Economics 307f
Empirical Methods for Dynamic Economic Models
Brandeis University
Spring 2016
George J. Hall
Course Outline and Reading List
Empirical Methods for Dynamic Economic Models is a topics course on contemporary methods
and issues in structural economics. In particular, this module will focus on the solution methods,
estimation, and evaluation of dynamic models of the macroeconomy and individual markets.
The learning goals for the course are: 1) learn how to solve dynamic models using recursive
methods; 2) learn how to estimate the structural parameters of dynamic models; 3) learn how to
assess the goodness-of-fit of mis-specified models.
Since most of the models we will be working with do not have analytical solutions, the first
part of the course will focus on computational methods. I will discuss solution techniques in class
and provide Matlab code. Students will be required to use the computer to solve models and
implement the solution and estimation methods presented in the course.
While we are going to work largely with Matlab in this course, you may wish to work in another computer language. Both python and Julia are popular programming languages for solving
economic models. The web course http://quant-econ.net/ covers many of the same topics we
will cover and uses these two languages. Below I refer to the material from this course.
One of the appealing aspects of dynamic structural models is the tight link these models
provide between theory and estimation. The solution (or really the equilibrium) of these models
are exactly the objects for which the econometrics is cast. The decision rules and the law of
motions for the state variables are Markov processes, and these Markov processes imply ergodic
distributions. Markov processes and ergodic distributions are objects econometricians are well set
up to estimate.
Of course most dynamic models suffer from a paucity of shocks and general mis-specification.
Furthermore it is often difficult if not impossible to fully characterize the likelihood function for
these models. Hence this course will discuss a variety of methods for parameterizing and evaluating the goodness of fit of these models. We will discuss not only the consistency and efficiency
of these methods, but their numerical implementation as well.
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The primary textbooks for this course are:
Adda, Jerome and Russell Cooper (2003) Dynamic Economics: Quantitative Methods and
Applications, The MIT Press.
DeJong, David with Chetan Dave (2011) Structural Macroeconometrics, 2nd edition, Princeton University Press.
Sargent, Thomas and John Stachurski (2016) QUANTITATIVE ECONOMICS with Python
available from http://quant-econ.net/
In addition to these three texts, you may find it useful to have the following books close at
hand:
Cochrane, John (2005) Asset Pricing, Princeton University Press.
Ljungqvist, Lars and Thomas J. Sargent (2012) Recursive Macroeconomic Theory, 3rd edition, The MIT Press
We will focus on numerically implementing the methods discussed. Consequently much of
the material will be presented in a more “cookbook” style than may be the case in other classes.
As you progress in your use of these methods, you will want to move beyond these cookbook
discussions; but hopefully this course will allow you make links between your economic theory
and econometrics courses. Moreover this course is designed to have you enter your third-year
of graduate school moving forward on a dissertation rather than spending time learning how to
translate what you have learned in your course work onto the computer. You will probably find
it useful to have on your bookshelf a good econometrics text at the level of
Greene, William (2011) Econometric Analysis, 7th edition, Prentice Hall and/or
Hamilton, James (1994) Time Series Analysis Princeton University Press.
Class will meet on Tuesday and Thursday afternoons from 5:00 to 6:20 in the Sachar Chancellor’s Suite.
Students are encouraged to ask questions in class or to stop by my office. My official office
hours are Wednesdays 3-5. You may wish to send me an e-mail before showing up if you would
like to meet outside that time. My contact information is:
George Hall
Lemberg 164
781-736-2242
ghall@brandeis.edu
Economics 302a and 304a are prerequisites for this course. Students who have take Econ 182a
may take this course with permission.
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There will be several homework assignments and a short research paper.
Success in this 4 credit hour course is based on the expectation that students will spend a
minimum of 9 hours of study time per week in preparation for class (readings, papers, discussion
sections, preparation for exams, etc.).
While you are encouraged to discuss the problems with others, you are expected to answer
problems on your own. Resist the temptation to copy someone else’s answer. This is worse than
useless as it is not only a violation of Brandeis University rules but also will lull you into a false
sense that you understand the material. More generally, you are expected to be familiar with and
to follow the University’s policies on academic integrity (see http://www.brandeis.edu/studentlife/sdc/ai).
Instances of alleged dishonesty will be forwarded to the Department of Student Rights and Community Standards for possible referral to the Student Judicial System. Potential sanctions include
failure in the course and suspension from the University.
If you are student with a documented disability on record at Brandeis University and wish to
have a reasonable accommodation made for you in this class, please see Mr. Hall immediately.
The home page for this course is on the LATTE system. Announcements, problem sets, computer programs, and additional handouts will be posted on this page. You are encourage to check
the web page regularly.
When writing a dissertation, you will find that your understanding of both current issues and
methods will be bolstered by regularly attending the Research Seminar, the Faculty Brown Bag
seminar, and the Ph.D. Workshop. You are strongly encouraged to attend all three.
The reading list provides neither a comprehensive list of methods nor a complete list of papers
developing and using these methods. The list is designed to highlight some of the key papers and
illustrate the methods used in a variety of settings.
1. Models and Solution Methods
(a) Discrete State Space Dynamic Programming
Cooper and Adda, chapters 1, 2 and 3.
Ljungqvist and Sargent, chapters 3 and 4.
Sargent and Stachurski, section 2.13
(b) Incomplete Markets Models with Uninsurable Individual Risks
Sargent and Stachurski, sections 3.3 and 3.7
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Ljungqvist-Sargent, chapter 17, will be distributed in class
Aiyagari, S. Rao (1994) “Uninsured Idiosyncratic Risk and Aggregate Saving”
Quarterly Journal of Economics 109, 659-684.
Huggett, Mark (1993) “The Risk-free Rate in Heterogeneous-Agents, Incomplete
Markets Economies” Journal of Economic Dynamics and Control 17, 953-969.
Imrohoroglu, Ayse (1992) “The Welfare Cost of Inflation Under Imperfect Insurance” Journal of Economic Dynamics and Control 16, 79-92.
(c) The Life-cycle Model
(d) The Optimal Linear Regulator
Ljungqvist and Sargent, chapter 5.
Sargent and Stachurski, section 2.12
(e) Time Series Analysis
DeJong and Dave, chapters 3 and 4.
Ljungqvist and Sargent, chapter 2.
Sargent and Stachurski, sections 2.6, 3.8
Hansen, Lars and Thomas Sargent (2005) Recursive Models of Dynamic Linear
Economies, chapter 2. Manuscript available from
http://homepages.nyu.edu/~ts43/books/mbook2.pdf. Also see accompanying
Matlab computer code.
2. Taking models to the data
(a) Calibration
DeJong and Dave, chapter 6.
Kydland Finn and Edward Prescott (1996) “The Computational Experiment: An
Econometric Tool,” Journal of Economic Perspectives 10:1 Winter, 69-85.
Hansen, Lars and James Heckman (1996) “The Empirical Foundations of Calibration,” Journal of Economic Perspectives 10:1 Winter, 87-104.
Sims, Christopher (1996) “Macroeconomics and Methodology,” Journal of Economic Perspectives 10:1 Winter, 105-120.
Browning, Martin, Lars Hansen and James Heckman (1999) “Micro Data and
General Equilibrium Models” Handbook of Macroeconomics Volume 1 edited by
J.B. Taylor and M. Woodford, Elsevier Science B.V.
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DeJong David, Beth Ingram, and Charles Whiteman (1996)“A Bayesian Approach
to Calibration,” Journal of Business and Economic Statistics 14, 1-9.
Watson, Mark (1993) “Measures of Fit for Calibrated Models,” Journal of Political
Economy 101:6, 1011-1041.
(b) Maximum Likelihood
Adda and Cooper, chapter 4.
DeJong and Dave, chapter 8.
Sargent and Stachurski, section 2.8
Hansen, Lars and Thomas Sargent (2005) Recursive Models of Dynamic Linear
Economies, chapters 8 and 9. Manuscript available from
http://homepages.nyu.edu/~ts43/BOOKS/books.htm. Also see accompanying
Matlab computer code.
Fernandez-Villarverde, Jesus, Juan F. Rubio Ramirez, and Frank Schorfheide
(2016) “Solution and Estimation Methods for DSGE Models” NBER Working
Paper 21862. Section 10 and Section 11.1.
Griffoli, Tommaso Mancini (2007) Dynare User Guide.
Anderson, Evan, Lars Hansen, Ellen McGrattan, and Thomas Sargent (1996) “Mechanics of Forming and Estimating Dynamic Linear Economies” In Hans Amman,
David Kenrick and John Rust (eds.), Handbook of Computational Economics Amsterdam: Elsevier Science, North Holland, 171-252.
Hall, George (1996) “Overtime, Effort and the Propagation of Business Cycle
Shocks,” Journal of Monetary Economics 38, 139-160.
Hall, George (2004) “Exchange Rates and Casualties During the First World War,”
Journal of Monetary Economics 51, 1711-1742.
(c) Generalized Method of Moments
Adda and Cooper, chapter 4.
DeJong and Dave, chapter 7.
Christiano, Lawrence and Martin Eichenbaum (1992) “Current Real Business Cycle Theories and Aggregate Labor Market Fluctuations,” American Economic Review 82, June, 430-450.
Burnside, Craig (2000) “Matlab Code for Real Business Cycle Models: Linear
Approximation and GMM Estimation” Available from
http://ideas.repec.org/c/dge/qmrbcd/76.html
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(d) Indirect Inference
Adda and Cooper, chapter 4.
DeJong and Dave, chapter 7.
Fernandez-Villarverde, Jesus, Juan F. Rubio Ramirez, and Frank Schorfheide
(2016) “Solution and Estimation Methods for DSGE Models” NBER Working
Paper 21862. Sections 11.2 and 11.3.
Smith, Anthony (1993) “Estimating Nonlinear Time-Series Models Using Simulated Vector Autoregressions,” Journal of Applied Econometrics, 8, December,
S63-S84.
Cooper Russell, and John Haltiwanger (2000) “On the Nature of the Capital Adjustment Process,” NBER Working Paper 7925.
Copeland, Adam and George Hall (2011) “The Response of Prices, Sales, and Output to Temporary Changes in Demand,” Journal of Applied Econometrics March,
Vol. 26, No. 2, pp. 232-269.
Graddy, Kathryn and George Hall (2011) “A Dynamic Model of Price Discrimination and Inventory Management at the Fulton Fish Market” Journal of Economic
Behavior and Organization, September, Vol. 80, No. 1, pp. 6-19.
(e) Bayesian Approaches
DeJong and Dave, chapter 9.
Fernandez-Villarverde, Jesus, Juan F. Rubio Ramirez, and Frank Schorfheide
(2016) “Solution and Estimation Methods for DSGE Models” NBER Working
Paper 21862. Sections 12.
Fernandez-Villaverde, Jesus and Juan Rubio-Ramirez (2004) “Comparing Equilibrium Models to Data: A Bayesian Approach,” Journal of Econometrics 123,
153-187.
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