Econ7115: Structural Models and Numerical Methods in Economics

Zi Wang
Hong Kong Baptist University
Spring 2025

The objective of this course is to familiarize students with structural models across various economic fields. Throughout the course, students will acquire a comprehensive understanding of: (i) the fundamental concepts underlying structural modelling, (ii) the state-of-art process of connecting models with empirical data in economics, (iii) methods for identifying and estimating structural models, and (iv) essential numerical techniques required for structural modelling. Additionally, students will practice the Matlab programming language for numerical solutions in structural modeling.

Logistics

Course materials: https://github.com/WangZi-HKBU/econ7115
Class schedule: Wednesdays 18:30-21:20
Location: DLB712
Email: [email protected]
Office hours: By appointment, please email

Assessments

• Class Participation/Discussion (10%)
• Bi-weekly Assignments (60%)
• Individual Project (30%)

Main References

• Mario Miranda and Paul Fackler (MF). 2004. Applied Computational Economics and Finance.
• Jerome Adda and Russell Cooper (AC). 2003. Dynamic Economics: Quantitative Methods and Applications

Course Outline and Reading List

I may revise the course outline and reading list during the semester.

Week 1: Introduction and solving equations

• Reiss, Peter, and Frank Wolak (2007), "Structural Economic Modeling: Rationales and Examples from Industrial Organization." Handbook of Econometrics
• Low, Hamish, and Costas Meghir (2017), "The Use of Structural Models in Econometrics." Journal of Economic Perspectives
• Whited, Toni M. (2015), "Overview of Structural Estimation." Lecture in University of Texas, Austin
• Whited, Toni M. (2023) "Integrating Structural and Reduced-Form Methods in Empirical Finance." Journal of Financial Econometrics
• MF Ch3
• Andrews, Isaiah et al. (2017), "Measuring the Sensitivity of Parameter Estimates to Estimation Moments"
• Sanders, Bas (2023), "Counterfactual Sensitivity in Quantitative Trade and Spatial Models"

Week 2: Optimization

• Gentzkow, Matthew and Jesse M. Shapiro (2014) "Code and Data for the Social Sciences: A Practitioner's Guide"
• MF Ch4
• Su, C.-L. and Judd, K.L. (2012), "Constrained Optimization Approaches to Estimation of Structural Models." Econometrica
• Rust, John (2000), "Nested Fixed Point Algorithm: Documentation Manual." Technical notes
• Fernandez-Villaverde, Jesus (2023),"Taming the Curse of Dimensionality: Old Ideas and New Strategies"

Week 3: Numerical differentiation and integration

• MF Ch5
• Sun, Jeffrey (2023), "Granular Agents: Implicit Automatic Differentiation." Technical notes
• Edmond, Chris et al. (2023), "How Costly Are Markups?" Journal of Political Economy

Week 4: Dynamic programming

• AC Ch2&5
• Aw, Bee Yan, Mark J. Roberts, and Daniel Yi Xu. (2008). "R&D Investments, Exporting, and the Evolution of Firm Productivity." American Economic Review
• Maliar et al. (2021). "Deep learning for solving dynamic economic models." Journal of Monetary Economics
• Cai and Judd (2014). "Advances in Numerical Dynamic Programming and New Applications" Chapter 8, Handbook of Computational Economics, Volume 3

Week 5: Quantitative trade models

• Lashkaripour, Ahmad, and Volodymyr Lugovskyy. (2023). "Profits, Scale Economies, and the Gains from Trade and Industrial Policy." American Economic Review
• Dominick Bartelme, Arnaud Costinot, Dave J. Donaldson, and Andres Rodriguez-Clare. (2025) "The Textbook Case for Industrial Policy: Theory Meets Data." Journal of Political Economy
• Ossa, Ralph. (2014). "Trade Wars and Trade Talks with Data." American Economic Review
• Eaton, J., Kortum, S. and Kramarz, F. (2011), "An Anatomy of International Trade: Evidence From French Firms." Econometrica
• Allen, Treb, Costas Arkolakis, and Xiangliang Li. 2024. "On the Equilibrium Properties of Spatial Models." American Economic Review: Insights 6 (4): 472–89.

Week 6: Quantitative spatial models

• Treb Allen, Costas Arkolakis (2014). "Trade and the Topography of the Spatial Economy." The Quarterly Journal of Economics
• Ahlfeldt, G.M., Redding, S.J., Sturm, D.M. and Wolf, N. (2015), "The Economics of Density: Evidence From the Berlin Wall." Econometrica
• Monte, Ferdinando, Stephen J. Redding, and Esteban Rossi-Hansberg (2018), "Commuting, Migration, and Local Employment Elasticities." American Economic Review
• Treb Allen, Costas Arkolakis (2022), "The Welfare Effects of Transportation Infrastructure Improvements", The Review of Economic Studies

Week 7: Dynamic trade and spatial models

• Cai, Jie, Nan Li, and Ana Maria Santacreu. (2022). "Knowledge Diffusion, Trade, and Innovation across Countries and Sectors." American Economic Journal: Macroeconomics
• Caliendo, L., Dvorkin, M. and Parro, F. (2019), "Trade and Labor Market Dynamics: General Equilibrium Analysis of the China Trade Shock." Econometrica
• Kleinman, B., Liu, E. and Redding, S.J. (2023), "Dynamic Spatial General Equilibrium." Econometrica

Week 8: Productivity and quality estimation

• Olley, G. S., & Pakes, A. (1996). The Dynamics of Productivity in the Telecommunications Equipment Industry. Econometrica
• James Levinsohn, Amil Petrin (2003), Estimating Production Functions Using Inputs to Control for Unobservables, The Review of Economic Studies
• Ackerberg, D.A., Caves, K. and Frazer, G. (2015), Identification Properties of Recent Production Function Estimators. Econometrica
• Amit Khandelwal (2010).The Long and Short (of) Quality Ladders, The Review of Economic Studies
• Paul Piveteau, Gabriel Smagghue (2019). "Estimating firm product quality using trade data." Journal of International Economics

Week 9: Demand estimation

• Berry, S., Levinsohn, J., & Pakes, A. (1995). Automobile Prices in Market Equilibrium. Econometrica
• Nevo, A. (2001). Measuring Market Power in the Ready-to-Eat Cereal Industry. Econometrica
• Berry, S., Levinsohn, J., & Pakes, A. (2004). Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The New Car Market. Journal of Political Economy

Week 10: Dynamic discrete choice models

• Keane, Michael P., and Kenneth I. Wolpin. (2009). "Empirical applications of discrete choice dynamic programming models." Review of Economic Dynamics
• Arcidiacono, Peter, and Paul B. Ellickson. (2011). "Practical Methods for Estimation of Dynamic Discrete Choice Models." Annual Review of Economics

Week 11: Using IV to identify and validate structural models

• Davis, M.A., Fisher, J.D.M. and Whited, T.M. (2014), "Macroeconomic Implications of Agglomeration." Econometrica
• Rodrigo Adao, Arnaud Costinot, Dave Donaldson (2024) "Putting Quantitative Models to the Test: an Application to the US-China Trade War." The Quarterly Journal of Economics

Week 12: Indirect inference

• Anthony A. Smith Jr. (2008). "Indirect Inference" The New Palgrave Dictionary of Economics
• Catherine, S., Chaney, T., Huang, Z., Sraer, D. and Thesmar, D. (2022), Quantifying Reduced-Form Evidence on Collateral Constraints. The Journal of Finance
• Konrad B. Burchardi, Thomas Chaney, Tarek Alexander Hassan, Lisa Tarquinio & Stephen J. Terry. (2024) "Immigration, Innovation, and Growth." American Economic Review

Week 13: Sufficient statistics

• Donaldson, Dave. (2022). "Blending Theory and Data: A Space Odyssey." Journal of Economic Perspectives, 36 (3): 185–210.
• Henrik J. Kleven. (2021). "Sufficient Statistics Revisited." Annual Review of Economics