MATH0001: Numbers
Semester 1
Credits: 6
Topic: Mathematics
Level: Level 1
Assessment: EX100
Requisites:
Aims & Learning Objectives: Aims: This course is designed
to cater for first year students with widely different backgrounds
in school and college mathematics. It will treat elementary matters
of advanced arithmetic, such as summation formulae for progressions
and will deal matters at a certain level of abstraction. This
will include the principle of mathematical induction and some
of its applications. Complex numbers will be introduced from first
principles and developed to a level where special functions of
a complex variable can be discussed at an elementary level.
Objectives: Students will become proficient in the use of mathematical
induction. Also they will have practice in real and complex arithmetic
and be familiar with abstract ideas of primes, rationals, integers
etc, and their algebraic properties. Calculations using classical
circular and hyperbolic trigonometric functions and the complex
roots of unity, and their uses, will also become familiar with
practice.
Content: Natural numbers, integers, rationals and reals.
Highest common factor. Lowest common multiple. Prime numbers,
statement of prime decomposition theorem, Euclid's Algorithm.
Proofs by induction. Elementary formulae. Polynomials and their
manipulation. Finite and infinite APs, GPs. Binomial polynomials
for positive integer powers and binomial expansions for non-integer
powers of a+b. Finite sums over multiple indices and changing
the order of summation. Algebraic and geometric treatment of complex
numbers, Argand diagrams, complex roots of unity. Trigonometric,
log, exponential and hyperbolic functions of real and complex
arguments. Gaussian integers. Trigonometric identities. Polynomial
and transcendental equations.
Students must have A-level Mathematics, normally Grade B or better,
or equivalent, in order to undertake this unit.
MATH0033: Statistical inference 1
Semester 1
Credits: 6
Topic: Statistics
Level: Level 2
Assessment: EX100
Requisites: Pre MATH0031, Pre MATH0032
Aims & Learning Objectives: Aims: Introduce classical
estimation and hypothesis-testing principles.
Objectives: Ability to perform standard estimation procedures
and tests on normal data. Ability to carry out goodness-of-fit
tests, analyse contingency tables, and carry out non-parametric
tests.
Content: Point estimation: Maximum-likelihood estimation;
further properties of estimators, including mean square error,
efficiency and consistency; robust methods of estimation such
as the median and trimmed mean.
Interval estimation: Revision of confidence intervals.
Hypothesis testing: Size and power of tests; one-sided and two-sided
tests. Examples. Neyman-Pearson lemma.
Distributions related to the normal: t, chi-square and
F distributions.
Inference for normal data: Tests and confidence intervals for
normal means and variances, one-sample problems, paired and unpaired
two-sample problems. Contingency tables and goodness-of-fit tests.
Non-parametric methods: Sign test, signed rank test, Mann-Whitney
U-test.
MATH0035: Statistical inference 2
Semester 2
Credits: 6
Topic: Statistics
Level: Level 2
Assessment: EX75 CW25
Requisites: Pre MATH0033
Aims & Learning Objectives: Aims: Introduce the principles
of building and analysing linear models.
Objectives: Ability to carry out analyses using linear Gaussian
models, including regression and ANOVA. Understand the principles
of statistical modelling.
Content: One-way analysis of variance (ANOVA): One-way
classification model, F-test, comparison of group means.
Regression: Estimation of model parameters, tests and confidence
intervals, prediction intervals, polynomial and multiple regression.
Two-way ANOVA: Two-way classification model. Main effects and
interaction, parameter estimation, F- and t-tests.
Discussion of experimental design.
Principles of modelling: Role of the statistical model. Critical
appraisal of model selection methods. Use of residuals to check
model assumptions: probability plots, identification and treatment
of outliers.
Multivariate distributions: Joint, marginal and conditional distributions;
expectation and variance-covariance matrix of a random vector;
statement of properties of the bivariate and multivariate normal
distribution. The general linear model: Vector and matrix notation,
examples of the design matrix for regression and ANOVA, least
squares estimation, internally and externally Studentized residuals.
MATH0084: Linear models
Semester 1
Credits: 6
Topic: Statistics
Level: Level 3
Assessment: EX100
Requisites: Pre MATH0035, Pre MATH0002, Pre MATH0003, Pre MATH0005,
Pre MATH0008
Aims & Learning Objectives: Aims To present the theory
and application of normal linear models and generalised linear
models, including estimation, hypothesis testing and confidence
intervals. To describe methods of model choice and the use of
residuals in diagnostic checking.
Objectives On completing the course, students should be able to
(a) choose an appropriate generalised linear model for a given
set of data; (b) fit this model using the GLIM program, select
terms for inclusion in the model and assess the adequacy of a
selected model; (c) make inferences on the basis of a fitted model
and recognise the assumptions underlying these inferences and
possible limitations to their accuracy.
Content: Normal linear model: Vector and matrix representation,
constraints on parameters, least squares estimation, distributions
of parameter and variance estimates, t-tests and confidence
intervals, the Analysis of Variance, F-tests for unbalanced
designs.
Model building: Criteria for use in model selection including
Mallows Cp statistic, the PRESS criterion, Akaike's information
criterion. Subset selection and stepwise regression methods with
applications in polynomial regression and multiple regression.
Effects of collinearity in regression variables. Implications
of model choice on subsequent inferential statements.
Uses of residuals: Probability plots, added variable plots, plotting
residuals against fitted values to detect a mean-variance relationship,
standardised residuals for outlier detection, masking.
Generalised linear models: Exponential families, standard form,
statement of asymptotic theory for i.i.d. samples, Fisher information.
Linear predictors and link functions, statement of asymptotic
theory for the generalised linear model, applications to z-tests
and confidence intervals, c²-tests
and the analysis of deviance. Residuals from generalised linear
models and their uses. Applications to bioassay, dose response
relationships, logistic regression, contingency tables.
MATH0085: Time series
Semester 1
Credits: 6
Topic: Statistics
Level: Level 3
Assessment: EX100
Requisites: Pre MATH0035
Aims & Learning Objectives: Aims To introduce a variety
of statistical models for time series and cover the main methods
for analysing these models.
Objectives At the end of the course, the student should be able
to
* compute and interpret a correlogram and a sample spectrum
* derive the properties of ARIMA and state-space models
* choose an appropriate ARIMA model for a given set of data and
fit the model using the MINITAB package
* compute forecasts for a variety of linear methods and models.
Content: Introduction: Examples, simple descriptive techniques,
trend, seasonality, the correlogram.
Probability models for time series: Stationarity; moving average
(MA), autoregressive (AR), ARMA and ARIMA models.
Estimating the autocorrelation function and fitting ARIMA models.
Forecasting: Exponential smoothing, Box-Jenkins method.
Stationary processes in the frequency domain: The spectral density
function, the periodogram, spectral analysis.
Bivariate processes: Cross-correlation function, cross spectrum.
Linear systems: Impulse response, step response and frequency
response functions.
State-space models: Dynamic linear models and the Kalman filter.
THIS UNIT IS ONLY AVAILABLE IN ACADEMIC YEARS STARTING IN AN ODD
YEAR.
MATH0086: Medical statistics
Semester 1
Credits: 6
Topic: Statistics
Level: Level 3
Assessment: EX100
Requisites: Pre MATH0035, Pre MATH0003, Pre MATH0005
Aims & Learning Objectives: Aims To introduce students
to the statistical needs of medical research and describe commonly
used methods in the design and analysis of clinical trials.
Objectives On completing the course, students should be able to
(a) recognise the statistically important features of a medical
research problem and, where appropriate, suggest a suitable clinical
trial design; (b)· analyse data collected from a comparative
clinical trial, including crossover and case-control studies,
binary response data and survival data.
Content: Drug development: Phases I to IV of drug development
and testing. Ethical considerations.
Design of clinical trials: Defining the patient population, the
trial protocol, possible sources of bias, randomisation, blinding,
use of placebo treatment, stratification, balancing prognostic
variables across treatments by "minimisation". Formulation
of clinical trials as hypothesis testing and decision problems.
Sample size calculations,
use of pilot studies, adaptive methods.
Analysis of clinical trials: Patient withdrawals, "intent
to treat" criterion for inclusion of patients in analysis,
inclusion of stratification variables in the analysis.
Interim analyses: Repeated significance tests, O'Brien and Fleming's
stopping rule, sample size calculations. Statistical analysis
following a group sequential trial, contrast between frequentist
and Bayesian analyses.
Crossover trials: Two treatment, two period design. Discussion
of more complex designs.
Case-control studies.
Binary data: Comparison of treatments with binary outcomes, inclusion
of prognostic variables in logit and probit models.
Survival data: Life tables, censoring. Parametric models for censored
survival data. Kaplan-Meier estimate, Greenwood's formula, the
proportional hazards model, logrank test, Cox's proportional hazards
regression model.
THIS UNIT IS ONLY AVAILABLE IN ACADEMIC YEARS STARTING IN AN ODD
YEAR.
MATH0087: Optimisation methods of operational research
Semester 1
Credits: 6
Topic: Statistics
Level: Level 3
Assessment: EX100
Requisites: Pre MATH0002, Pre MATH0005
Aims & Learning Objectives: Aims To present methods
of optimisation commonly used in OR, to explain their theoretical
basis and give an appreciation of the variety of areas in which
they are applicable.
Objectives On completing the course, students should be able to
* recognise practical problems where optimisation methods can
be used effectively
* implement the simplex and dual simplex algorithms, Dantzig's
method for the transportation problem and the Ford-Fulkerson algorithm
* explain the underlying theory of linear programming problems,
including duality.
Content: The Nature of OR: Brief introduction.
Linear Programming: Basic solutions and the fundamental theorem.
The simplex algorithm, two phase method for an initial solution.
Interpretation of the optimal tableau. Duality. Sensitivity analysis
and the dual simplex algorithm. Brief discussion of Karmarkar's
method. Applications of LP. The transportation problem and its
applications, solution by Dantzig's method. Network flow problems,
the Ford-Fulkerson theorem.
Non-linear Programming: Revision of classical Lagrangian methods.
Kuhn-Tucker conditions, necessity and sufficiency. Illustration
by application to quadratic programming.
MATH0088: Data collection
Semester 1
Credits: 6
Topic: Statistics
Level: Level 3
Assessment: EX100
Requisites: Pre MATH0035
Aims & Learning Objectives: Aims To illustrate the
principles of experimental design in randomised and factorial
designs and a variety of sample survey methods. To present components
of variance estimation in random effects models and discuss its
application in industrial quality improvement.
Objectives On completing the course, students should be able to
* identify the features of a proposed study that affect the choice
of experimental design
* choose a suitable, efficient design for a study and explain
how the data collected under this design should ultimately be
analysed
* design and analyse a components of variance experiment
* design and analyse a sample survey.
Content: Principles of experimental design: Randomisation
and the avoidance of bias. Advantages of orthogonal parameter
estimates. Efficiency and optimal designs. Practical considerations.
Observational studies: Confounding factors, reduction of bias
by matching and regression modelling. The scope of inference from
observational data.
Randomised designs: Completely randomised and randomised block
designs.
Factorial designs: Complete factorial designs, confounding and
fractional factorials, applications to modern quality improvement.
Random effects: Split plot designs, statistical models and analyses.
Sample surveys: Simple random sampling, stratified sampling, two-stage
sampling, cluster sampling, quota sampling. Inference about the
mean of a finite population. Randomised response methods for sensitive
questions.
THIS UNIT IS ONLY AVAILABLE IN ACADEMIC YEARS STARTING IN AN EVEN
YEAR.
MATH0089: Applied probability & finance
Semester 1
Credits: 6
Topic: Statistics
Level: Level 3
Assessment: EX100
Requisites: Pre MATH0034
Aims & Learning Objectives: Aims To develop and apply
the theory of probability and stochastic processes to examples
from finance and economics.
Objectives At the end of the course, students should be able to
* formulate mathematically, and then solve, dynamic programming
problems
* describe the Capital Asset Pricing Model and its conclusions
* price an option on a stock modelled by a single step of a random
walk
* perform simple calculations involving properties of Brownian
motion.
Content: Dynamic programming: Markov decision processes,
Bellman equation; examples including consumption/investment, bid
acceptance, optimal stopping. Infinite horizon problems; discounted
programming, the Howard Improvement Lemma, negative and positive
programming, simple examples and counter-examples.
Utility theory: Risk aversion, the Capital Asset Pricing Model.
Option pricing for random walks: Arbitrage pricing theory, prices
and discounted prices as Martingales, hedging.
Brownian motion: Introduction to Brownian motion, definition and
simple properties.
Exponential Brownian motion as the model for a stock price, the
Black-Scholes formula.
THIS UNIT IS ONLY AVAILABLE IN ACADEMIC YEARS STARTING IN AN EVEN
YEAR.
MATH0090: Multivariate analysis
Semester 2
Credits: 6
Topic: Statistics
Level: Level 3
Assessment: EX100
Requisites: Pre MATH0035, Pre MATH0008
Aims & Learning Objectives: Aims To develop facility
in the analysis and interpretation of multivariate data.
Objectives At the end of the course, students should be able to
· use graphical methods to identify possible structure in
high-dimensional data
· select appropriately among a variety of techniques for
dimensionality reduction
· combine classical inferential methods with more recent
computationally-intensive techniques to produce more in-depth
analyses than were possible before the computer era.
Content: Introduction: Graphical exploratory analysis of
high-dimensional data. Revision of matrix techniques, eigenvalue
and singular value decompositions.
Principal components analysis: Derivation and interpretation,
approximate reduction of dimensionality, scaling problems.
Factor analysis.
Multidimensional distributions: The multivariate normal distribution,
its properties and estimation of parameters. One and two sample
tests on means, the Wishart distribution, Hotelling's T-squared.
The multivariate linear model.
Canonical correlations and canonical variables: Discriminant analysis,
classification problems and cluster analysis.
Topics selected from: Metrics and similarity coefficients; multi-dimensional
scaling;
clustering algorithms; correspondence analysis, the biplot, Procrustes
analysis and projection pursuit; Classification and Regression
Trees.
MATH0091: Applied statistics
Semester 2
Credits: 6
Topic: Statistics
Level: Level 3
Assessment: EX100
Requisites: Pre MATH0084
Aims & Learning Objectives: Aims To give students experience
in tackling a variety of "real-life" statistical problems.
Objectives During the course, students should become proficient
in
* formulating a problem and carrying out an exploratory data analysis
* tackling non-standard, "messy" data
* presenting the results of an analysis in a clear report.
Content: Formulating statistical problems: Objectives,
the importance of the initial examination of data, processing
large-scale data sets.
Analysis: Choosing an appropriate method of analysis, verification
of assumptions.
Presentation of results: Report writing, communication with non-statisticians.
Using resources: The computer, the library.
Project topics may include: Exploratory data analysis. Practical
aspects of sample surveys. Fitting general and generalised linear
models. The analysis of standard and non-standard data arising
from theoretical work in other blocks.
MATH0092: Statistical inference
Semester 2
Credits: 6
Topic: Statistics
Level: Level 3
Assessment: EX100
Requisites: Pre MATH0033
Aims & Learning Objectives: Aims To develop a formal
basis for methods of statistical inference and decision making,
including criteria for the comparison of procedures. To give an
in depth description of Bayesian methods and the asymptotic theory
of maximum likelihood methods.
Objectives On completing the course, students should be able to
* identify and compute admissible, minimax and Bayes decision
rules
* calculate properties of estimates and hypothesis tests
* derive efficient estimates and tests for a broad range of problems,
including applications to a variety of standard distributions.
Content: Revision of standard distributions: Bernoulli,
binomial, Poisson, exponential, gamma and normal, and their interrelationships.
Sufficiency and Exponential families.
Decision theory: Admissibility and minimax decision rules; Bayes
risk and Bayes rules. Bayesian inference; prior and posterior
distributions, conjugate priors.
Point estimation: Bias and variance considerations, mean squared
error. Cramer-Rao lower bound and efficiency. Unbiased minimum
variance estimators and a direct appreciation of efficiency through
some examples. Bias reduction. Asymptotic theory for maximum likelihood
estimators.
Hypothesis testing: Hypothesis testing, review of the Neyman-Pearson
lemma and maximisation of power. Maximum likelihood ratio tests,
asymptotic theory. Compound alternative hypotheses, uniformly
most powerful tests, locally most powerful tests and score statistics.
Compound null hypotheses, monotone likelihood ratio property,
uniformly most powerful unbiased tests. Nuisance parameters, generalised
likelihood ratio tests.
MATH0093: Stochastic processes
Semester 2
Credits: 6
Topic: Statistics
Level: Level 3
Assessment: EX100
Requisites: Pre MATH0003, Pre MATH0005, Pre MATH0032
Aims & Learning Objectives: Aims To present a formal
description of Markov chains and Markov processes, their qualitative
properties and ergodic theory. To apply results in modelling real
life phenomena, such as biological processes and queueing systems,
and in controlling such systems.
Objectives On completing the course, students should be able to
* classify the states of a Markov chain and find its ergodic distribution
* calculate generating functions, waiting time distributions and
limiting behaviour of queues
* apply these results to solve OR type problems of process control.
Content: Markov chains: Definitions and examples, n-step
transition probabilities, equilibrium and stationary distributions,
classification of states and ergodic theorems, multiplicative
chains.
Markov processes with discrete states in continuous time: Properties
of the Poisson process, birth and death processes, immigration/emigration
processes, equilibrium distributions.
Queues: Kendall's classification system and examples, M/M/1 including
time dependent solution, M/M/k and other Markov queues, the method
of stages, machine interference, the queue M/G/l, priority systems.
MATH0105: Industrial placement
Academic Year
Credits: 60
Level: Level 2
Assessment:
Requisites:
See Director of Studies for details.
SOCS0013: Intermediate macroeconomics 1
Semester 1
Credits: 3
Topic: Economics
Level: Level 2
Assessment: EX100
Requisites: Pre SOCS0006, Co SOCS0014
Aims & Learning Objectives: To build on first year
macroeconomics ,a rigorous structure of macro analysis, with a
European Union empirical perspective. Students should see this
field as an integrated area, rather than a series of isolated,
even if interesting, policy orientated topics.
Content: Topics include intertemporal budget constraints;
money and the demand for money; monetary policy, aggregate demand
and output.
SOCS0014: Intermediate macroeconomics 2
Semester 2
Credits: 3
Topic: Economics
Level: Level 2
Assessment: EX100
Requisites: Co SOCS0013
Aims & Learning Objectives: To build on first year
macroeconomics ,a rigorous structure of macro analysis, with a
European Union empirical perspective. Students should see this
field as an integrated area, rather than a series of isolated,
even if interesting, policy orientated topics.
Content: Topics include: inflation and business cycles;
fiscal policy; labour markets; exchange rates and financial markets;
the international monetary system.
SOCS0015: Intermediate microeconomics 1
Semester 1
Credits: 3
Topic: Economics
Level: Level 2
Assessment: EX100
Requisites: Pre SOCS0004, Co SOCS0016
Aims & Learning Objectives: The aim is to provide students
specialising in economics with the analytical foundations for
the study of resource allocation within the household, firm, government,
or other institutions in a modern economy. It is essential for
anyone wishing to undertake further study of the economics of
industry, labour, environment and other sectoral economic issues
Content: The course will cover the theory of consumer behaviour,
the theory of the firm in a competitive situation, industrial
organisation and imperfect competition, the theory of factor markets,
the economics of information, welfare economics and general equilibrium
theory.
SOCS0016: Intermediate microeconomics 2
Semester 2
Credits: 3
Topic: Economics
Level: Level 2
Assessment: EX100
Requisites: Co SOCS0015
Aims & Learning Objectives: The aim is to provide students
specialising in economics with the analytical foundations for
the study of resource allocation within the household, firm, government,
or other institutions in a modern economy. It is essential for
anyone wishing to undertake further study of the economics of
industry, labour, environment and other sectoral economic issues
Content: The course will cover the theory of factor markets,
the economics of information, welfare economics and general equilibrium
theory. It will follow directly on from Intermediate Microeconomics
I
SOCS0022: Mathematical economics
Semester 2
Credits: 6
Topic: Economics
Level: Level 2
Assessment: EX80 CW20
Requisites: Pre SOCS0004, Pre SOCS0006
Aims & Learning Objectives: The aim of this course
is to equip students with an understanding of, and an ability
to use, mathematical methods in economics
Content: The course covers constrained optimisation for
the household and the firm using the Lagrangian method, including
duality; linear programming; matrix algebra as applied to input-output
analysis and macro-models; the use of first and second order difference
and differential equations in economic dynamics; simple non-linear
dynamics.
Students must have A-level Mathematics or undertaken the appropriate
pre-requisite units to take this unit.
SOCS0027: Economics of development 1
Semester 1
Credits: 6
Topic: Economics
Level: Level 3
Assessment: EX50 CW20 ES30
Requisites: Pre SOCS0004, Pre SOCS0006, Pre SOCS0001, Pre SOCS0002
Aims & Learning Objectives: To relate economic theory
to debates over the determinants of global poverty, and over the
prospects for economic development and poverty reduction in low
and middle income countries.
Content: The status of development economics as a sub-discipline.
Open and closed dual economy models of industrialization. Industrialization
and trade strategies. Definition and measurement of poverty. Models
of the farm-household, and theories of agrarian change. Demographic
transition and the environment.
SOCS0028: Economics of development 2
Semester 2
Credits: 6
Topic: Economics
Level: Level 3
Assessment: EX50 ES50
Requisites: Pre SOCS0027, Pre SOCS0031
Aims & Learning Objectives: To apply general theories
of economic development to contemporary issues in selected low
and middle income countries, and to understand the relationship
between economics and other social science disciplines relevant
to the analysis of these issues.
Content: Development economics is first located within
the wider framework of development studies. Contemporary policy
issues in selected low and middle income countries are then considered,
with a current focus on the origins, components and effects of
stabilisation and structural adjustment in Sub-Saharan Africa
and South Asia.
SOCS0029: Economics of transition
Semester 2
Credits: 6
Topic: Economics
Level: Level 3
Assessment: EX100
Requisites: Pre SOCS0016, Pre SOCS0014
Aims & Learning Objectives: To use economic analysis
to understand the changes which are taking place in Central and
Eastern Europe and the former Soviet Union, relating them to the
creation of market economies.
Content: Topics covered will include the speed and sequencing
of adjustment; privatisation; financial markets; foreign trade;
growth and inflation; legal changes; the labour market; public
finance issues.
SOCS0030: International monetary economics
Semester 2
Credits: 6
Topic: Economics
Level: Level 3
Assessment: EX100
Requisites: Pre SOCS0016, Pre SOCS0014
Aims & Learning Objectives: The aim is to present a
fairly rigorous account of the material that relates to monetary
aspects of an open economy. The emphasis is on theory and analysis
rather than policy. Students should gain a critical appreciation
of the theoretical tools used in this important area of economics
alongside an understanding of the different "economic"
worlds they can be used to create.
Content: The course tries to emphasise debate by generally
contrasting a Keynesian real side approach with a more classically
inspired monetary approach. Specific topics include: the nature
and significance of the balance of payments; parity concepts;
the "efficient markets" hypothesis; devaluation; open
economy macroeconomics; flexible versus fixed exchange rates;
the foreign trade sector, "Europe" and international
policy co-ordination.
SOCS0031: Economic growth & natural resources
Semester 1
Credits: 6
Topic: Economics
Level: Level 3
Assessment: EX100
Requisites: Pre SOCS0014, Pre SOCS0016
Aims & Learning Objectives: The aim is to provide a
fairly sophisticated account of theories of economic growth and
of natural resource use, leading on to a discussion of the concept
of sustainable development. Though the course draws on some techniques
of dynamic optimisation, the emphasis is on economic intuition
and empirical relevance rather than rigorous mathematical proof.
Content: The neo-classical model of growth; endogenous
growth; optimal saving; depletion of exhaustible resources; management
of renewable resources; intergenerational equity; sustainable
development.
SOCS0032: Environmental economics
Semester 2
Credits: 6
Topic: Economics
Level: Level 3
Assessment: EX100
Requisites: Pre SOCS0016
Aims & Learning Objectives: The course provides the
economic perspective on environmental regulation and on the management
of natural resources. The emphasis is on the use of economic tools
to value environmental impacts and the use of natural resources;
and to design cost effective methods of controlling pollution
and misuse of the natural environment.
Content: The course will discuss the welfare economic basis
of environmental economics and why market systems do not provide
adequate environmental protection. It will go on to study different
methods of valuing the environment and on regulating it in a national
context. Finally it will deal with the theme of environment and
development, and the idea of sustainable development.
SOCS0033: Advanced microeconomics
Semester 2
Credits: 6
Topic: Economics
Level: Level 3
Assessment: EX100
Requisites: Pre SOCS0016, Pre SOCS0022
Aims & Learning Objectives: The aim of this course
is to build on second year microeconomics and introduce topics
that are the subject of recent academic research. This will provide
students with: (i) an understanding of the scope of modern microeconomics
and its applications, (ii) an ability to read and understand current
literature in microeconomics, (iii) an ability to use advanced
microeconomic concepts in analysing specific issues.
Content: The course covers topics that deal with three
inter-related issues: the passage of time, uncertainty about the
future, the use of information. These include: the principles
of decision making under uncertainty, with applications to insurance,
stock-markets and firm behaviour; investment behaviour of firms
under certainty and uncertainty; problems of asymmetric information;
screening and signalling; strategic behaviour.
SOCS0034: Advanced macroeconomics
Semester 1
Credits: 6
Topic: Economics
Level: Level 3
Assessment: EX100
Requisites: Pre SOCS0016, Pre SOCS0022
Aims & Learning Objectives: The aim of this course
is to build on second year macroeconomics and introduce topics
that are the subject of recent academic research, this will provide
students with: (I) an
understanding of the scope of modern macroeconomics and its applications,
(ii) an ability to read and understand current literature in macroeconomics,
(iii) an ability to use advanced macroeconomic concepts in analysing
specific issues.
Content: The course covers in depth two inter-related issues:
the causes of business cycles and of unemployment. Topics covered
include modern real business cycle theory; endogenous business
cycles, simple non-linear models, wage and price rigidity, insider
and outsider behaviour, efficiency wages and unemployment hysteresis.
SOCS0037: International trade
Semester 1
Credits: 6
Topic: Economics
Level: Level 3
Assessment: EX100
Requisites: Pre SOCS0016
Aims & Learning Objectives: The aim of the course is
to provide an understanding of the way in which economic theory
can be applied to issues such as why countries engage in international
trade and why they adopt trade restraints. The emphasis of the
course is on theory and analysis rather than description. Students
will become more skilled in understanding and applying economic
analysis and more aware of economic debates concerning current
issues in international trade.
Content: After an introduction to basic concepts, the topics
discussed will include: comparative advantage; the gains from
trade; adjustment costs; the Heckscher-Ohlin-Samuelson model;
the Specific Factors Model; theories of intra-industry trade;
the costs of protection, smuggling, trade taxes as a revenue source;
the optimum tariff; export subsidies; international cartels, quotas
and voluntary export restraint,; international integration; multinational
enterprises and the welfare effects of the international movement
of factors of production.
SOCS0038: Public expenditure & public choice
Semester 1
Credits: 6
Topic: Economics
Level: Level 3
Assessment: EX100
Requisites: Pre SOCS0016
Aims & Learning Objectives: The aim of the course is
to examine alternative ways by which the allocation of resources
within the public sector can be evaluated. Criteria for evaluation
of public expenditure are discussed and techniques, such as cost
benefit analysis, are appraised. An important learning objective
is to develop an understanding of how different perspectives can
be applied. In particular, the standard 'public finance' approach
is contrasted with the more recent 'public choice' approach. The
course is theoretical and analytical rather than descriptive.
Content: The course begins with a review of welfare economics
(- as public expenditure analysis is applied welfare economics).
Market failure and the rationale for government intervention is
assessed. The impact of alleged 'failings' in the political process
is also assessed. The behaviour of voters, political parties,
bureaucrats and pressure groups is analysed using microeconomic
theory. The growth of the public sector is considered in terms
of both market and government failure. Techniques for public sector
appraisal are discussed.
SOCS0039: Economics of taxation
Semester 2
Credits: 6
Topic: Economics
Level: Level 3
Assessment: EX100
Requisites: Pre SOCS0016, Pre SOCS0014
Aims & Learning Objectives: The aim is to provide criteria
which can be used to assess different taxes. The student will
learn how to appraise tax reform against a set of criteria which
include efficiency, equity, etc. The learning objective is to
develop skills associated with the application of economic theory.
The course is theoretical and analytical rather than descriptive.
Content: The course begins with an analysis of the welfare
costs of taxation. Tax incidence is discussed. The effect of tax
on work effort, saving and risk taking is explored (and, in particular,
the claims of 'supply-side economists' are assessed). Tax expenditures
(e.g. tax relief for charitable giving) are appraised. Tax evasion
and policy to deter tax evasion is discussed International taxation
is considered. The choice between taxation and government borrowing
is examined.
SOCS0040: Macroeconomic modelling
Semester 2
Credits: 6
Topic: Economics
Level: Level 3
Assessment: EX100
Requisites:
Aims & Learning Objectives: The aim is to provide a
thorough grounding in the practice, techniques and limitations
of macroeconomic modelling.
Content: Building a macroeconomic model, optimisation subject
to the constraints of a model, comparison of UK macroeconomic
models and industry forecasting models.
SOCS0041: Econometrics 1
Semester 1
Credits: 6
Topic: Economics
Level: Level 2
Assessment: EX100
Requisites:
Aims & Learning Objectives: The aim is to present a
rigorous account of econometrics. The language of econometrics
is matrix algebra. The emphasis is on both theory and applications
in equal measure. Knowledge of econometrics is an essential part
of the toolkit of any economist and econometric techniques are
used in a wide range of disciplines, including management, statistics
and biological sciences.
Content: The course follows Johnson's classic text to a
large extent. Specific topics include, ols, 2sls and lagged variables.
There are no formal pre-requisites but a knowledge of basic statistics,
economics and computing is essential.
SOCS0042: Econometrics 2
Semester 2
Credits: 6
Topic: Economics
Level: Level 2
Assessment: EX100
Requisites: Pre SOCS0041
Aims & Learning Objectives: The aim is to present a
rigorous account of econometrics. It continues from Econometrics
I. The emphasis is on both theory and applications in equal measure.
Knowledge of econometrics is an essential part of the tool kit
of any economist and econometric techniques are used in a wide
range of disciplines, including management, statistics and biological
sciences.
Content: The course follows Johnson's classic text to a
large extent. Specific topics include, nonlinear least squares,
analysis of forecasts, ARIMA modelling, cointegration and error
correction models.
SOCS0043: Advanced econometrics 1
Semester 1
Credits: 6
Topic: Economics
Level: Level 3
Assessment: EX100
Requisites: Pre SOCS0042
Aims & Learning Objectives: The aim is to extend the
knowledge of econometrics to a very high and rigorous level. The
language is a combination of matrix algebra and maximum likelihood.
The emphasis is on both theory and applications in equal measure.
The course concentrates on both time series analysis and cross
section analysis.
Content: The course builds on the econometrics course and
includes 3sls, fiml, probit, logit and other limited dependent
variable techniques and sure.
SOCS0044: Advanced econometrics 2
Semester 2
Credits: 6
Topic: Economics
Level: Level 3
Assessment: EX100
Requisites: Pre SOCS0043
Aims & Learning Objectives: The aim is to extend the
knowledge of econometrics to a very high and rigorous level. The
language is a combination of matrix algebra and maximum likelihood.
The emphasis is on both theory and applications in equal measure.
The course concentrates on both time series analysis .
Content: The course builds on the Advanced Econometrics
I course and includes splines, vars, Granger causality, Box and
Cox methods and spectral analysis.
SOCS0153: Placement
Academic Year
Credits: 60
Level: Level 2
Assessment:
Requisites:
Aims & Learning Objectives: The placement period enables
the student to gain valuable practical experience. Please see
the Director of Studies or course tutor for details about individual
placements.
Back to:
Economics and Statistics Programme Catalogue
Programme / Unit Catalogue 1997/98