Module Directory

This module provides an introduction to the econometric methods commonly used to analyse economic data. The focus of the module is on linear regression and the ordinary least squares (OLS) estimation procedure. This is introduced after a review of basic probability, random variables, simple descriptive statistics and the concept of hypothesis testing.

Students will then be taught about how t-tests and F-tests can be used to make inferences about the estimated parameters in economic models and what diagnostic tests are available to test some of the assumptions of the regression model. The module then moves on to consider further model specification and data issues as well as the concept of heteroskedasticity before concluding with methods appropriate for the analysis of time series data. The methods will be illustrated throughout using the R software package with real-world economic data.

The aims of this module are:

• To carry out simple statistical and econometric calculations and understand how these methods are used with real data.


• To understand and assess critically empirical findings reported in the applied economics literature.


• To use the linear regression model and econometric methods developed for it to conduct a simple econometric investigation of an empirical issue of interest.


• To develop problem-solving analytical and deductive skills.

By the end of this module, students will be expected to be able to:

1. Apply econometric methods to real world data and perform statistical and econometric analysis.


2. Perform and understand econometric tests and Statistical inference.


3. Understand and critically assess empirical economic findings.


4. Formulate coherent written statistical analyses and assessments of the subject in the preparation of an assignment and in more concise examination answers.

Syllabus

• Review of Probability


• Review of Statistics


• The Simple Regression Model


• Multiple Regression Analysis. Estimation


• Multiple Regression Analysis: Inference


• Multiple Regression Analysis. Further Inference and Asymptotics


• Further Specification and Data Issues


• Heteroskedasticity


• Regression Analysis with Time Series Data


• Review and the Econometrics Exercise

The module will be delivered via:

• Two lecture hours per week for ten weeks in the Autumn term.

One class hour per week would be held for specific purposes (to provide guidance and support for assignment preparation, problem sets, and discussion of topical issues related to the module's material). Laboratories will also be held as needed to support statistical work.