# FOUNDATIONS OF STATISTICS AND ECONOMETRICS - 2020/1

Module code: MANM467

## Module Overview

This module is an introduction to the methods of specification, estimation and testing of statistical and econometric models. The techniques are applied to business data making use of the statistical computer packages (e.g. Stata).

### Module provider

TAVALAEI Mahdi (SBS)

### Module cap (Maximum number of students): 100

Independent Learning Hours: 117

Lecture Hours: 22

Laboratory Hours: 11

Semester 1

N/A

## Module content

Review of descriptive statistics and statistical inference (e.g., estimation, hypotheses testing, etc.); Analysis of variance; Linear Regression basics, and OLS model; Multiple regression using cross sectional and panel data; Regression diagnostics (e.g., multicollinarity, serial correlation and heteroskedasticity); Endogeneity, causal inference, instrumental variables, and two-stage least squares (2SLS) regression; Supplementary topics (e.g., difference in difference method, nonlinear regressions, etc.)

## Assessment pattern

Assessment type Unit of assessment Weighting
School-timetabled exam/test A written exam about the probability and statistics and simple regressions 40
Project (Group/Individual/Dissertation) A final report on applying econometric analyses on a real world data set using statistical computer package 60

## Alternative Assessment

Coursework exercise which can be completed off-campus.

## Assessment Strategy

This module has a technical and a practical component. The latter is, at this level, more important. As such, assessment emphasises work based on applying theoretical knowledge using econometric and statistical package (mainly Stata) in the form of an off-class project, in which students are asked to analyse real economic data. The technical component is assessed via a written examination. Thus, the summative assessment for this module consists of: • 40% In-semester test: written exam about the probability and statistics and simple regressions. • 60% a final written report of applying econometric analyses on a real world dataset using computer package. Formative assessment and feedback is done by a Feedback section in Surreylearn, plus specific, individualised written comments, feedback meetings with students and general feedback in classes.

## Module aims

• Provide knowledge of fundamental concepts and models widely used in statistical and econometric analyses
• Apply theoretical concepts and use their understanding and insight gained throughout the unit to implement and interpret econometric models
• Use various types of quantitative data to make decisions in the business world

## Learning outcomes

 Attributes Developed 001 Display a fundamental understanding of the statistical and econometric analysis (such as assumptions, inference, and diagnostics), KC 002 Be able to build econometric models and analyse various types of quantitative data, and interpret the results, KPT 003 Be able to evaluate empirical research in the field of business critically, and propose and apply robustness tests, and KCPT 004 Be able to write up a full-fledged econometric analysis report on real-world data, and to communicate clearly and evaluate the findings critically. KCPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

## Methods of Teaching / Learning

The learning and teaching strategy is designed to: give students the theoretical (and practical) tools they need to analyse real world situations; encourage rigour in their approach to problems; encourage hands-on study of empirical problems; The learning and teaching methods include: readings using lecturers, solving exercises, responding to questions in class, preparing and taking part in the test.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.