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Panel Data for Banking Sector Analysts

Registrations open in Autumn 2018

 
  • General description

    The course will cover the basics of panel data analysis and some more advanced extensions, focusing mainly on microeconometric settings with a large number of cross-sectional observations.

    The common estimators – random effects, fixed effects, and first differencing will be discussed, with emphasis on robust inference and specification tests. Extensions that allow heterogeneous slopes and trends, and instrumental variables methods, will also be covered. Estimation of dynamic models also will be covered. Fixed effects estimation and inference with a large number of time periods, applicable to more aggregated data, will also be treated. The problem of unbalanced panels and how to test for nonrandom sample selection will be covered. If time permits, nonlinear models for binary and nonnegative outcomes will be introduced.

    The statistical package Stata will be used to illustrate all of the methods, including applications to the banking sector.

  • Topics covered

    • Random Effects, Fixed Effects, First Differencing
    • Robust Inference and Robust Specification Tests
    • Instrumental Variables
    • Heterogeneous Trend and Slope Models
    • Dynamic Models
    • Large-T Panels
    • Correlated random effects approaches to panel data
    • Unbalanced panels and detecting sample selection problems
    • Nonlinear Panel Data Models
  • What you will learn

    • You will learn to use Stata to estimate basic linear panel data models by random effects, fixed effects, first differencing, and instrumental variables versions of these.
    • You will learn how to use robust specification tests to choose among estimation methods.
    • You will learn what happens when additional heterogeneity is introduced into the basic model.
    • You will be introduced to large T panel data sets.
    • You will understand the consequences of unbalanced panel data sets.
    • You will understand the tradeoffs between pooled and joint estimation methods for nonlinear models.

  • How the course will work

    Total course length: 15 hours.

    The course will feature lectures (9 hours) as well as tutorial sessions with hands on Stata work, followed by discussion of solutions (6 hours).

    A certificate of attendance will be provided to all participants after the course.

  • Meet the instructor

    Jeffrey Wooldridge is University Distinguished Professor of Economics at Michigan State University. He is a Fellow of the Econometric Society and the Journal of Econometrics. He is the author of two textbooks in econometrics: Introductory Econometrics: A Modern Approach, 6e; and Econometric Analysis of Cross Section and Panel Data, 2e. He has served on several editorial boards, including as editor for the Journal of Business and Economic Statistics as co-editor of Economics Letters. While an assistant professor at MIT, he won the graduate teacher-of-the-year award three times. He has given dozens of econometrics short courses around the world.

  • Prerequisites

    MA degree in business, economics, or statistics. Or, a BA in economics with training in linear algebra and multivariable calculus. A knowledge and understanding of ordinary least squares, generalized least squares, instrumental variables (using matrix algebra), some asymptotic theory is required to follow the course

    Technical Prerequisites

    Participant are required to bring their own laptops with the STATA software installed.

  • Fees

    Information on course fees will be published in Autumn 2018.

  • Practical information

    Accommodation 

    Recommended hotels:
    Hotel Villa La Stella (20 min walking distance from EUI )
    Hotel Cellai
    Hotel Palazzo Ricasoli
    Hotel de la Pace
    Hotel Athenaeum

    Wi-Fi

    On arrival, participants will be provided with temporary wi-fi access for the whole duration of the course.

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