Economics and Statistics Unit Catalogue

ECOI0024: Economics of development 1
Semester 1
Credits: 6
Contact:
Topic: Economics
Level: Level 3
Assessment: EX50 ES30 CW20
Requisites: Pre ECOI0001, Pre ECOI0002, Pre ECOI0006, Pre ECOI0007
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. As well as the stated pre-requisites students must also have taken at least 2 second year economics units.

ECOI0025: Economics of development 2
Semester 2
Credits: 6
Contact:
Topic: Economics
Level: Level 3
Assessment: EX50 ES50
Requisites: Pre ECOI0024, Pre ECOI0028
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.

ECOI0026: Economics of transition
Semester 2
Credits: 6
Contact:
Topic: Economics
Level: Level 3
Assessment: EX100
Requisites: Pre ECOI0010, Pre ECOI0011
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.

ECOI0027: International monetary economics
Semester 2
Credits: 6
Contact:
Topic: Economics
Level: Level 3
Assessment: EX100
Requisites: Pre ECOI0010, Pre ECOI0011
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 constrasting 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.

ECOI0028: Economic growth & natural resources
Semester 1
Credits: 6
Contact:
Topic: Economics
Level: Level 3
Assessment: EX100
Requisites: Pre ECOI0010, Pre ECOI0011
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.

ECOI0029: Environmental economics
Semester 2
Credits: 6
Contact:
Topic: Economics
Level: Level 3
Assessment: EX100
Requisites: Pre ECOI0010
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.

ECOI0030: Advanced microeconomics
Semester 2
Credits: 6
Contact:
Topic: Economics
Level: Level 3
Assessment: EX100
Requisites: Pre ECOI0010, Pre ECOI0018
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.

ECOI0031: Advanced macroeconomics
Semester 1
Credits: 6
Contact:
Topic: Economics
Level: Level 3
Assessment: EX100
Requisites: Pre ECOI0011
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) anunderstanding 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.

ECOI0034: International trade
Semester 1
Credits: 6
Contact:
Topic: Economics
Level: Level 3
Assessment: EX100
Requisites: Pre ECOI0010
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.

ECOI0035: Public expenditure & public choice
Semester 1
Credits: 6
Contact:
Topic: Economics
Level: Level 3
Assessment: EX100
Requisites: Pre ECOI0010
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.

ECOI0036: Economics of taxation
Semester 2
Credits: 6
Contact:
Topic: Economics
Level: Level 3
Assessment: EX100
Requisites: Pre ECOI0010, Pre ECOI0011
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.

ECOI0037: Macroeconomic modelling
Semester 2
Credits: 6
Contact:
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.

ECOI0038: Advanced econometrics 1
Semester 1
Credits: 6
Contact:
Topic: Economics
Level: Level 3
Assessment: EX100
Requisites: Pre ECOI0021, Pre ECOI0020
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.

ECOI0039: Advanced econometrics 2
Semester 2
Credits: 6
Contact:
Topic: Economics
Level: Level 3
Assessment: EX100
Requisites: Pre ECOI0038
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.

MATH0084: Linear models
Semester 1
Credits: 6
Contact:
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
Contact:
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
Contact:
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, ncluding 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
Contact:
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.

MATH0091: Applied statistics
Semester 2
Credits: 6
Contact:
Topic: Statistics
Level: Level 3
Assessment: CW100
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
Contact:
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.

MATH0118: Management statistics
Semester 2
Credits: 5
Contact:
Topic:
Level: Level 3
Assessment: EX60 CW40
Requisites:
Pre MATH0097 or Pre MATH0035 Aims & learning objectives:
This unit is designed primarily for DBA Final Year students who have taken the First and Second Year management statistics units but is also available for Final Year Statistics students from the Department of Mathematical Sciences. Well qualified students from the IMML course would also be considered. It introduces three statistical topics which are particularly relevant to Management Science, namely quality control, forecasting and decision theory. Aims: To introduce some statistical topics which are particularly relevant to Management Science. Objectives: On completing the unit, students should be able to implement some quality control procedures, and some univariate forecasting procedures. They should also understand the ideas of decision theory.
Content:
Quality Control: Acceptance sampling, single and double schemes, SPRT applied to sequential scheme. Process control, Shewhart charts for mean and range, operating characteristics, ideas of cusum charts. Practical forecasting. Time plot. Trend-and-seasonal models. Exponential smoothing. Holt's linear trend model and Holt-Winters seasonal forecasting. Autoregressive models. Box-Jenkins ARIMA forecasting. Introduction to decision analysis for discrete events: Revision of Bayes' Theorem, admissability, Bayes' decisions, minimax. Decision trees, expected value of perfect information. Utility, subjective probability and its measurement.