# Applied Econometrics

Module Code:
P101 (P545)

This module is about econometric methods and how they are applied to estimate and test the unknown parameters of economic relationships. Priority is given to both the statistical reasoning underlying the methodology and the practical considerations involved in using this methodology with a variety of models and real data.

There is a limit to the material that can be covered in less than 150 hours’ study time. In an econometrics module it is difficult to trade-off breadth and depth since, without good groundwork and sufficient information at each stage, ideas may be misunderstood and techniques misapplied. This module follows the standard structure of most econometrics textbooks. It deals only with single-equation models, but by the end of the module the student is ready to tackle simultaneous equation models, which would be the next step in increasing proficiency in applied econometrics.

#### Objectives and learning outcomes of the module

By the end of this module students should be able to:

• understand and selectively and critically apply the basic principles of regression analysis and statistical inference in the context of a single-equation regression model
• formulate a single-equation regression model, estimate its parameters, carry out a variety of tests relating to model specification and critically interpret all results
• test hypotheses about economic behaviour and critically interpret the results of these tests
• specify and interpret models using dummy variables, different types of dynamic specification and incorporate and test linear restrictions
• test for heteroscedasticity and endogeneity, and take appropriate action when these conditions are found to be present.

#### Scope and syllabus

The focus of the module is on the classical linear regression model. This is the basis for much econometric methodology and it provides the framework for organising the module. The module covers

• the principles of regression analysis and its statistical foundations
• the simple linear regression model
• the multiple linear regression model
• violations of the assumptions of classical linear regression