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Econometrics Summer School, Cambridge

  • Location: University of 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

09:30 - 11:00 - 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 - 15:45 - 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.

    Return to menu

    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

    Return to menu

Prerequisites

Course 1: 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.

Principal Texts: Classical

  • Wooldridge, J. (2013) Introductory Econometrics: A Modern Approach, 5th edition. International Student Edition.
  • A. C. Cameron and P. K. Trivedi (2005) Microeconometrics: Methods and Applications. Cambridge University Press
  • A. C. Cameron and P. K. Trivedi (2009) Microeconometrics Using STATA. STATA Press
  • Wooldridge, J. (2010). Econometric Analysis of Cross-Section and Panel Data. MIT. 2nd edition

Principal Texts: Bayesian

  • Koop, G. (2003): Bayesian Econometrics
  • Rossi, P. McCullogh and Allenby (2009) Bayesian Statistics and Marketing , Wiley Series in probability and Statistics.
  • Rossi, P., Robert E. McCulloch, and Greg M. Allenby. 2005. Hierarchical Bayes: A Practitioners Guide. mimeo, Ohio State University.
  • Lancaster, T. (2004): An Introduction to Modern Bayesian Econometrics, Blackwell Publishing
  • Gilks, W., Richardson, S., and D. Spiegelhalter (1996) Markov Chain Monte Carlo in Practice, Chapman and Hall
  • Mariano, R., Schuermann, T., and M. Weeks (2008): Simulation- based Inference in Econometrics Methods and Applications, Cambridge University Press.
  • Greenberg, E. (2013): Introduction to Bayesian Econometrics, Second Edition, Cambridge University Press.
  • Koop, G. and Poirier, D. J. and Tobias, L. (2007) Bayesian Econometric Methods (Econometric Exercises), Cambridge University Press.
  • Train, K. (2008) Discrete Choice Methods with Simulation, Cambridge University Press (second edition).
  • Van Dijk, H.K., A. Monfort and B.W. Brown (eds) (1995). Econometric Inference Using Simulation Techniques, John Wiley and Sons, Chichester, West Sussex, England.

Policy Evaluation

  • Heckman, J. and Hotz, V. (1989). Choosing among Alternatives Non- experimental Methods for Estimating the Impact of Social Programs, Journal of the American Statistical Association, 84, 862-874.
  • Heckman, J., Ichimura, H. and Todd, P. (1997). Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme, The Review of Economic Studies, 64(4), 605-654.
  • Heckman, J. Ichimura, H. and Todd, P. (1998). Matching as an Econometric Evaluation Estimator, The Review of Economic Studies 65(2), 261-294.
  • Imbens, G. and Lemieux, T. (2007). Regression discontinuity designs: A guide to practice. Journal of Econometrics.
  • LaLonde, R. (1986). Evaluating the Econometric Evaluations of Training Programs with Experimental Data, American Economic Review, 76, 604-620.
  • Angrist, J.D. and J.-S. Pischke, (2009), Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press.
  • Joshua D. Angrist (2001) Estimation of Limited Dependent Variable Models with Dummy Endogenous Regressors: Simple Strategies for Empirical Practice. Journal of Business & Economic Statistics, Vol. 19, No. 1.
  • Joshua D. Angrist (2004) Treatment Effect Heterogeneity in Theory and Practice. The Economic Journal, Vol. 114.
  • Angrist, J. D. and A. Krueger (1999) Empirical Strategies in Labour Economics. in Handbook of Labour Economics, vol. 3A.
  • Heckman, J. J. Tobias and E. Vytlacil (2001) Four Parameters of Interest in the Evaluation of Social Programs. Southern Economic Journal, vol. 68, pp210-223.
  • Heckman, J. (2007) Schools, Skills and Synapses. Lecture at Peking University, China. http://jenni.uchicago,edu/papers/pku_2007/.
  • Blundell, R.W., and M. Costa Dias, (2002), “Alternative Approaches to Evaluation in Empirical Microeconomics.” Portuguese Economic Journal, 1, 91-115. http://cemmap.ifs.org.uk/wps/cwp0210.pdf

IV and Generalised Method of Moments

  • CHAPTER 6. Generalised Method of Moments and Systems Estimation in Cameron, C.A. and P.K. Trivedi (2005) Microeconometrics: Methods and Applications. Cambridge University Press.
  • Angrist, J. “Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from Social Security Administrative Records”, American Economic Review, June 1990.
  • Angrist, J. “Using Social Security Data on Military Applicants to Estimate the Effect of Military Service on Earnings”, Econometrica, March 1998.
  • Newey, W. “Generalised Method of Moments Specification Testing”, Journal of Econometrics 29, 1985, 229-256.
  • Newey, W. and K. West, “Hypothesis Testing with Efficient Method of Moments Estimation”, International Economic Review 28, October 1987, 777-787.
  • Wooldridge, Jeffrey. M. 2001. ”Applications of Generalized Method of Moments Estimation.” Journal of Economic Perspectives, 15(4): 87– 100.

Weak Instruments

  • Bound, J. Jaeger, D. and Baker, R. “Problems with Instrumental Variables Estimation when the Correlation Between the Instruments and the Endogeneous Regressors is Weak”, JASA, June 1995.
  • Murray, M.P. (2006) “Avoiding Invalid Instruments and Coping with Weak Instruments”. The Journal of Economic Perspectives, Vol. 20, No. 4 (Fall, 2006), pp. 111-132
  • Stock, J.H. and Yogo, M. (2005). Testing for Weak Instruments in Linear IV Regression. Chapter 5 in J.H. Stock and D.W.K. Andrews (eds) Identification and Inference for Econometric Models: Essays in Honor of Thomas J. Rothenberg, Cambridge University Press. Originally published 2001 as NBER Technical Working Paper No. 284; newer version 2004.
  • Stock, J.H., Wright, J.H. and Yogo, M. (2002). A Survey of Weak In- struments and Weak Identification in Generalised Method of Moments, Journal of Business and Economic Statistics, 20, 518-529.

Robust Estimation

  • Bramati, M.C. and C. Croux (2007). Robust Estimators for the Fixed Effects Panel Data Model. Econometrics Journal 10(3): 521-540.
  • Edgeworth, F.Y. (1887). On Observations Relating to Several Quanti- ties, Hermathena, 6: 279-285.
  • Huber, P. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics 35(1): 73-101.
  • Rousseeuw, P.J. and a. Leroy (1987). Robust Regression and Outlier Detection. New York: John Wiley and Sons.
  • Rousseeuw, P.J. and B. van Zomeren (1990). Unmasking Multivariate Outliers and Leverage Points. Journal of the American Statistical Association 85: 633-639.

Quantile Regression

  • Angrist, J., V. Chernozhukov and I. Fern ́andez-Val (2006). “Quantile Regression under Misspecification with an Application to the U.S. Wage Structure”, Econometrica, 74, 539-563.
  • Chernozhukov, V. and C. Hansen (2006). “Instrumental Quantile Regression Inference for Structural and the Treatment of Effect Models”, Journal of Econometrics, 132, 491-525.
  • Koenker, R. and G. Basset (1978): “Regression Quantiles”, Economet- rica, 46, 33-50.
  • Machado, J. and J. Mata (2005). “Counterfactual Decomposition of Changes in Wage Distributions using Quantile Regression”, Journal of Applied Econometrics, 20, 45-465.
  • Powell, J.L. (1986). “Censored Regression Quantiles”, Journal of Econo- metrics, 32, 143-155.
  • Chernozhukov, V. and Hong, H. (2002). “The Effects of 401(k) partici- pation on the wealth distribution: An instrumental Quantile Regression Analysis”. Review of Economics and Statistics 86(3), 735-751.

Quantile Regression

  • Angrist, J., V. Chernozhukov and I. Fern ́andez-Val (2006). “Quantile Regression under Misspecification with an Application to the U.S. Wage Structure”, Econometrica, 74, 539-563.
  • Chernozhukov, V. and C. Hansen (2006). “Instrumental Quantile Regression Inference for Structural and the Treatment of Effect Models”, Journal of Econometrics, 132, 491-525.
  • Koenker, R. and G. Basset (1978): “Regression Quantiles”, Economet- rica, 46, 33-50.
  • Machado, J. and J. Mata (2005). “Counterfactual Decomposition of Changes in Wage Distributions using Quantile Regression”, Journal of Applied Econometrics, 20, 45-465.
  • Powell, J.L. (1986). “Censored Regression Quantiles”, Journal of Econo- metrics, 32, 143-155.
  • Chernozhukov, V. and Hong, H. (2002). “The Effects of 401(k) partici- pation on the wealth distribution: An instrumental Quantile Regression Analysis”. Review of Economics and Statistics 86(3), 735-751.

Static Panel Data Models

  • Chamberlain, G. (1984). Panel Data, Handbook of Econometrics, Vol- ume 2, ed. Z. Griliches and M. D. Intriligator. North Holland, 1248-1318.
  • Mundlak, Y. (1978). On the Pooling of Time series and Cross Section Data, Econometrica, 46, 69-85.

Dynamic Panel Data Models

  • CHAPTER 21 [22.5]. Linear Panel Data Models: Extensions, C.A. and P.K. Trivedi (2005) Microeconometrics: Methods and Applications. Cambridge University Press.
  • Arellano, M. and Bond, S.R. (1991), Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations, Review of Economic Studies, 58, 277-297.
  • Arellano, M. and Bover, O. (1995), Another look at the instrumental variable estimation of error-components models, Journal of Economet- rics, 68, 29-52.
  • Blundell, R.W. and Bond, S.R. (1998), Initial conditions and moment restrictions in dynamic panel data models, Journal of Econometrics, 87, 115-143
  • Bundell, R.W. and Bond, S.R. (2000), GMM estimation with persis- tent panel data: an application to production functions, Econometric Reviews, 19, 321-340.
  • Bond, S.R., Hoeffler, A. and Temple, J. (2001), GMM estima- tion of empirical growth models, CEPR Discussion Paper no. 3048 (http://www.cepr.org/pubs/new-dps/dplist.asp?dpno=3048)
  • Arellano, M. (2003), Panel Data Econometrics, Oxford University Press
  • Wooldridge, J.M. (2001), Econometric Analysis of Cross Section and Panel Data, MIT
  • Roodman, J.M. (2006), How To Do Xtabond2, Working Paper Center for Global Development, No. 103
  • Judson, R. A. and A. L. Owen (1999), Estimating Dynamic Panel Data Models: A Guide for Macroeconomists, Economic Letter, vol. 65, pp. 9-15.
  • S. Bond (2002), Dynamic Panel Data Models: A Guide to Micro Data Methods and Practice, Cemmap Workin Paper CWP09/02, Institiute for Fiscal Studies.

Econometrics of Discrete Choice

  • CHAPTER 15, Multinomial Models in Cameron, C.A. and P.K. Trivedi (2005) Microeconometrics: Methods and Applications. Cambridge Uni- versity Press.
  • Kenneth, E. Train (2003), Discrete Choice Methods with Simulation, Cambridge University Press, Cambridge, UK.
  • Chesher, A. and J. Santos-Silva (2002) “Taste variation in discrete choice models”, Review of Economic Studies 69, 62-78.
  • Daganzo, C. (1979), Multinomial Probit: The Theory and its Application to Demand Forecasting, Academic Press, New York.
  • Hensher, D. and W. Greene (2001), “The mixed logit model: The state of practice and warnings for the unwary”, Working Paper, School of Business, The University of Sydney.
  • McFadden, D. (2001), “Economic choices”, American Economic Review 91, 351-378. Bond, S.R., Hoeffler, A. and Temple, J. (2001), GMM estima- tion of empirical growth models, CEPR Discussion Paper no. 3048 (http://www.cepr.org/pubs/new-dps/dplist.asp?dpno=3048)
  • McFadden, D. and K. Train (2000), “Mixed MNL models of discrete response”, Journal of Applied Econometrics 15, 447-470.
  • McFadden, D., K. Train and W. Tye (1978), “An application of diag- nostic tests for the independence from irrelevant alternatives property of the multinomial logit model”, Transportation Research Record 637, 39-46.
  • Revelt, D. and K. Train (1998), “Mixed logit with repeated choices”, Review of Economics and Statistics 80, 647-657.
  • Train, K. (1999), “Mixed logit models for recreation demand”, in J. Herriges and C. Kling, eds., Valuing Recreation and the Environment, Edward Elgar, Northampton, MA.

Towards Flexible Substitutions Patterns

  • Anderson, S. A. de Palma, and F. Thisse (1989). “Demand for Differ- entiated Products Choice Models and the Characteristics Approach”, Review of Economic Studies, 56, 2.
  • Berry, S. (1994). “Estimating Discrete Choice Models of Product Differentiation”, RAND Journal of Economics, 25, 242-262.
  • Berry, S. J. Levinsohn, and A. Pakes (1995). “Automobile Prices in Market Equilibrium”. Econometrica Vol. 63, No. 4 (July) 841-890.
  • Brownstone, D. and K. Train (1999), “Forecasting new product penetra- tion with flexible substitution patterns”, Journal of Econometrics 89, 109-129.
  • McFadden, D. and K. Train (1996), “Consumers’ evaluation of new products: Learning from self and others”, Journal of Political Economy 104, 683-703.
  • Petrin, A. (2002). “Quantifying the Benefits of New Products: The Case of the Minivan”. Journal of Political Economy, vol. 110, no. 4. pp. 705- 729.
  • Goldberg, P.K. (1995). ”Product Differentiation and Oligopoly in International Markets: the Case of the US Automobile Industry”. Econometrica 63 (July), pp. 891-951.
  • Nevo, A. (2000) ”Mergers with differentiated products: the case of the ready-to-eat cereal industry”. Rand Journal of Economics, Vol. 31, pp. 395-421.
  • Nevo, A. (2001) ”Measuring Market Power in the Ready-to-Eat Cereal Industry”. Econometrica 69(2), pp. 307-342.
  • Reiss, P. and Wolak, F. (2004) ”Structural Econometric Modeling: Rationales and Examples from IO”. Handbook of Econometrics, Vol. 5
  • Pakes, A. (2003) ”Common Sense and Simplicity in Empirical Industrial Organization” Working Paper 10154, NBER, Cambridge, Mass. USA

Static Panel Data Models

  • Chamberlain, G. (1984). Panel Data, Handbook of Econometrics, Vol- ume 2, ed. Z. Griliches and M. D. Intriligator. North Holland, 1248-1318.
  • Mundlak, Y. (1978). On the Pooling of Time series and Cross Section Data, Econometrica, 46, 69-85.

Dynamic Panel Data Models

  • CHAPTER 21 [22.5]. Linear Panel Data Models: Extensions, C.A. and P.K. Trivedi (2005) Microeconometrics: Methods and Applications. Cambridge University Press.
  • Arellano, M. and Bond, S.R. (1991), Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations, Review of Economic Studies, 58, 277-297.
  • Arellano, M. and Bover, O. (1995), Another look at the instrumental variable estimation of error-components models, Journal of Economet- rics, 68, 29-52.
  • Blundell, R.W. and Bond, S.R. (1998), Initial conditions and moment restrictions in dynamic panel data models, Journal of Econometrics, 87, 115-143
  • Bundell, R.W. and Bond, S.R. (2000), GMM estimation with persis- tent panel data: an application to production functions, Econometric Reviews, 19, 321-340.
  • Bond, S.R., Hoeffler, A. and Temple, J. (2001), GMM estima- tion of empirical growth models, CEPR Discussion Paper no. 3048 (http://www.cepr.org/pubs/new-dps/dplist.asp?dpno=3048)
  • Arellano, M. (2003), Panel Data Econometrics, Oxford University Press
  • Wooldridge, J.M. (2001), Econometric Analysis of Cross Section and Panel Data, MIT
  • Roodman, J.M. (2006), How To Do Xtabond2, Working Paper Center for Global Development, No. 103
  • Judson, R. A. and A. L. Owen (1999), Estimating Dynamic Panel Data Models: A Guide for Macroeconomists, Economic Letter, vol. 65, pp. 9-15.
  • S. Bond (2002), Dynamic Panel Data Models: A Guide to Micro Data Methods and Practice, Cemmap Workin Paper CWP09/02, Institiute for Fiscal Studies.

Dynamic Binary Choice

  • Bernard, A.B. and J. B. Jensen (2004). ‘Why Some Firms Export’. The Review of Economics and Statistics, 86(2), 561-569.
  • Carrasco, R. (2001). ‘Binary Choice with Binary Endogenous Regressors in Panel Data: Estimating the Effect of Fertility on Female Labor Participation’. Journal of Business and Economic Statistics, 19(4). American Statistical Association.
  • Chay, K. and D. Hyslop (1998). Identification and Estimation of Dynamic Binary Response Panel Data Models: Empirical Evidence using Alternative Approaches. mimeo, Dept. of Economics, UCLA.
  • Dong, Y. and Lewbel, A. (2012). ‘Simple Estimators for Binary Choice Models with Endogenous Regressors’. Research Paper. University of California Irvine and Boston College.
  • Wooldridge, J.M. (2002). ‘Simple Solutions to the Initial Conditions Problem in Dynamic, Nonlinear Panel Data Models with Unobserved Heterogeneity’. Cenmap Working Paper CWP18/02. The Institute for Fiscal Studies, Department of Economics, UCL.
  • Stewart, M. (2006). ‘Maximum simulated likelihood estimation of random-effects dynamic probit models with autocorrelated errors’. The Stata Journal 6(2) 256-272.

Fundamentals of Bayesian Inference

  • Akaike H. (1973). Information Theory and an Extension of the Maximum Likelihood Principle. In Second International Symposium on Information Theory, Petrov B, Csake F. (eds). Akademiai Kiado: Budapest.
  • Geweke J. (1993). “Bayesian Treatment of the Independent Student-t Linear Model. Journal of Applied Econometrics 8: S19-S40.
  • Kass R., Raftery A. (1995). Bayes Factors. Journal of the American Statistical Association 90(430): 773-95.
  • Leamer E.E. (1973). Multicollinearity: A Bayesian Interpretation. Review of Economics and Statistics 55(3): 371-80.
  • Smith, A., & A. Gelfand (1992). Bayesian Statistics without Tears: A SamplingResampling Perspective. The American Statistician, vol. 46, Issue 2.

Markov Chain Monte Carlo

  • Albert, J., and S. Chib (1993) Bayesian Analysis of Binary and Polychoto- mous Response Data, Journal of the American Statistical Association, Vol. 88, No. 422 pp. 669-679.
  • Casella, G., George, E.I. (1992). Explaining the Gibbs Sampler. The American Statistician, 46, 167 174.
  • Gelfand, A. E., and F. M. Adrian Smith (1990) Sampling-Based Ap- proaches to Calculating Marginal Densities, Journal of the American Sta- tistical Association, Vol. 85, No. 410. , pp. 398-409.
  • Wei, G., and M. Tanner (1990) A Monte Carlo Implementation of the EM Algorithm and the Poor Man’s Data Augmentation Algorithm, Journal of the American Statistical Association Vol. 85, No. 411 (Sep., 1990), pp. 699-704.
  • Metropolis, N. & S. Ulam (1949). The Monte-Carlo method. Journal of the American Statistical Association 44:335-341.
  • Metropolis, N., A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller & E. Teller (1953). Equation of state calculations by fast computing machines. Journal of Chemical Physics 21: 1087-1092.
  • https://en.wikipedia.org/wiki/MetropolisHastings_algorithm
  • Tanner, M., and W. Wong (2010) From EM to Data Augmentation: The Emergence of MCMC Bayesian Computation in the 1980s. Statistical Science Vol. 25, No. 4, 506516.

Machine Learning

  • L. Breiman, J. Freidman, R. Olshen, C. Stone. Classification and Regression Trees. Klein-Verlag, 1990
  • J. Freidman, T. Hastie, R. Tibshirani. The Elements of Statistical Learning. Springer, 2009.
  • G. James, D. Witten, T. Hastie, R. Tibshirani. An Introduction to Statistical Learning with Applications in R. Springer, 2013.
  • S. Athey, G. Imbens. Recursive partitioning for heterogeneous causal effects Proceedings of the National Academy of Sciences, 113(27):7353– 7360, 2016.

Bayesian Discrete Choice Models [Not Covered]

  • CHAPTER 13, Bayesian Methods in Cameron, C.A. and P.K. Trivedi (2005) Microeconometrics: Methods and Applications. Cambridge Uni- versity Press.
  • Albert, J. and S. Chib (1993), “Bayesian analysis of binary and polychotomous response data”, Journal of the American Statistical Association 88, 669-679.
  • Allenby, G.M. and P.E. Rossi (1999) “Marketing Models of Consumer Heterogeneity” Journal of Econometrics 89, 57-78.
  • Chib, S. and E. Greenberg (1998), “Analysis of multivariate probit models”, Biometrika 85, 347-361.
  • McCulloch, R. and P. Rossi (2000), “An exact likelihood analysis of the multinomial probit model”, Journal of Econometrics 64, 207-240.
  • McCulloch, R. and P. Rossi (2000), “Bayesian analysis of the multi- nomial probit model” in R. Mariano, T. Schuermann and M. Weeks (eds). Simulation-Based Inference in Econometrics, Cambridge Univer- sity Press, New York.
  • Poirier, D.J. (1996) “A Bayesian Analysis of Nested Logit Models” Journal of Econometrics 75, 163-181.

Count Data [Not Covered]

  • CHAPTER 20, Models of Count Data in Cameron, C.A. and P.K. Trivedi (2005) Microeconometrics: Methods and Applications. Cam- bridge University Press.
  • Cameron, A.C. and P.K. Trivedi (1986), “Econometric Models Based on Count Data: Comparisons and Applications of Some Estimators”, Journal of Applied Econometrics, 1, 29-53.
  • Cameron, A.C. and P.K. Trivedi (1998), Regression Analysis for Count Data, Econometric Society Monograph No. 30, Cambridge UK, Cam- bridge University Press.
  • Gourieroux, C., A. Monfort, and A. Trignon (1984), “Pseudo-Maximum Likelihood Methods: Applications to Poisson Models,” Econometrica, 52, 701-720.

Course 3: 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.

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