PhD Course, Fall 2003
Martin Flodén
Syllabus
Lecture notes 1
Lecture notes 2
Lecture notes 3
Lecture notes 4
Lecture notes 5
Lecture notes 6
Lecture notes 7
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Assignment 1 |
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| 1.a. Miranda & Fackler 2.2 | exercise_1_a.m |
| 2.a. Equation solver | eqn_solve.m |
| 2.e. CD utility, Gaussian quadrature | solve_cd_gauss.m foc_cd_gauss.m |
| 2.f. Separable utility | solve_sepU.m foc_sepU.m |
| Assignment 2 | |
| 6.a-c. Transistion after new g. | transition.m eval_foc.m |
| Assignment 3 | |
| 2-3. RBC model (uses grad.m, hessian.m, and hpfilter.m from Example 3). | rbc_ql.m func_r.m simulate.m |
| 5. Coconut model with Markov process for income. | coconut_markov.m |
| Assignment 4.1 | |
| Interpolation | assignment4_1.m interpolate.m |
| Assignment 4.2 | |
| Deaton (1991) with iid income and collocation method | deaton.m euler.m |
| Assignment 5 | |
| a. Analytical solution of Brock Mirman model | solution5a.pdf |
| b. Solution with PEA method | BrockMirman.m EulerRHS.m |
Example 1: Two-period model with Cobb-Douglas utility, Monte Carlo
integration (see lecture notes1)
solve_cd.m
foc_cd.m
Example 2: Transition to new steady state after permanent increase in labor
productivity (see lecture notes 2)
transition.m
eval_foc.m
| Example 3: RBC model, linear-quadratic solution (see lecture notes 3) | |
| main program | |
| evaluates utility as a function of states and decisions | |
| numerical gradient | |
| numerical hessian | |
| hpfilter: use this when simulating the model | |
Example 4: Solve simple coconut model (see lecture notes 4)
coconut.m
Example 5: Algorithm to approximate AR(1) process with Markov chain,
following Tauchen (1986)
tauchen.m
Example 6: Part of code for Assignment 4.2 - 4.3
deaton_iid_shell.m
deaton_ar1_shell.m