Entry Requirements

General information

  • Any individual from any country is welcome to apply.
  • CERGE-EI does not discriminate on the basis of nationality, race, age, gender, or religion.
  • There is no application fee.

Educational background requirements

  • Strong background in advanced mathematics (see below for details).
  • Excellent command of written and spoken English to fully understand PhD-level study material.
  • Previous education in economics is recommended (however, applicants from non-economic fields with a strong mathematical background are also encouraged to apply).
  • For PhD in Economics (Czech and US study track): A Master's degree or equivalent that can be recognized in the Czech Republic (or the expectation of completing a Master's degree by September of the year for which you are applying).
  • For PhD in Economics (US study track): We accept applications from talented BA holders who wish to pursue a PhD education. However, due to the Czech Higher Education Act 1998, s 48(3): ‘Admission to a Doctoral degree programme is conditional on the completion of studies in a Master’s degree programme,’ therefore in order to admit a Bachelor student into the US PhD program, the applicant is required to simultaneously apply for the Master in Economic Research program to be able to directly enrol into the PhD study track.

Mathematical background

Students entering the PhD program at CERGE-EI should have completed at least: a two-semester course in calculus, a one-semester course in linear algebra, and a one-semester course in statistics prior to starting their studies at CERGE-EI. Additional mathematical background is certainly a plus and can help students to sustain good academic standing.

Students commencing CERGE-EI graduate studies should have mastered the following general topics:

Calculus: Convergence of numerical sequences; functions of one variable and many variables; limits and continuity of functions; differentiation and integration; partial and full derivatives; numerical and functional series; Taylor’s expansion, ordinary differential equations.

Algebra: Linear vector spaces; systems of linear equations; matrices and operators; matrix eigenvalues and eigenvalues; Euclidian and Hilbert spaces; quadratic forms; complex numbers.

Statistics: Random variables; probability distributions; moments; conditional probabilities and moments; law of large numbers, central limit theorem; confidence intervals; hypothesis testing.