# Quantitative methods for Economists

- Module Code:
- 153400121
- Credits:
- 30
- Year of study:
- Year 1 or Year 2
- Taught in:
- Full Year

This module offers a survey of applied mathematical analysis and economic statistical techniques useful for intermediate and advanced economic theory.

This is a core module for the BSc Economics and compulsory for BSc Development Economics and BA Economics and ... (two subjects degree with Economics to appear first in title). Introduction to Quantitative Methods for Economists (153400120) is a prerequisite for this module if taken in Year 2. Students with at least a B in A level mathematics or equivalent are permitted to take this course in Year 1, subject to the year tutor's approval. The module is a pre-requisite for Econometrics (153400103).

#### Prerequisites

(153400120) Introduction to Quantitative Methods for Economists

#### Objectives and learning outcomes of the module

On successful completion of the course, students should be able to:

- apply matrix and vector operations
- solve systems of equations using matrix algebra
- use Jacobian determinants to test for functional dependence
- demonstrate understanding of derivatives, rules of differentiation, partial derivatives, differentials and total differentials, higher order derivatives, their uses and applications
- find unconstrained and constrained optima for multivariate functions and use these techniques to solve problems in economics
- understand indefinite integrals and apply them to economic problems
- understand differential equations and difference equations and apply them to economic problems
- use index numbers to describe changes in economic measures
- use Lorenz curve and Gini coefficient to describe inequality
- explain and apply concepts in probability theory
- use characteristics or moments of probability density functions
- use population parameters and sample estimators
- demonstrate understanding of and ability to use distribution, chi-square, t and F distributions
- explain the sampling distribution of an estimator
- demonstrate understanding of measures of goodness of fit including their uses and limitations
- explain the assumptions of the classical linear regression model

use hypothesis test on regression coefficients

#### Method of assessment

Assessment weighting: Exam 80%, Assignments 20%

#### Suggested reading

**Mathematics**

- Chian, A.C. and K. Wainwright (2005) Fundamental Methods of Mathematical Economics, Fourth Edition. McGraw-Hill.
- Jacques, I. (2009) Mathematics for Economics and Business, 5th Edition, Prentice Hall.
- Wisniewski, M. (2003) Mathematics for Economics, 3rd Edition, Palgrave Macmillan.

**Statistics**

- Newbold, P., W. Carlson and B. Thorne (2013) Statistics for Business and Economics (8th edition), Pearson Education Edition.
- Barrow, M. (2009) Statistics for Economics Accounting and Business Studies, 5th Edition, Prentice-Hall.
- Diez, D., C. Barr and M. Cetinkaya-Rundel (2015) Openintro Statistics, 3rd Edition, Openintro.
- Illowsky, B., et al. (2013) Introductory Statistics, Openstax College.
- Larsen, R. J. and Marx, M. L. (2011) An Introduction to Mathematical Statistics and Its Applications, Pearson Education