Altissimo, F. and A. Mele (2009). Simulated non-parametric estimation of dynamic models. The Review of Economic Studies 76 (2), 413–450. Andersen, T. G., L. Benzoni, and J. Lund (2002). An empirical investigation of continuous-time equity return models. The Journal of Finance 57 (3), 1239–1284.

Angeletos, G.-M. and C. Lian (2018). Forward guidance without common knowledge. American Economic Review 108 (9), 2477–2512.

Anufriev, M. and C. Hommes (2012). Evolutionary selection of individual expectations and aggregate outcomes in asset pricing experiments. American Economic Journal: Microeconomics 4 (4), 35–64.

Anufriev, M., C. Hommes, and T. Makarewicz (2018). Simple forecasting heuristics that make us smart: Evidence from different market experiments. Journal of the European Economic Association 17 (5), 1538–1584.

Anufriev, M., T. Bao, and J. Tuinstra (2016). Microfoundations for switching behavior in heterogeneous agent models: An experiment. Journal of Economic Behavior & Organization 129, 74 – 99.

Assenza, T., P. Heemeijer, C. Hommes, and D. Massaro (2013). Individual expectations and aggregate macro behavior. Tinbergen Institute Discussion Papers 13-016/II, Tinbergen Institute.

Assenza, T., T. Bao, C. Hommes, and D. Massaro (2014). Experiments on expectations in macroeconomics and finance. In Experiments in Macroeconomics, Chapter 2, pp. 11–70. Emerald.

Barde, S. (2017). A practical, accurate, information criterion for Nth order Markov processes. Computational Economics 50 (2), 281–324.

Barde, S. (2020). Macroeconomic simulation comparison with a multivariate extension of the Markov information criterion. Journal of Economic Dynamics and Control 111, 103795.

Barde, S. and S. van der Hoog (2017). An empirical validation protocol for large-scale agent-based models. Technical report, Bielefeld Working Papers in Economics and Management No. 04-2017.

Boswijk, H. P., C. H. Hommes, and S. Manzan (2007). Behavioral heterogeneity in stock prices. Journal of Economic Dynamics & Control 31 (2), 1938–1970.

Brock, W. A. and C. H. Hommes (1997). A rational route to randomness. Econometrica 65 (5), 1059–1095.

Brock, W. A. and C. H. Hommes (1998). Heterogeneous beliefs and routes to chaos in a simple asset pricing model. Journal of Economic Dynamics & Control 22, 1235–1274.

Chari, V. V., P. J. Kehoe, and E. R. McGrattan (2002). Can sticky price models generate volatile and persistent real exchange rates? The Review of Economic Studies 69 (3), 533–563.

Coibion, O. and Y. Gorodnichenko (2012). What can survey forecasts tell us about information rigidities? Journal of Political Economy 120 (1), 116–159.

Coibion, O. and Y. Gorodnichenko (2015, August). Information rigidity and the expectations formation process: A simple framework and new facts. American Economic Review 105 (8), 2644–78.

Cox, J. C., J. Ingersoll, Jonathan E., and S. A. Ross (1985). A theory of the term structure of interest rates. Econometrica 53 (2), 385–407.

De Grauwe, P. (2010). Top-down versus bottom-up macroeconomics. CESifo Economic Studies 56 (4), 465–497.

De Grauwe, P. (2011). Animal spirits and monetary policy. Economic Theory 47 (2), 423–457.

De Grauwe, P. (2012). Booms and busts in economic activity: A behavioral explanation. Journal of Economic Behavior & Organization 83 (3), 484–501. The Great Recession: Motivation for re-thinking paradigms in macroeconomic modeling.

De Grauwe, P. and Y. Ji (2020). Structural reforms, animal spirits, and monetary policies. European Economic Review 124, 103395.

Deak, S., P. Levine, J. Pearlman, and B. Yang (2017). Internal rationality, learning and imperfect information. Technical report, University of Surrey.

Del Negro, M., F. Schorfheide, F. Smets, and R. Wouters (2007). On the fit of New Keynesian models. Journal of Business & Economic Statistics 25 (2), 123–143.

Dieci, R. and X.-Z. He (2018). Heterogeneous agent models in finance. In C. Hommes and B. LeBaron (Eds.), Handbook of Computational Economics, Volume 4 of Handbook of Computational Economics, Chapter 5, pp. 257–328. Elsevier.

Doucet, A., M. K. Pitt, G. Deligiannidis, and R. Kohn (2015). Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator. Biometrika 102 (2), 295–313.

Fagiolo, G., M. Guerini, F. Lamperti, A. Moneta, and A. Roventini (2019). Validation of Agent-Based Models in Economics and Finance, pp. 763–787. Springer International Publishing.

Fernandez-Villaverde, J. and J. F. Rubio-Ramırez (2007). Estimating macroeconomic models: A likelihood approach. The Review of Economic Studies 74 (4), 1059–1087.

Fernandez-Villaverde, J. and P. Guerron-Quintana (2020). Estimating DSGE models: Recent advances and future challenges. NBER Working Paper (w27715).

Fernandez-Villaverde, J., J. F. Rubio-Ramırez, and F. Schorfheide (2016). Solution and estimation methods for DSGE models. In Handbook of Macroeconomics, Volume 2, pp. 527–724. Elsevier.

Franke, R. and F. Westerhoff (2012). Structural stochastic volatility in asset pricing dynamics: Estimation and model contest. Journal of Economic Dynamics and Control 36 (8), 1193–1211.

Franke, R. and F. Westerhoff (2017). Taking stock: A rigorous modelling of animal spirits in macroeconomics. Journal of Economic Surveys 31 (5), 1152–1182.

Franke, R., T.-S. Jang, and S. Sacht (2015). Moment matching versus Bayesian estimation: Backward-looking behaviour in a New-Keynesian baseline model. The North American Journal of Economics and Finance 31, 126–154.

Gaunersdorfer, A. and C. Hommes (2007). A Nonlinear Structural Model for Volatility Clustering, pp. 265–288. Berlin, Heidelberg: Springer Berlin Heidelberg.

Goy, G., C. Hommes, and K. Mavromatis (2020). Forward guidance and the role of central bank credibility under heterogeneous beliefs. Journal of Economic Behavior & Organization.

Grazzini, J. and M. Richiardi (2015). Estimation of ergodic agent-based models by simulated minimum distance. Journal of Economic Dynamics & Control 51, 148–165.

Grazzini, J., M. G. Richiardi, and M. Tsionas (2017). Bayesian estimation of agent-based models. Journal of Economic Dynamics and Control 77, 26–47.

Guerini, M. and A. Moneta (2017). A method for agent-based models validation. Journal of Economic Dynamics and Control 82, 125–141.

Hendry, D. and G. E. Mizon (2010). On the mathematical basis of inter-temporal optimization. Economics Series Working Papers 497, University of Oxford, Department of Economics.

Herbst, E. and F. Schorfheide (2014). Sequential Monte Carlo sampling for DSGE models. Journal of Applied Econometrics 29 (7), 1073–1098.

Hommes, C. (2013). Behavioral Rationality and Heterogeneous Expectations in Complex Economic Systems. Cambridge University Press. Cambridge Books Online.

Hommes, C. H. (2006). Heterogeneous agent models in economics and finance. In L. Tesfatsion and K. Judd (Eds.), Handbook of Computational Economics, Volume 2 of Handbook of Computational Economics, Chapter 23, pp. 1109–1186. Elsevier.

Hommes, C. H. (2021). Behavioral & experimental macroeconomics and policy analysis: A complex systems approach. Journal of Economic Literature, forthcoming.

Hommes, C., D. Massaro, and M. Weber (2019). Monetary policy under behavioral expectations: Theory and experiment. European Economic Review 118, 193–212.

Jang, T.-S. (2012). Structural estimation of the New-Keynesian model: A formal test of backward-and forward-looking behavior. Emerald Group Publishing Limited.

Jang, T.-S. and S. Sacht (2016). Animal spirits and the business cycle: Empirical evidence from moment matching. Metroeconomica 67 (1), 76–113.

Jang, T.-S. and S. Sacht (2019). Forecast heuristics, consumer expectations, and New-Keynesian macroeconomics: A horse race. Journal of Economic Behavior & Organization.

Kleibergen, F. and S. Mavroeidis (2014). Identification issues in limited-information Bayesian analysis of structural macroeconomic models. Journal of Applied Econometrics 29 (7), 1183–1209.

Kristensen, D. (2009). Uniform convergence rates of kernel estimators with heterogeneous dependent data. Econometric Theory 25, 1433–1445.

Kristensen, D. and Y. Shin (2012). Estimation of dynamic models with nonparametric simulated maximum likelihood. Journal of Econometrics 167 (1), 76–94.

Kukacka, J. (2019). Simulated maximum likelihood estimation of agent-based models in economics and finance. In A. S. Chakrabarti, L. Pichl, and T. Kaizoji (Eds.), Network Theory and Agent-Based Modeling in Economics and Finance, pp. 203–226. Singapore: Springer Singapore.

Kukacka, J. and J. Barunik (2017). Estimation of financial agent-based models with simulated maximum likelihood. Journal of Economic Dynamics & Control 85, 21–45.

Kulish, M. and A. Pagan (2017). Estimation and solution of models with expectations and structural changes. Journal of Applied Econometrics 32 (2), 255–274.

Lamperti, F. (2018a, Apr). Empirical validation of simulated models through the GSL-div: An illustrative application. Journal of Economic Interaction and Coordination 13 (1), 143–171.

Lamperti, F. (2018b). An information theoretic criterion for empirical validation of simulation models. Econometrics and Statistics 5, 83–106.

Lamperti, F., A. Roventini, and A. Sani (2018). Agent-based model calibration using machine learning surrogates. Journal of Economic Dynamics and Control 90, 366–389.

Lee, D. and K. Song (2015). Simulated maximum likelihood estimation for discrete choices using transformed simulated frequencies. Journal of Econometrics 187 (1), 131–153.

Liu, C. and P. Minford (2014). Comparing behavioural and rational expectations for the US post-war economy. Economic Modelling 43, 407–415.

Lux, T. (2018). Estimation of agent-based models using sequential Monte Carlo methods. Journal of Economic Dynamics and Control 91, 391–408.

Munier, B., R. Selten, D. Bouyssou, P. Bourgine, R. Day, N. Harvey, D. Hilton, M. J. Machina, P. Parker, J. Sterman, E. Weber, B. Wernerfelt, and R. Wensley (1999, Aug). Bounded rationality modeling. Marketing Letters 10 (3), 233–248.

Sims, C. A. (1980). Macroeconomics and reality. Econometrica 48 (1), 1–48.

Stock, J. H. and M. W. Watson (1999). Forecasting inflation. Journal of Monetary Economics 44(2), 293 – 335.

Woodford, M. (2019). Monetary policy analysis when planning horizons are finite. NBER Macroeconomics Annual 33, 1–50.

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