University of Illinois
ECON 590B1, Fall 2015
Applied Macroeconometrics Instructor:
Minchul Shin Email:
[email protected] URL: www.minchulshin.com Office Hours: Tuesday 9:30a-11:30a
Scheduled Class Time and Organization: We will meet twice a week Tuesdays and Thursdays from 8:00a - 9:20a in 1DKH 108. Course Description: Broadly speaking, we will study econometric models and methods that are useful to conduct substantive empirical research in macroeconomics. We consider the estimation and evaluation of dynamic stochastic general equilibrium models, analysis of linear and nonlinear vector autoregressive models, time series models with regime switches and time-varying coefficients, as well as dynamic factor models. For the most part, we will focus on Bayesian methods of inference, with detailed discussions of suitable simulation methods such as Markov-Chain-Monte-Carlo and Sequential Monte-Carlo methods. Prerequisites: {ECON 532, ECON 534, ECON 535} or equivalent graduate level econometrics and macroeconomics courses. Course Web Page: We will use the black-board software: https://compass2g.illinois.edu/ Course Requirements: • Class Participation and Problem Sets [48%]: There will be several problem sets, assigned during the semester. Moreover, you are expected to carefully study the assigned readings and participate in classroom discussions. One of main goals in this course is to strengthen your computer coding skill. Hence, you are required to write your own code for problem sets and final exams. You can pick your favorite computer language such as MATLAB, R, C++, FORTRAN, Python, Julia, etc. • Midterm Exam [22%]: There will be one or two take home exams • Final Exam or Research Paper [30%]: TBA. • Finding typos in slides, notes, and problem sets: 0.3% per typo, 1% for conceptual fallacy. • Macroeconomics/Econometrics Workshop: You are expected to attend the econometrics and macroeconomics workshop. Workshop schedule can be found in Macroeconomics workshop: http://www.economics.illinois.edu/seminars/macroeconomics/ Econometrics workshop: http://www.economics.illinois.edu/seminars/econometrics/ Course Texts: The main text for this course is the book manuscript Bayesian Estimation of DSGE Models by Ed Herbst and Frank Schorfheide. The manuscript will be available through course web page. 1 Another important reference is a handbook chapter by Marco Del Negro and Frank Schorfheide on Bayesian Macroeconometrics for a Handbook of Bayesian Econometrics.2 These two references give a broad overview of the 1 You 2 You
can download it from http://sites.sas.upenn.edu/schorf/files/herbst_and_schorfheide_v5.pdf. can download it from https://sites.sas.upenn.edu/sites/default/files/schorf/files/bayesian_macro.pdf.
Minchul Shin: ECON 590B1, Fall 2015
2
field and we will fill in many of the details by consulting research papers. Geweke (2005) provides a solid introduction to Bayesian inference. Much of the material related to the estimation and evaluation of DSGE models is also summarized in the survey article by Sungbae An and Frank Schorfheide. 3 Kim and Nelson (1999) is a useful monograph on regime switching models. In addition to Kim and Nelson (1999), Durbin and Koopman (2012) is a good reference on the state space modeling. Hamilton (1994) and Martin, Hurn, and Harris (2012) 4 are thorough general reference for time series analysis. Two books that provide an introduction to modern macroeconometrics are Canova (2007) and DeJong and Dave (2011). An, Sungbae and Frank Schorfheide (2007): “Bayesian Analysis of DSGE Models,” Econometric Reviews, 39, 113-172. Canova, Fabio (2007): Methods for Applied Macroeconomic Research, Princeton University Press, ISBN13: 978-0691115047. Del Negro, Marco and Frank Schorfheide (2011): “Bayesian Macroeconometrics,” in Handbook of Bayesian Econometrics. DeJong, David and Chetan Dave (2011): Structural Macroeconometrics, Princeton University Press, ISBN-13: 978-0691152875. J. Durbin, and S. J. Koopman (2012): Time Series Analysis by State Space Methods, Oxford University Press, ISBN-13: 978-0199641178. Geweke, John (2005): Contemporary Bayesian Econometrics and Statistics, Wiley, New York, ISBN 0-471-67932-1. Hamilton, James D. (1994): Time Series Analysis, Princeton University Press, ISBN 0-691-04289-6. Kim, Chang-Jin and Charles R. Nelson (1999): State-Space Models with Regime Switching, MIT Press, ISBN 0-262-11238-8. Martin, Vance, Stan Hurn, and David Harris (2012): Econometric Modeling with Time Series, Cambridge University Press, ISBN-13: 978-0521196604. Further Details: are available via blackboard.
3 You
can download it from http://sites.sas.upenn.edu/schorf/files/er-final.pdf. will use this book for his topics course in the next spring semester.
4 Jihyung
3
Minchul Shin: ECON 590B1, Fall 2015
Tentative schedule Date
Lecture
Topics
Bayesian econometrics Aug, 25 L1 Class introduction, Bayesian econometrics 1 - Intro Aug, 27 L2 Bayesian econometrics 2 Sep, 1 L3 Bayesian econometrics 3 Sep, 3 L4 Bayesian econometrics 4 Sep, 8 L5 Bayesian econometrics 5 Sep, 10 L6 Bayesian econometrics 6 Time-series econometrics Sep, 15 L7 Basic time series econometrics 1 - Intorduction and ARMA Sep, 17 L8 Basic time series econometrics 2 - ARMA and Unit-root Sep, 22 L9 Basic time series econometrics 3 - Reduced form VAR 1 Sep, 22 L10 Basic time series econometrics 4 - Reduced form VAR 2 Sep, 29 L11 State space modeling 1 - Introduction and Kalman filtering Oct, 1 L12 State space modeling 2 - Kalman filtering and MLE Oct, 6 L13 State space modeling 3 - Data-augmentation and Gibbs sampling Midterm Oct, 8 No class Oct, 13 No class
PS1
PS2
Take-home exam Take-home exam
DSGE model estimation and evaluation Oct, 15 L14 Structural VAR Oct, 20 L15 Midterm Review and DSGE model 1 - Introduction Oct, 22 L16 DSGE model 2 - Metropolis-Hastings algorithm Oct, 27 L17 DSGE model 3 - Model evaluation – Marginal Likelihood/Predictive Check Oct, 29 L18 DSGE model 4 - DSGE-VAR Nov, 3 L19 DSGE model 5 - ... Nov, 5 L20 DSGE model 6 - ... Nov, 10 No class Nov, 12 L21 DSGE model 7 - ... Nov, 17 No class Nonlinear models Nov, 19 L22 Nov, 24 Fall Break Nov, 26 Fall Break Dec, 1 L23 Dec, 3 L24 Dec, 8 L25
PS
Nonlinear State Space - Overview
Nonlinear State Space - Stochastic volatility Nonlinear State Space - Markov Switching Nonlinear State Space - Particle Filtering and PMCMC
Special topics: Application of structural VARs Dec, 11 L26 & L27 Structural VARs
PS 3
PS 4