390031 SE PhD-VGSE: Numerical and Empirical Methods in Applied Microeconomics (2016S)
Continuous assessment of course work
Labels
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Th 03.03.2016 11:39 to Fr 04.03.2016 12:00
- Deregistration possible until Fr 04.03.2016 15:00
Details
max. 24 participants
Language: English
Lecturers
Classes
Monday, 30.5., 11.30 - 13.00 , Seminarroom VGSE
Monday, 30.5., 15.00 - 16.30, Seminarroom VGSETuesday, 31.5., 11.00 - 12.30, Seminarroom VGSETuesday, 31.5., 13.30 - 15.00, Seminarroom VGSEFriday, 3. 6., 09.00 - 10.30, Seminarroom VGSEFriday, 3.6., 11.00 - 12.30, Seminarroom VGSEMonday, 6.6., 11.30 - 13.00, Seminarroom VGSEMonday, 6.6., 15.00 - 16.30, Seminarroom VGSETuesday, 7.6., 11.00 - 12.30, Seminarroom VGSETuesday, 7.6., 13.30 - 15.00, Seminarroom VGSEFriday, 10.6., 09.00 - 10.30, Seminarroom VGSEFriday, 10.6., 11.00 - 12.30, Seminarroom VGSEInformation
Aims, contents and method of the course
Assessment and permitted materials
There will be two problem sets, each of which accounts for one half of the course grade.
Homework 1: Estimating a Rust (1987) type model
Homework 2: Computing and estimating a dynamic model
I strongly recommend that you work as a group of several students, but each of you should write your own answer/code.
Homework 1: Estimating a Rust (1987) type model
Homework 2: Computing and estimating a dynamic model
I strongly recommend that you work as a group of several students, but each of you should write your own answer/code.
Minimum requirements and assessment criteria
Examination topics
Reading list
Reading. There is no required textbook for this course. During the lectures, I will mainly use expositions and examples from the following three textbooks:1. Judd, K. (1998). Numerical Methods in Economics. The MIT Press.
2. Miranda, M. and Fackler, P. (2002). Applied Computational Economics and Finance. The MIT Press.
3. Heer, B. and Maussner, A. (2009). Dynamic General Equilibrium Modeling. Springer, 2nd edition.You do not need to buy any of these textbooks. I will distribute class slides for each lecture. Additional readings are listed below.
1. Aguirregabiria, V. and Mira, P. (2007). Sequential Estimation of Dynamic Discrete Games. Econometrica 75(1): 1-53.
2. Arcidiacono, P. and Miller, R. (2011). Conditional Choice Probability Estimation of Dynamic Discrete Choice Models with Unobserved Heterogeneity. Econometrica 79(6): 1823-1867.
3. Bajari, P., Benkard, L. and Levin, J. (2007). Estimating Dynamic Models of Imperfect Competition. Econometrica 75(5): 1331-70.
4. Collard-Wexler, A. (2013). Demand fluctuations in the ready-mix concrete industry, Econometrica 81(3): 1003-1037.
5. Ericson, R. and Pakes, A. (1995). Markov Perfect Industry Dynamics: A Framework for Empirical Work. Review of Economic Studies 62(1): 53-82.
6. Hotz, J. and Miller, R. (1993). Conditional Choice Probabilities and the Estimation of Dynamic Models, Review of Economic Studies 60(3): 497-529.
7. Hotz, V., Miller, R., Sanders, S., and Smith, J. (1994). A Simulation Estimator for Dynamic Models of Discrete Choice. Review of Economic Studies 61(2): 265-289.
8. Keane, M. and Wolpin, K. (1997). The Career Decisions of Young Men. Journal of Political Economy 105(3): 473-522.
9. Lee, D. and Wolpin, K. (2006). Intersectoral Labor Mobility and the Growth of the Service Sector. Econometrica 74(1): 1-46.
10. Otsu, T., Pesendorfer, M., and Takahashi, Y. (2015). Pooling Data across Markets in Dynamic Markov Games, forthcoming in Quantitative Economics.
11. Pesendorfer, M. and Schmidt-Dengler, P. (2003). Identification and Estimation of Dynamic Games. NBER Working paper 9726.
12. Pesendorfer, M. and Schmidt-Dengler, P. (2008). Asymptotic Least Squares Estimators for Dynamic Games. Review of Economic Studies 75(3): 901-928.
13. Pesendorfer, M. and Schmidt-Dengler, P. (2010). Sequential Estimation of Dynamic Discrete Games: A Comment. Econometrica 78(2): 833-842.
14. Rust, J. (1987). Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher. Econometrica 55(5): 999-1033.
15. Rust, J. (1994). Structural Estimation of Markov Decision Processes. In R. Engle and D. McFadden (eds.) Handbook of Econometrics Volume 4, 3082-3139, North Holland.
16. Ryan, S. (2012). The Costs of Environmental Regulation in a Concentrated Industry, Econometrica 80(3): 1019-1061.
17. Schmidt-Dengler, P. (2006). The Timing of New Technology Adoption: The Case of MRI, Working Paper.
18. Seim, K. (2006). An Empirical Model of Firm Entry with Endogenous Product-Type Choices. RAND Journal of Economics 37(3): 619-640.
19. Timmins, C. (2002). Measuring the Dynamic Efficiency Costs of Regulators Preferences: Municipal Water Utilities in the Arid West. Econometrica 70(2): 603-629.
2. Miranda, M. and Fackler, P. (2002). Applied Computational Economics and Finance. The MIT Press.
3. Heer, B. and Maussner, A. (2009). Dynamic General Equilibrium Modeling. Springer, 2nd edition.You do not need to buy any of these textbooks. I will distribute class slides for each lecture. Additional readings are listed below.
1. Aguirregabiria, V. and Mira, P. (2007). Sequential Estimation of Dynamic Discrete Games. Econometrica 75(1): 1-53.
2. Arcidiacono, P. and Miller, R. (2011). Conditional Choice Probability Estimation of Dynamic Discrete Choice Models with Unobserved Heterogeneity. Econometrica 79(6): 1823-1867.
3. Bajari, P., Benkard, L. and Levin, J. (2007). Estimating Dynamic Models of Imperfect Competition. Econometrica 75(5): 1331-70.
4. Collard-Wexler, A. (2013). Demand fluctuations in the ready-mix concrete industry, Econometrica 81(3): 1003-1037.
5. Ericson, R. and Pakes, A. (1995). Markov Perfect Industry Dynamics: A Framework for Empirical Work. Review of Economic Studies 62(1): 53-82.
6. Hotz, J. and Miller, R. (1993). Conditional Choice Probabilities and the Estimation of Dynamic Models, Review of Economic Studies 60(3): 497-529.
7. Hotz, V., Miller, R., Sanders, S., and Smith, J. (1994). A Simulation Estimator for Dynamic Models of Discrete Choice. Review of Economic Studies 61(2): 265-289.
8. Keane, M. and Wolpin, K. (1997). The Career Decisions of Young Men. Journal of Political Economy 105(3): 473-522.
9. Lee, D. and Wolpin, K. (2006). Intersectoral Labor Mobility and the Growth of the Service Sector. Econometrica 74(1): 1-46.
10. Otsu, T., Pesendorfer, M., and Takahashi, Y. (2015). Pooling Data across Markets in Dynamic Markov Games, forthcoming in Quantitative Economics.
11. Pesendorfer, M. and Schmidt-Dengler, P. (2003). Identification and Estimation of Dynamic Games. NBER Working paper 9726.
12. Pesendorfer, M. and Schmidt-Dengler, P. (2008). Asymptotic Least Squares Estimators for Dynamic Games. Review of Economic Studies 75(3): 901-928.
13. Pesendorfer, M. and Schmidt-Dengler, P. (2010). Sequential Estimation of Dynamic Discrete Games: A Comment. Econometrica 78(2): 833-842.
14. Rust, J. (1987). Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher. Econometrica 55(5): 999-1033.
15. Rust, J. (1994). Structural Estimation of Markov Decision Processes. In R. Engle and D. McFadden (eds.) Handbook of Econometrics Volume 4, 3082-3139, North Holland.
16. Ryan, S. (2012). The Costs of Environmental Regulation in a Concentrated Industry, Econometrica 80(3): 1019-1061.
17. Schmidt-Dengler, P. (2006). The Timing of New Technology Adoption: The Case of MRI, Working Paper.
18. Seim, K. (2006). An Empirical Model of Firm Entry with Endogenous Product-Type Choices. RAND Journal of Economics 37(3): 619-640.
19. Timmins, C. (2002). Measuring the Dynamic Efficiency Costs of Regulators Preferences: Municipal Water Utilities in the Arid West. Econometrica 70(2): 603-629.
Association in the course directory
Last modified: Mo 07.09.2020 15:46
- L-U factorization
- Iterative methods
Lecture 2 (May. 30) Non-linear equations
- Bisection method
- Fixed-point iteration
- Newton method
- Application: Solving entry model
Lecture 3 (May. 31) Dynamic programming
- Math preparation
- Dynamic discrete choice
Lecture 4 (May. 31) Applications
- Rust (1987)
- Application: Timmins (2002)
Lecture 5 (Jun. 3) Optimization
- Comparison method
- Newton-Raphson method
- Stochastic search
Lecture 6 (Jun. 3) Function approximation
- Local approximation methods
- Interpolation methods
Lecture 7 (Jun. 6) Numerical integration and differentiation
- Newton-Cotes methods
- Gaussian quadrature
- Monte Carlo integration
- Numerical differentiation
Lecture 8 (Jun. 6) Applications
- Keane and Wolpin (1997)
- Lee and Wolpin (2006)
Lecture 9 (Jun. 7) CCP methods
- Hotz and Miller (1993)
- Hotz, Miller, Sanders, and Smith (1994)
Lecture 10 (Jun. 7) Estimation of dynamic game I
- Introduction to estimation of games: static entry models
- Dynamic Markov game
- Nested fixed point algorithm VS two step methods
Lecture 11 (Jun. 10) Estimation of dynamic game II
- Pesendorfer and Schmidt-Dengler (2008)
- Bajari, Benkard, and Levin (2007)
- Aguirregabiria and Mira (2007)
Lecture 12 (Jun. 10) Applications
- Schmidt-Dengler (2006)
- Ryan (2012)
- Collard-Wexler (2013)