MSC IN ECONOMETRICS AND COMPUTATIONAL ECONOMICS (MECE)

Module Code	Module Name	Description
MECE501	Computational Microeconomics	The module provides an introduction to the theory of incentives and contracts which has become an indispensable part of economics. It is an interaction between computer science and economics. As computer systems become more interconnected, multiple parties interact in the same environment and compete for scarce resources, which necessarily introduces economic phenomena. The module introduces students to major tools of microeconomic theory accompanied with applications. The module aims at developing strategic thinking needed for understanding many economic environments
MECE 506	Econometric Systems	Econometric systems are designed to equip post-graduate students with a solid and up-to-date grounding in various systems of econometrics. The module helps students to appreciate theory and application of various systems of econometrics. Econometric systems prepare students to be able to identify appropriate econometric methodology for a particular economic research problem. It basically acts as the foundation of all the econometric courses in the Master of Science Degree in Econometrics and
MECE507	Computational Macroeconomics	This module equips students with powerful computational tools to be used in macroeconomic analysis. Students will learn how to solve macroeconomic models using computational methods, calibrate these models, and use calibrated models to address interesting questions in macroeconomics. While students are exposed to some basic macro models throughout the module, the main objective is computer implementation
MECE508	Panel Data Econometrics	Panel data econometrics is offered at post-graduate level as a micro-econometrics module. This module equips post-graduate students with a solid and up-to-date foundation in theoretical and applied panel data techniques. Unlike time series models which only studies time-specific variation, panel data models are richer since they analyse both period-specific and individual specific heterogeneity. Panel data econometrics has evolved rapidly over the last decade. The module prepares students with dynamic panel data estimation, non-linear panel data methods and non-stationary
MECE509	Financial Econometrics	Financial econometrics applies statistical and econometric models to analyze financial market data, focusing on asset prices, volatility, risk, and market dynamics using techniques like time series analysis, regression, and GARCH models to understand complex relationships and forecast outcomes for portfolio management, risk assessment, and valuation. It bridges theoretical finance with empirical data analysis, using methods from basic statistics to advanced time-series modelling.
MECE502	Non-parametric Econometrics	Most econometrics models are parametric/smoothed models or models with a defined functional form. While most relationships among economic, financial and business variables take the form of a defined functional form, there are equally several relationships without a well-defined functional form. Over the years, the non-parametric smoothing approach has been neglected and the popular parametric approach has been widely preferred because of its simplicity and mathematical convenience. This module therefore equips post-graduate students in economics, finance and business with a strong and up-to-date foundation in theoretical and applied non-parametric econometrics.
MECE503	Machine Learning	This module provides a broad introduction to machine learning and statistical pattern recognition. Machine learning uses interdisciplinary techniques such as statistics, linear algebra, optimization, and computer science to create automated systems that can be used on large volumes of data at high speed in order to make predictions without human intervention. Machine learning as a course is universal, with applications bridging from business intelligence to homeland security. This module will familiarize students with a broad cross-section of models and algorithms for machine learning as well as preparing students for research and industry application of machine learning techniques.
MECE504	Artificial Intelligence	This module introduces students to artificial intelligence as an introductory module. It looks at the design of intelligent agents, where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. Topics to be covered include artificial intelligence methodology and fundamentals, intelligent agents, search algorithms, game playing, supervised and unsupervised learning, decision tree learning, neural networks, nearest neighbour methods, dimensionality reduction, clustering, kernel machines, support vector machines, uncertainty and probability theory, Bayesian networks and statistical learning.
MECE505	Big Data Analysis	Big data analysis can be offered as a Programme with several modules. However, as a module, big data analysis equips students with up-to-date foundation in big data analysis. This module prepares post-graduate students with critical skills required in the analysis of big data. Students are prepared to appreciate the handling of large data sets and exposed to data sets from the national statistical agency such as Census data, labour force surveys, Demographic Health Surveys, PICES, Integrated household surveys, World Bank surveys, IMF surveys, among others.
MECE510	Design and analysis of Algorithms	Design and Analysis of Algorithms (DAA) is a core area of computer science that involves developing efficient methods to solve computational problems and evaluating their performance. The primary goal is to find optimal solutions, considering constraints like time, space, and cost.
MECE570	Dissertation	Students will do a dissertation related to theory and knowledge gained from the programme. The dissertation will offer students an opportunity to apply computational knowledge on complex economic problems.