0) 20 8697 3377

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Prerequisites

Course 1: Microeconometrics

  • A basic knowledge of Statistics and Regression Analysis is required. Analytical thinking is key for this course as its delivery will push you to think behind the intuition of these econometric concepts. Familiarity with Stata software is advisable.

Course 2: Time Series Analysis & Modelling

  • Participants are expected to have taken an introductory course in Econometrics or Time Series Analysis. The textbook '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: Macroeconomic Modelling & Forecasting

    • 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.
    • 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).
  • If you need assistance in locating hotel accommodation, please notify us at the time of booking.
  • Payment of course fees required 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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