EC205-Intermediate Econometrics

Module Provider: School of Politics, Economics and International Relations
Number of credits: 20 [10 ECTS credits]
Level:5
Terms in which taught: Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites: EC204 Introductory Econometrics or EC204NU Introductory Econometrics
Modules excluded: EC207 Empirical Methods for Economics and Social Sciences
Current from: 2023/4

Module Convenor: Dr Shixuan Wang
Email: shixuan.wang@reading.ac.uk

Type of module:

Summary module description:

This module complements EC204 Introductory Econometrics and will provide foundations for econometrics and other modules at part three. It will explore more deeply the ordinary least squares (OLS) estimator and its properties, hypothesis testing, study departures from the standard assumptions, aspects of model specification, and will further develop data analytical skills in R.

Aims:

To provide a deeper understanding of the OLS regression and the underlying assumptions, including proofs and derivations of the estimator’s statistical properties; to develop an understanding of how to go about making model specification choices as an applied econometrician; to understand some alternative estimators and when these could be appropriate to model relationships between variables; to further develop statistical software, programming, and data handling skills.

Assessable learning outcomes:

At the end of the module students should be able to:

• Derive the OLS estimator and prove its statistical properties.


• Understand departures form the OLS assumptions and the consequences when data and models are not consistent with them.


• Understand more deeply hypothesis testing, estimation methods, and aspects of model specification.


• Understand some alternatives to OLS.


• Undertake econometric tasks using computer software.Ìý

Additional outcomes:

Greater familiarity with econometric software, programming, and the processes involved in handling data and preparing them for analysis.

Outline content:

The module may cover the following topics: simple linear regression, multiple linear regression with matrix algebra, and the Gauss Markov Theorem, statistical inference with OLS, heteroskedasticity, model specification testing, endogeneity and instrumental variables, qualitative independent variables, and limited dependent variables.

Brief description of teaching and learning methods:

Lectures, seminars, and computer classes; supported by independent study.