MacroSpring2018

Course Content

1. Introduction

1.1 Lecture:

slides with course motivation, outline, requirements, literature,
short history of business cycles - the underlying paper, policy analysis with macro models

1.2 Seminar:

matlab basics: slides, code solving the class exercise, NICHOLAS SIM code we did not mange to look at in class, Winistörfer P. and Canova F., 2006,''Introduction to Matlab'', Matlab cheat sheet, nice youtube lecture, source of some other materials
Function approximation: slides, approx.m, classpolinomials.m
Log-linearization: Katrin's lecture notes on log-linearization, Uhlig log-linearization, yet another lecture notes, very nice material on the story behind Lagrange Multipliers
Problem set 1, due to 2.3.2018

2. Stylized facts

2.1 Lecture:

Introduction to macro data: slides, matlab code to reproduce the class material: hpfilter.m, bandpass.m, stylizedfacts.m
Recommended reading Stock and Watson (1998): "Business Cycle Fluctuations in U.S. Macroeconomic Time Series",
Obligatory reading: King and Rebelo (2000): "Resuscitating Real Business Cycles", (chapter 2)
Nice lecture notes by Eric Sims

2.2 Seminar:

3. Real Business Cycle Model

3.1 Lecture:

First set of lecture notes
code for the first model we did in the class: optimal growth model,
code for the growth model with labor: optimal growth model with labor leisure choice, some algebra on the labor leisure choice growth model
stochastic growth model,
RBC model with balanced growth path,
important reading King and Rebelo (2000): "Resuscitating Real Business Cycles",
King Plosser Rebelo (2001): ".Production, Growth and Business Cycles: Technical Appendix"
Methods for solving rational expectations models: slides, Undetermined coefficients (Uhlig 1997), Perturbation Methods by SGU, QZ decomposition (Klein 1999, JEDC)

3.2 Seminar:

Introduction to dynare, stochastic growth model log-linearized by dynare, log-lin model in dynare, model in levels
code for RBC model with balanced growth path
Calibration - updated slides, paper by Cooley 1997, Calibrated Models
model evaluation + observation equation based on Pfeifer lecture notes
code to solve basic growth model by diagonalization
Paul Klein solab function implementing QZ decomposition, application to our stochastic growth model

Problem sets results can be find here.

Please fill the questionnaire after you solve the fourth problem set.

4. Performance of RBC model

4.1 Model Evaluation

Model evaluation in my slides, underlying paper for the discussion is here, lecture by Chris Sims see here
see critique by Galí (1999) of RBC models, some evidence and discussion on monetary policy neutrality Gali textbook ch.1
for summary on what we know about shocks driving the cycle see Ramey 2016
- - clean dynare output function
  - this code shows how to use Kalman filter to extract shocks from the observable series (watch Kalman filter explained in an extremely intuitive way, see Pfeifer lecture notes on how to link observables with your model variables), and it also demonstrates one way how to run loops in dynare, you will also need this .mod file, this is the code for King Rebelo 99 when frish elasticity is different from one
Problem set 5, due 11.5.2018

Google Sites

Report abuse