- Joined
- 7/4/23
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Would this university module at a top 3 UK uni cover the calculus prerequisites for an MFE?
Topics covered: Matrix methods in portfolio analysis. Linear independence. Rank of a matrix. Eigenvalues and eigenvectors. Diagonalisation. Linear systems of recurrence equations. Markov process. Second-order recurrence equations. Macroeconomic models. Vector geometry. Gradient and directional derivative. Tangent hyperplanes and the optimal bundle. Resource allocation and Pareto efficiency. Orthogonal matrices and quadratic forms. Critical points of quadratic functions. Taylor's approximation. Optimisation of functions of two or more variables.
If not, what courses would you recommend to add to this?
Am also planning to take further econometrics containing:
Randomised control experiments, exact and propensity score matching methods, difference-in-differences estimation, instrumental variables and the identification of the local average treatment effect, technique for estimating the marginal treatment effect, the weak instrument problems, and sharp and fuzzy regression discontinuity design, the analysis of cross section and panel data, including fixed and random effects, computing standard error and clustering, issues of measurement error, binary choice models, maximum likelihood estimation, introduction to discrete choice model and demand estimation.
Thanks!
Topics covered: Matrix methods in portfolio analysis. Linear independence. Rank of a matrix. Eigenvalues and eigenvectors. Diagonalisation. Linear systems of recurrence equations. Markov process. Second-order recurrence equations. Macroeconomic models. Vector geometry. Gradient and directional derivative. Tangent hyperplanes and the optimal bundle. Resource allocation and Pareto efficiency. Orthogonal matrices and quadratic forms. Critical points of quadratic functions. Taylor's approximation. Optimisation of functions of two or more variables.
If not, what courses would you recommend to add to this?
Am also planning to take further econometrics containing:
Randomised control experiments, exact and propensity score matching methods, difference-in-differences estimation, instrumental variables and the identification of the local average treatment effect, technique for estimating the marginal treatment effect, the weak instrument problems, and sharp and fuzzy regression discontinuity design, the analysis of cross section and panel data, including fixed and random effects, computing standard error and clustering, issues of measurement error, binary choice models, maximum likelihood estimation, introduction to discrete choice model and demand estimation.
Thanks!
