References

This folder holds the following references to publications, sorted by year and author.

There are 48 references in this bibliography folder.

Chen, S, Chang, C, and Du, Y (2012).
Agent-based economic models and econometrics
Knowledge Engineering Review, 27(02):187-219.

Galam, S (2012).
The question: Do humans behave like atoms?
Understanding Coplex Systems:21-39.

De Grauwe, P (2010b).
Animal spirits and monetary policy
Economic Theory(urn:hdl:123456789/288339).

De Grauwe, P (2010a).
The scientific foundation of dynamic stochastic general equilibrium (DSGE) models
Public Choice, 144(3):413-443.

Chang, Y, Kim, S, and Schorfheide, F (2010).
Financial frictions, aggregation, and the Lucas critique
, Working Papers.

Lengnick, M and Wohltmann, H (2010).
Agent-based financial markets and New Keynesian macroeconomics: A synthesis
Christian-Albrechts-University of Kiel, Department of Economics, Working Papers(2011,09).

Schiavo, S, Reyes, J, and Fagiolo, G (2010).
International trade and financial integration: A weighted network analysis
Quantitative Finance, 10(4):389-399.

Wen, Y (2010).
Liquidity demand and welfare in a heterogeneous-agent economy
Federal Reserve Bank of St. Louis, Working Papers(2010-009).

Westerhoff, F (2010).
An agent-based macroeconomic model with interacting firms, socio-economic opinion formation and optimistic/pessimistic sales expectations
, Working Papers(7).

Alfarano, S and Milakovic, M (2009).
Network structure and N-dependence in agent-based herding models
Journal of Economic Dynamics and Control, 33(1):78-92.

Alfarano, S, Milaković, M, and Raddant, M (2009).
Network hierarchy in Kirman's ant model: Fund investment can create systemic risk
Christian-Albrechts-University of Kiel, Department of Economics, Working Papers(2009,09).

Assenza, T, Heemeijer, P, Hommes, C, and Massaro, D (2009).
Experimenting with expectations: From individual behavior in the lab to aggregate macro behavior
, Working Papers.

Branch, W and McGough, B (2009).
A new Keynesian model with heterogeneous expectations
Journal of Economic Dynamics and Control, 33(5):1036-1051.

Milani, F (2009).
Adaptive learning and macroeconomic inertia in the euro area
Journal of Common Market Studies, 47:579-599.

Chen, Y and Kulthanavit, P (2008).
Monetary policy design under imperfect knowledge: An open economy analysis
University of Washington, Department of Economics, Working Papers(UWEC-2008-14).

Iori, G, De Masi, G, Precup, O, Gabbi, G, and Caldarelli, G (2008).
A network analysis of the Italian overnight money market
Journal of Economic Dynamics and Control, 32(1):259-278.

Orphanides, A and Williams, J (2007a).
Robust monetary policy with imperfect knowledge
Journal of Monetary Economics, 54(5):1406-1435.

Bask, M (2007).
Long swings and chaos in the exchange rate in a DSGE model with a Taylor rule
Bank of Finland, Research Discussion Papers(19/2007).

Orphanides, A and Williams, J (2007).
Imperfect knowledge, inflation expectations, and monetary policy
In: The Inflation-Targeting Debate, ed. by Bernanke, M. and Woodford, M., pp. 201-246, IL: University of Chicago Press.

Vega-Redondo, F (2007).
Complex social networks. Econometric society monograph series
Cambridge: Cambridge University Press.

Jackson, M (2005).
A survey of network formation models: Stability and efficiency
In: Group formation in economics, ed. by Demange, G. and Wooders, M., pp. 11–88, Cambridge: Cambridge University Press.

Lux, T and Schornstein, S (2005).
Genetic learning as an explanation of stylized facts of foreign exchange markets
Journal of Mathematical Economics, 41(1-2):169-196.

Ball, P (2004).
Critical mass: How one thing leads to another
Farrar, Straus and Giroux, New: York. (ISBN: 0374281254).

Aiello, W, Chung, F, and Lu, L (2002).
Random evolution in massive graphs
In: Handbook of massive data sets, ed. by Abello, J., Pardalos, M. and Resende, M.G.C., pp. 97-122, Dordrecht: Kluwer.

Ebel, H, Mielsch, L, and Bornholdt, S (2002).
Scale-free topology of e-mail networks
Physical Review E, 66(3).