Overview

Quantitative economic analysis provides essential tools for decoding market structures, financial systems, and global resource allocation. Studying the BSc (Hons) Economics at Lancaster University equips students with a rigorous, data-driven approach to solving complex economic problems. The curriculum emphasizes mathematical and statistical methodologies alongside traditional theoretical frameworks, delivering a strongly empirical focus. Learners explore core microeconomic models and macroeconomic principles while mastering computational tools to evaluate real-world policy and financial data. Supported by research-led teaching staff, specialized computing laboratories, and dedicated quantitative support networks, the course provides an academically challenging environment. This degree suits analytical problem solvers, logical thinkers, and data-minded students who wish to build advanced skills in quantitative reasoning, econometric modeling, data visualization, and economic forecasting.

What You'll Learn

Mathematical methods and calculus techniques applied to microeconomic optimization and macroeconomic modeling

Advanced econometric methods, statistical testing, and quantitative forecasting for complex real-world datasets

Principles of game theory, information economics, market design, and behavioral decision-making

Application of specialized statistical computing software to analyze financial trends and economic variables

Global macroeconomic policy frameworks, central banking, international financial markets, and economic growth models

Empirical research methods for designing quantitative studies, testing hypotheses, and evaluating economic interventions

Learning Outcomes

Graduates of this program will possess a deep quantitative understanding of theoretical economic models and empirical analysis methods. Students acquire the ability to critically evaluate economic policies, market phenomena, and financial risk using sophisticated mathematical frameworks and statistical testing. Through hands-on computational projects, learners gain practical skills in managing large datasets, coding statistical routines, and building predictive models. Seminar presentations and technical reporting exercises develop the student's capacity to translate intricate quantitative findings into clear, accessible insights for diverse audiences. Ultimately, graduates are equipped to apply rigorous mathematical and statistical tools to economic challenges, enabling them to specialize in areas such as financial analysis, quantitative research, economic forecasting, data science, or postgraduate academic study.