APME84-Introductory Statistics and Econometrics

Module Provider: School of Agriculture, Policy and Development
Number of credits: 10 [5 ECTS credits]
Level:7
Terms in which taught: Autumn term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded: APME71 Econometrics
Current from: 2021/2

Module Convenor: Prof Kelvin Balcombe
Email: k.g.balcombe@reading.ac.uk

Type of module:

Summary module description:

Learn how to analyse data using basic tools to answer questions in economics and other social sciences, through a combination of lectures and practical classes. Understand the fundamentals of regression analysis: model specification, hypothesis testing, coefficient interpretation. Learn how to translate data into models to make forecasts and to support decision making in a wide variety of fields, ranging from microeconomics to finance and marketing. The prerequisites for this course are familiarity with elementary mathematics and statistics.

Aims:

This module provides an introduction to two different regression techniques. At the end of this module students should be able to

• translate data into a regression model to make forecasts and to support decision making


• conduct hypothesis testing and interpret results


• handle data sets and use the software Gretl to carry out basic regression analyses


• interpret and critically evaluate regression model outputs

Assessable learning outcomes:

At the end of the modules, students should be able to:

• Understand how basic regression techniques are used to analyse data


• Combine data handling skills and econometric software skills to undertake applied econometric analysis and evaluate and interpret results

Additional outcomes:

Outline content:

1. Probability Theory I


2. Probability Theory II


3. Simple regression Models


4. Multiple Regression Models I


5. Multiple Regression – Application


6. Multiple Regression Models II


7. Single & joint restrictions


8. Hypothesis Testing – p-values


9. Logistic regression


10. Logistic Regression – Application

Brief description of teaching and learning methods:

Lectures will provide an understanding of fundamental concepts and demonstrate the use of data analysis methods. Practical classes will involve students analysing real data sets with a focus on learning the concepts taught in the lectures.