Central Bank of Lithuania

Syllabus

Lecture 1: Agent-Based (AB) models: Features and historical development.

Lecture 2: Agents’ behaviour: Expectations, bounded rationality, learning.

Lecture 3: AB macro models.

Lecture 4: Modelling general equilibrium effects in MS models.

Lecture 5: Tax-benefit MS models: EUROMOD - Intro

Lecture 6: Tax-benefit MS models: EUROMOD - Use

Lecture 7: Simulation platforms: JAS-mine.

Lecture 8: Analysis of model behaviour: AB models as Markov chains, statistical equilibria, sensitivity analysis.

Lecture 9: Calibration and estimation of AB models.

Laboratory 1: Simulation platforms: Netlogo.

Laboratory 2: Implementation of the Schelling segregation model in Netlogo

Laboratory 3: Implementation of a simple model of choice (“model X”) in Netlogo.

Laboratory 4: The JAS-mine demo models.

Laboratory 5: Implementation of the Demo07 model in JAS-mine.

Laboratory 6: Implementation of the Job Application model in JAS-mine.

Laboratory 7: Implementation of model X in JAS-mine.

Laboratory 8: Implementation of the K+S model in JAS-mine.

Reading list

(A personal selection; a more comprehensive list is available from the ACE website maintained by Leigh Tesfatsion)

Introduction to Agent-Based modelling

1. Billari, Prskawetz (2003). Agent-Based Computational Demography. Using Simulation to Improve Our Understanding of Demographic Behaviour. Springer.

2. Cederman (2001). Agent-based modeling in political science. The Political Methodologist 10 (1): 16–22.

3. Chen, Kaboudan, Du (2018). The Oxford Handbook of Computational Economics and Finance. Oxford University Press.

4. Epstein (2006). Generative Social Science: Studies in Agent-Based Computational Modeling. Princeton University Press.

5. Epstein, Axtell (1996). Growing Artificial Societies: Social Science from the Bottom Up. The MIT Press.

6. Gilbert, Hamill (2015). Agent-based modelling in Economics. Wiley.

7. Kendrick, Mercado, Amman (2006). Computational Economics. Princeton University Press.

8. Macy, Willer (2002). From Factors to Actors: Computational Sociology and Agent-Based Modeling. Annual Review of Sociology 28: 143-166.

9. Miller, Page (2006). Complex Adaptive Systems: An Introduction to Computational Models of Social Life. Princeton University Press.

10. Delli Gatti, Gallegati, Fagiolo, Richiardi, Russo (2018). Agent-based Models in Economics: A Toolkit. Cambridge University Press.

11. Tesfatsion, Judd (eds.) (2006), Handbook of Computational Economics. Elsevier.

Introduction to microsimulation modelling

1. Aaberge, Colombino (2018,). Structural Labour Supply Models And Microsimulation. International Journal of Microsimulation.

2. Figari, Sutherland (2013). EUROMOD: the European Union tax-benefit microsimulation model. International Journal of Microsimulation, 6(1): 4-26.

3. Li, O'Donoghue (2013). A survey of dynamic microsimulation models: uses, model structure and methodology. International Journal of Microsimulation, 6(2): 3-55

4. Sutherland (2018). Quality Assessment Of Microsimulation Models: The Case Of EUROMOD. International Journal of Microsimulation.

Agent-Based macro and finance

1. Barde (2016). Direct comparison of agent-based models of herding in financial markets. Journal of Economic Dynamics and Control, 73(C): 329-353.

2. Caiani, Godin, Caverzasi, Gallegati, Kinsella, Stiglitz (2016). "Agent based-stock flow consistent macroeconomics: Towards a benchmark model," Journal of Economic Dynamics and Control, Elsevier, 69(C): 375-408.

3. Dawid, Gemkow, Harting, van der Hoog, Neugart (2018). Agent-Based Macroeconomic Modeling and Policy Analysis: The Eurace@Unibi Model. In: Chen, Kaboudan, Du. The Oxford Handbook of Computational Economics and Finance. Oxford University Press.

4. Dilaver, Jump, Levine (2016). Agent-based Macroeconomics and Dynamic Stochastic General Equilibrium Models: Where do we go from here? School of Economics Discussion Papers 0116, School of Economics, University of Surrey.

5. Dosi, Napoletano, Roventini, Treibich (2017). Micro and macro policies in Keynes+Schumpeter evolutionary models. Journal of Evolutionary Economics, 27(1): 63-90.

6. Erlingsson, Teglio, Cincotti, Stefansson, Sturlusson, Raberto (2014). Housing market bubbles and business cycles in an agent-based credit economy. Economics - The Open-Access, Open-Assessment E-Journal, 8:1-42.

7. Fagiolo, Roventini (2017). Macroeconomic Policy in DSGE and Agent-Based Models Redux: New Developments and Challenges Ahead. Journal of Artificial Societies and Social Simulation, 20(1) 1.

8. Farmer, Foley (2009). The economy needs agent-based modelling. Nature, 460: 685-686.

9. Geanakoplos, Axtell, Farmer, Howitt, Conlee, Goldstein, Hendrey, Palmer, Yang (2012). Getting at Systemic Risk via an Agent-Based Model of the Housing Market. American Economic Review, 102(3): 53-58.

10. Neugart, Richiardi (2018). Agent-Based Models of the Labor Market. In: Chen, Kaboudan, Du. The Oxford Handbook of Computational Economics and Finance. Oxford University Press.

11. Popoyan, Napoletano, Roventini (2017). Taming macroeconomic instability: Monetary and macro-prudential policy interactions in an agent-based model. Journal of Economic Behavior & Organization, 134(C): 117-140.

12. Riccetti, Russo, Gallegati (2016). "Financialisation and crisis in an agent based macroeconomic model," Economic Modelling, Elsevier, 52(PA): 162-172.

13. Salle (2015). Modeling expectations in agent-based models - An application to central bank's communication and monetary policy. Economic Modelling, 46(C): 130-141.

Micro-to-macro simulations

1. Eliasson (2018). Why Complex, Data Demanding and Difficult to Estimate Agent Based Models? Lessons from a decades long research program. International Journal of Microsimulation.

2. International Journal of Microsimulation (2010). Special issue on Macro-Micro Analytics: A Guide to Combining Computable General Equilibrium and Microsimulation modelling frameworks.

3. International Journal of Microsimulation (2016). Special issue on CGE Microsimulations in Developing countries.

Estimation of Agent-Based models

1. Barde (2017). A Practical, Accurate, Information Criterion for Nth Order Markov Processes. Computational Economics, 50(2): 281-324.

2. Grazzini, Richiardi, Tsionas (2017). Bayesian Estimation of Agent-Based Models. Journal of Economic Dynamics and Control, 77: 26-47.

3. Grazzini, Richiardi (2015). Estimation of Ergodic Agent-Based Models by Simulated Minimum Distance. Journal of Economic Dynamics and Control, 51: 148-165.

4. Jabot, Lagarrigues, Courbaud, Dumoulin (2014). A comparison of emulation methods for Approximate Bayesian Computation. arXiv:1412.7560.

5. Lamperti, Roventini, Sani (2018). Agent-Based Model Calibration Using Machine-Learning Surrogates. Journal of Economic Dynamics & Control, 90: 366-389.

6. Salle, Murat (2014). Efficient Sampling and Meta-Modeling for Computational Economic Models. Computational Economics, 44(4): 507-536.

Methodological issues

1. Messier (2017). The Code is the Model. International Journal of Microsimulation, 10(3): 184-201.

2. Richiardi (2013). The missing link: AB models and dynamic microsimulation. In: Leitner S, Wall, F (eds). Artificial Economics and Self Organization. Agent-Based Approaches to Economics and Social Systems. Springer, Lecture Notes in Economics and Mathematical Systems, vol. 669, Berlin.

3. Richiardi (2016). The Future of Agent-Based Modelling. Eastern Economic Journal, 43(2): 271-287.

4. Richiardi (2017). The Code and the Model. International Journal of Microsimulation, 10(3): 204-208.

5. Tang (2017). The Code is the Model, Sometimes. International Journal of Microsimulation, 10(3): 202-203.

Simulation platforms

1. JAS-mine

2. Liam2

3. Modgen

4. Netlogo