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Module · Core/Applied Econometrics·Data Science & Econometrics

Foundations of Econometrics

This module establishes the statistical and econometric foundations required across the postgraduate programme. Students develop a rigorous understanding of estimation and inference in the linear regression model, including the assumptions that underpin OLS, the consequences of their violation, and the diagnostic tools used to detect and correct misspecification.

Prerequisites

No prior econometrics study is required. This module is the foundation for many advanced modules, including Microeconometrics and Panel Data Econometrics.

Delivery

Live online lectures and tutorials, conducted at scheduled times and recorded for review.

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

EViews

Credits

20 CATS credits

Module Lead
Prof. Lorenzo Trapani

Prof. Lorenzo Trapani

Programme Faculty

Key Outcomes

  • —Basic statistics and its application to econometrics
  • —The process of quantitative research enquiry — formulating research questions, identifying methodological frameworks for empirical problems, and analysing results verbally, graphically and econometrically

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 econometric 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: Foundations of statistics

    • ·Descriptive statistics
    • ·Testing: basic notions, examples
    • ·Estimation: definition of estimators
    • ·Properties of estimators: finite and large sample properties
  2. WK 02

    Lecture 2: The maximum likelihood estimator and OLS estimation

    • ·The maximum likelihood estimator (MLE): definition and properties
    • ·The MLE: discussion
    • ·The simple linear regression model: OLS estimation
    • ·Assumptions for the linear regression model
    • ·The properties of OLS
  3. WK 03

    Lecture 3: The multiple linear regression model – part 1

    • ·Definition of the multiple linear regression model
    • ·The ceteris paribus interpretation
    • ·Deriving the ordinary least squares (OLS) estimates in a multivariate context
    • ·Goodness-of-fit and information criteria
    • ·Illustration through worked examples
  4. WK 04

    Lecture 4: The multiple linear regression model and misspecification analysis – part 1

    • ·Testing — Hypotheses about a single population parameter: the t test
    • ·Testing — Hypotheses about a single linear combination of the parameters
    • ·Testing — Multiple linear restrictions: the F test
    • ·Heteroskedasticity: evaluating the implication for the OLS estimator of heteroscedasticity, testing, robust standard errors, the GLS estimator
  5. WK 05

    Lecture 5: The multiple linear regression model and misspecification analysis – part 2

    • ·Serial correlation: evaluating the implication for the OLS estimator of serial correlation, testing, dynamic models
    • ·Normality: evaluating the implication for the OLS estimator of lack of normality, testing, removing outliers
    • ·Linearity of the functional form: the RESET test
    • ·Logarithmic functional forms, models with quadratics and with interactions

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.

Indicative Reading

  • • R C Hill, W E Griffiths & G C Lim (2018), Principles of Econometrics, Wiley, 5th Edition
  • • W E Griffiths, R C Hill and G C Lim, Using EViews for Principles of Econometrics, Wiley, 5th Edition
  • • J. M. Wooldridge (2012) Introductory Econometrics: A Modern Approach, CENGAGE Learning Custom Publishing, 5th edition
  • • C Dougherty (2011) Introduction to Econometrics, Oxford University Press
  • • Pindyck, R. S. and D. L. Rubinfeld (1997), Econometric Models and Economic Forecasts, 4th edition, McGraw-Hill
  • • W. H. Greene (2011) Econometric Analysis, Pearson Education
  • • H. Maylor and K. Blackmon, Researching Business and Management (Palgrave Macmillan, 2005)
  • • Maddala, G (1983). Limited Dependent and Qualitative Variables in Econometrics, Cambridge University Press
  • • Gujarati, D. (1995). Basic Econometrics, McGraw-Hill, New York
  • • Kennedy, P (1993). A Guide to Econometrics, Basil Blackwell, Oxford
  • • Maddala, G. (1992). Introduction to Econometrics, Macmillan, Ontario
  • • Stewart, M. and Wallis, K. (1990). Introductory Econometrics, Basil Blackwell, Oxford
Build your qualification

Take Foundations of Econometrics as a standalone module, or as part of a buildable MSc.

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