Science

Master of Science in Economics and Statistics

The Master of Science in Economics and Statistics is a postgraduate programme that prepares professionals for advanced practice in economic analysis, statistical methods, and data-driven decision-making. The programme combines economic theory with statistical and econometric methods, quantitative analysis, and research application.

Core areas include microeconomic theory, macroeconomic theory, econometrics, statistical inference, time series analysis, sampling methods, economic modelling, data analysis, research methods, and thesis. Students engage with both economic theory and practical statistical and computational work through coursework and research.

The programme is offered by Kenyatta University through the School of Business, Economics and Tourism Management with the Department of Econometrics and Statistics, and the University of Nairobi through the Faculty of Business and Management Sciences and the Department of Mathematics. Both are public universities offering programmes over two academic years through full-time and part-time modes of study.

Students develop competencies in microeconomics, macroeconomics, econometrics, statistical inference, time series, sampling, and economic modelling. The programme includes coursework, examinations, computer laboratory work, data analysis projects, and a supervised research thesis.

Kenyatta University charges approximately KES 246,000/year for economics masters programmes. The University of Nairobi charges approximately KES 304,500/year (KES 609,000 total) for MA in Economic Policy Management. Entry requires a Bachelor's degree with Second Class Honours Upper Division in Economics, Statistics, Mathematics, or related fields from a recognised university.

Graduates pursue careers as economic statisticians, data analysts, econometricians, economic researchers, statistical officers, and lecturers in economics and statistics across government agencies, central banks, research institutions, international organisations, consulting firms, and universities.

Skills Required

Microeconomic Theory and Analysis
Macroeconomic Theory and Policy
Econometric Methods and Modelling
Statistical Inference and Hypothesis Testing
Time Series Analysis and Forecasting
Sampling Methods and Survey Design
Economic Data Analysis and Visualisation
Economic Modelling and Simulation
Research Methods in Economics and Statistics
Academic Writing and Thesis Research

Key Subjects

Microeconomic Theory and Analysis
Macroeconomic Theory and Policy
Econometric Methods and Modelling
Statistical Inference and Hypothesis Testing
Time Series Analysis and Forecasting
Sampling Methods and Survey Design
Economic Data Analysis and Visualisation
Economic Modelling and Simulation
Research Methods in Economics and Statistics
Thesis Research

Certifications

KNBS Statistical Certification
IMF Statistics Certification

Specializations

Econometrics

Focuses on econometrics, covering regression, time series, panel data, causal inference, and managing econometric analysis.

Economic Statistics

Examines economic statistics, covering national accounts, price indices, economic indicators, and managing economic statistics.

Time Series and Forecasting

Covers time series analysis, covering ARIMA, VAR, forecasting, volatility, and managing time series analysis.

Survey Methods and Sampling

Focuses on survey methods and sampling, covering design, sampling, estimation, data collection, and managing survey operations.

Data Analytics

Examines data analytics, covering big data, machine learning, visualisation, programming, and managing data analytics.

Economic Modelling

Covers economic modelling, covering CGE, DSGE, microsimulation, optimisation, and managing economic modelling.

Duration: 2 years
Public, up to: Ksh 388,000
Job market: High

The programme

What you study, how long it takes, and how it is delivered.

Practicalities

Study mode: Full-time, Part-time
Attachment: 0 months
Average class: 25 students
Award: Masters

What you study

10 subjects

Microeconomic Theory and Analysis
Macroeconomic Theory and Policy
Econometric Methods and Modelling
Statistical Inference and Hypothesis Testing
Time Series Analysis and Forecasting
Sampling Methods and Survey Design
Economic Data Analysis and Visualisation
Economic Modelling and Simulation
Research Methods in Economics and Statistics
Thesis Research

Modules

12 in the programme

Microeconomic Theory and Analysis
Year 1Semester 13 creditsCore
Examines consumer theory, producer theory, market structures, welfare, externalities, and managing microeconomic analysis.
Macroeconomic Theory and Policy
Year 1Semester 13 creditsCore
Covers national income, inflation, unemployment, growth, fiscal and monetary policy, and managing macroeconomic analysis.
Statistical Inference and Hypothesis Testing
Year 1Semester 13 creditsCore
Examines estimation, hypothesis testing, confidence intervals, likelihood, and managing statistical inference.
Econometric Methods and Modelling
Year 1Semester 13 creditsCore
Covers regression, time series, panel data, forecasting, causal inference, and managing econometric analysis.
Time Series Analysis and Forecasting
Year 1Semester 23 creditsCore
Examines ARIMA, VAR, volatility, forecasting, spectral analysis, and managing time series analysis.
Sampling Methods and Survey Design
Year 1Semester 23 creditsCore
Covers design, sampling techniques, estimation, data collection, and managing survey operations.
Economic Data Analysis and Visualisation
Year 1Semester 23 creditsCore
Examines data management, visualisation, programming, big data, and managing data analytics for economics.
Economic Modelling and Simulation
Year 1Semester 23 creditsCore
Covers CGE, DSGE, microsimulation, optimisation, simulation, and managing economic modelling.
Research Methods in Economics and Statistics
Year 2Semester 13 creditsCore
Covers research design, data collection, analysis, economic and statistical research methods, and conducting research, preparing students for their thesis.
Bayesian Statistics and Econometrics
Year 2Semester 13 creditsCore
Examines Bayesian methods, MCMC, prior, posterior, hierarchical models, and managing Bayesian analysis.
Applied Econometrics and Policy Evaluation
Year 2Semester 13 creditsCore
Covers impact evaluation, causal inference, natural experiments, RCTs, DID, and managing policy evaluation.
Research Thesis
Year 2Semester 29 creditsCore
Original supervised research thesis on an economics and statistics topic, demonstrating mastery of research methods and economics and statistics knowledge, assessed through written submission and oral defence.

Specialisations

Econometrics
Focuses on econometrics, covering regression, time series, panel data, causal inference, and managing econometric analysis.
Economic Statistics
Examines economic statistics, covering national accounts, price indices, economic indicators, and managing economic statistics.
Time Series and Forecasting
Covers time series analysis, covering ARIMA, VAR, forecasting, volatility, and managing time series analysis.
Survey Methods and Sampling
Focuses on survey methods and sampling, covering design, sampling, estimation, data collection, and managing survey operations.
Data Analytics
Examines data analytics, covering big data, machine learning, visualisation, programming, and managing data analytics.
Economic Modelling
Covers economic modelling, covering CGE, DSGE, microsimulation, optimisation, and managing economic modelling.

A day as a student

A typical day during the MSc in Economics and Statistics programme combines lectures, seminars, tutorials, computer laboratory sessions, practical exercises, and independent study. Sessions cover microeconomics, macroeconomics, econometrics, and statistics. Microeconomics sessions examine consumer theory, producer theory, market structures, welfare, and managing microeconomic analysis. Macroeconomics sessions cover national income, inflation, unemployment, growth, and managing macroeconomic analysis. Econometrics sessions cover regression, time series, panel data, forecasting, and managing econometric analysis. Statistical inference sessions cover estimation, hypothesis testing, confidence intervals, and managing statistical inference. Time series sessions cover ARIMA, VAR, volatility, forecasting, and managing time series analysis. Sampling methods sessions cover design, sampling techniques, estimation, and managing survey operations. Economic modelling sessions cover CGE, DSGE, microsimulation, optimisation, and managing economic modelling. Data analysis sessions cover data management, visualisation, programming, big data, and managing data analytics. Research methods sessions prepare students for their thesis, covering research design, data collection, and analysis. Computer laboratory sessions provide hands-on experience with Stata, R, EViews, Python, and econometric and statistical software. Practical exercises provide hands-on experience with data analysis, economic modelling, and statistical inference. Seminars and discussion groups provide opportunities for debating current issues in economics and statistics and presenting research findings. Guest lectures from experienced economists, statisticians, and researchers provide practical insights. The programme culminates in a supervised research thesis on an economics and statistics topic.

The trade offs

In its favour

High demand for economics and statistics professionals with Kenya's data revolution, evidence-based policy, and growth in data analytics.
Programme combines economics and statistics, providing versatile skills for both economic analysis and data science careers.
KU offers competitive fees at approximately KES 246,000/year for economics masters programmes.
Graduates are eligible for KNBS and IMF certifications, enhancing career prospects.

Against it

UoN charges higher fees at approximately KES 304,500/year (KES 609,000 total).
No private university confirmed offering this specific combined programme.
Programme requires economics, statistics, or mathematics background, limiting access for non-related graduates.
Quantitative and mathematical components may be challenging for students without strong quantitative backgrounds.

What it costs

Tuition at both ends of the market, and how to pay for it.

What it costs, and where

Against 243 science courses

Public148k to 388k

148k at Kenyatta University388k at University of Nairobi

Annual tuition in Kenyan shillings, rounded. The upright tick is the median for this field, so a bar sitting entirely to its right is an expensive programme by the standards of its own subject.

The fine print

UoN Maths MSc ~388K/yr (uonbi.ac.ke). KU ~148K/yr (international.ku.ac.ke). Private: unverified.

HELB postgraduate loans are available for Kenyan students. KU and UoN may offer postgraduate bursaries and scholarships. KNBS and some research organisations may sponsor staff for postgraduate study. International organisations like IMF and World Bank may offer fellowships for statistics and economics professionals.

Funding options

HELB Postgraduate Loan
KU Postgraduate Scholarship
UoN Postgraduate Bursary

Scholarships

3 recorded

HELB Postgraduate Loan
LoanKsh 200,000Kenyan
Kenyan students pursuing postgraduate studies at recognised universities.
KU Postgraduate Scholarship
ScholarshipKsh 100,000Kenyan
KU offers postgraduate scholarships for eligible students.
UoN Postgraduate Bursary
ScholarshipKsh 100,000Kenyan
UoN offers postgraduate bursaries for eligible students.

Getting in

The grades, the alternatives, and who accredits the award.

What you need

KCSE mean grade: N/A (Postgraduate)
Alternative entry: KU requires Upper Second in Economics or related fields, or Lower Second with Grade C in relevant units. UoN requires Upper Second in Economics, Statistics, Mathematics, or related fields. Moi University requires similar qualifications. Contact respective universities for specific admission requirements.

How you are assessed

4 components

Coursework and Continuous Assessment
Coursework30% of the mark
Continuous assessment through coursework assignments, problem sets, data analysis exercises, seminar presentations, and class participation.
Written Examinations
Examination70% of the mark
Written examinations covering microeconomics, macroeconomics, econometrics, and statistical methods.
Practical and Computer Laboratory Assessment
Practical30% of the mark
Practical assessment through computer laboratory exercises, data analysis projects, econometric modelling, and demonstrating analytical skills.
Research Thesis
Research100% of the mark
Original supervised research thesis on an economics and statistics topic, demonstrating mastery of research methods and economics and statistics knowledge, assessed through written submission and oral defence.

Accreditation

The programme is accredited by the Commission for University Education (CUE). Kenyatta University and the University of Nairobi (both public) offer masters programmes in economics and statistics or related fields. All programmes meet CUE standards for postgraduate training in economics and statistics. Graduates are eligible for KNBS statistical certification and IMF statistics certification.

Accredited by

Commission for University Education (CUE)
Academic accreditationRequired
Programme accredited by CUE. Kenyatta University and the University of Nairobi (both public) offer masters programmes in economics and statistics or related fields. All programmes meet CUE standards for postgraduate training in economics and statistics. Graduates are eligible for KNBS statistical certification and IMF statistics certification.

Where it leads

The roles it opens, and what you leave with.

Where graduates go

6 roles

Economic Statistician
High demandKsh 120,000 to Ksh 550,000
Compiles and analyses economic statistics, overseeing national accounts, indicators, and managing economic statistics.
Data Analyst
Very high demandKsh 130,000 to Ksh 600,000
Analyses economic data, overseeing data management, visualisation, and managing data analytics.
Econometrician
High demandKsh 140,000 to Ksh 650,000
Develops econometric models, overseeing regression, forecasting, and managing econometric analysis.
Economic Researcher
Moderate demandKsh 120,000 to Ksh 550,000
Conducts economic research, overseeing analysis, publications, and managing research projects.
Statistical Officer
Moderate demandKsh 110,000 to Ksh 500,000
Manages statistical operations, overseeing data collection, analysis, and managing statistical systems.
Economics and Statistics Lecturer
Moderate demandKsh 120,000 to Ksh 500,000
Teaches economics and statistics at university or college level, overseeing instruction, research, and academic supervision.

Graduate outcomes

Where these fields lead

8 careers

Tools you will learn

Stata
SoftwarePrimary
Stata for econometric and statistical analysis, covering regression, time series, panel data, and managing econometric analysis.
R Studio
Software
R Studio for data analysis, covering statistics, econometrics, visualisation, and managing statistical computing.
EViews
Software
EViews for econometric analysis, covering time series, forecasting, modelling, and managing econometric analysis.
Python
Software
Python for data analysis, covering data science, machine learning, visualisation, and managing data analytics.

Industry links

Common misconceptions

Economics and statistics is just about numbers.
Economics and statistics covers comprehensive economic theory, econometric methods, statistical inference, and policy analysis beyond just numbers.
This programme is only for aspiring economists.
Economics and statistics skills are valuable for data analysts, researchers, consultants, and statisticians beyond just aspiring economists.
Econometrics is just about running regressions.
Econometrics covers comprehensive regression, time series, panel data, forecasting, causal inference, and model specification.
Statistics is just about calculating averages.
Statistics covers comprehensive inference, estimation, hypothesis testing, sampling, and modelling.
Economic modelling is just about spreadsheets.
Economic modelling covers comprehensive CGE, DSGE, microsimulation, optimisation, and simulation.
Data analysis is just about making charts.
Data analysis covers comprehensive data management, visualisation, programming, machine learning, and big data.