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Module · Core/Applied Econometrics·Data Science & Econometrics

Microeconometrics and Panel Data Econometrics

This module provides students with an in-depth understanding of panel data econometrics presented from a microeconometrics perspective. It covers linear panel data models with unobserved heterogeneity, including discussion of the strengths and weaknesses of the various estimation methods, before extending to dynamic and non-linear panel models.

Prerequisites

To take this module you must first have completed Foundations of Econometrics.

Delivery

Teaching runs as four hours of lectures per day across five days, plus a one-hour tutorial each day and optional clinics on the final day.

Study Hours

The module carries 20 credits: approximately 200 hours of study, of which 25 are contact hours and 175 are private study.

Software Used

Stata

Credits

20 CATS credits

Module Lead
Dr. Malvina Marchese

Dr. Malvina Marchese

Programme Faculty

Key Outcomes

  • —Static panel models: random effects, fixed effects and first differencing estimators
  • —Instrumental variables estimation of models without strictly exogenous explanatory variables
  • —Dynamic panel models: Arellano–Bond and Arellano–Bover/Blundell–Bond estimators
  • —Estimation of non-linear cross-sectional models
  • —Non-linear panels: estimation, interpretation and testing

Desired Skills

  • —Engage in abstract thinking by extracting the essential features of complex systems to facilitate problem solving and decision-making
  • —Communicate and present complex arguments in oral and written form with clarity and succinctness
  • —Present, interpret and analyse information in numerical form
  • —Utilise statistical and other packages effectively
  • —Apply basic statistical techniques to analyse economic and financial datasets
  • —Work effectively both individually and within a team environment

Lecture Programme

  1. WK 01

    Lecture 1: Overview of ordinary least squares with cross-sectional data

    • ·Motivation for OLS: applications of conditional means
    • ·Assumptions of the OLS estimator
    • ·Asymptotic properties of OLS and inference
    • ·Practical issues in using OLS: functional form, goodness-of-fit
  2. WK 02

    Lecture 2: Overview of instrumental variables with cross-sectional data

    • ·Endogeneity
    • ·Two stage least squares (2SLS)
    • ·Specification tests and control function methods
    • ·Weak instruments and optimal instruments
    • ·Pooled models
  3. WK 03

    Lecture 3: The unobserved effects model and basic estimators

    • ·The unobserved effects linear model
    • ·Assumptions for the linear model
    • ·Estimation methods: pooled OLS, first differencing
  4. WK 04

    Lecture 4: Specification tests and choosing among estimators

    • ·Estimation methods: fixed effects (FE), random effects (RE)
    • ·Comparing RE and FE
    • ·Comparing FE and first differencing (FD)
  5. WK 05

    Lecture 5: Specification tests and difference-in-differences

    • ·Comparing RE and OLS
    • ·Robust Hausman test
    • ·Basic difference-in-differences
    • ·Many time periods
    • ·Staggered interventions
  6. WK 06

    Lecture 6: Unbalanced panels and large-T panels

    • ·The nature of unbalanced panels
    • ·Properties of basic estimators with unbalanced panels
    • ·Inference in unbalanced panels
    • ·Inference with large-T panels and cross-sectional correlation
  7. WK 07

    Lecture 7: Pooled 2SLS, RE 2SLS and FE 2SLS

    • ·Idiosyncratic endogeneity
    • ·Definition and properties of the estimators
    • ·Specification tests: endogeneity and overidentification
  8. WK 08

    Lecture 8: First-differencing IV approaches

    • ·Dynamic panel models
    • ·Estimation in models under sequential exogeneity: the Arellano–Bond approach
    • ·Adding extra moment conditions: Arellano–Bover / Blundell–Bond
    • ·Inference in dynamic models
  9. WK 09

    Lecture 9: Nonlinear models – part 1

    • ·Linear versus nonlinear models
    • ·Including heterogeneity in nonlinear models and estimation approaches
    • ·Quantities of interest: average partial effects
    • ·The incidental parameter problem
    • ·Binary response models and their interpretation
    • ·Random effects and fixed effects Poisson estimation
  10. WK 10

    Lecture 10: Nonlinear models – part 2

    • ·Quantile regression
    • ·Count models
    • ·Endogeneity in non-linear panel models
    • ·Non-linear panel data case study

Assessment

Online coursework submission with the support of your module lecturer and tutor. All sessions are conducted live online at a scheduled time and recorded for review.

Build your qualification

Take Microeconometrics and Panel Data Econometrics as a standalone module, or as part of a buildable MSc.

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