About

Dr Mohamed Zahran is a Lecturer in Economics, Fellow of the Higher Education Academy (FHEA, UK), and Research Associate of the Economic Research Forum (ERF). He was awarded his PhD in Economics by City, University of London in 2024 and subsequently completed a Postgraduate Diploma in Academic Practice at City St George’s, University of London in 2026. 

His academic specialism is in monetary economics, open-economy macroeconomics, and international finance. His research is centred on uncertainty transmission, the credibility of monetary policy, sovereign risk, exchange-rate dynamics, and external financing conditions, particularly in emerging open economies. In parallel, he works on applied time-series econometrics, forecasting, and empirically grounded macroeconomic policy analysis, while advancing an emerging research agenda in fiscal policy, fiscal multipliers, and public debt. 

Dr Zahran is a reviewer for the Bulletin of Economic Research and the International Journal of Emerging Markets. He has held visiting research positions at the University of Texas at Dallas and the University of Victoria, and has received advanced professional training in DSGE modelling and forecasting at the International Monetary Fund, as well as in computational methods at the University of Oxford. He has taught quantitative methods, econometrics, macroeconomics, economic policy, and political economy across undergraduate and postgraduate curricula, with experience in the Departments of Economics at the University of Nottingham, Queen Mary University of London, and City St George’s, University of London. He has also supervised a substantial number of undergraduate and Master’s dissertations in applied macroeconomics and econometrics.

Primary Research Interests: 

• Monetary Economics | Open-Economy Macroeconomics | International Finance

Secondary Research Interests:

• Applied Time-Series Econometrics | Public Finance 

Quantitative Methods: Local Projections, DSGE modelling, Dynamic Factor Models for big data forecasting, Time-varying Stochastic Volatility Models, and time–frequency methods.

Software: MATLAB (Dynare), R, Python, Stata. LATEX.