Institut für VWL  UnivIS  ERASMUS  QIS  Site Plan 

Institut für Volkswirtschaftslehre  Department of Economics 
Nummer 
201707 

Autoren 
Thomas Lux 

Titel

Estimation of AgentBased Models using Sequential Monte Carlo Methods  
Abstract 
Estimation of agentbased models is currently an intense area of research. Recent contributions have to a large extent resorted to simulationbased methods mostly using some form of simulated method of moments estimation (SMM). There is, however, an entire branch of statistical methods that should appear promising, but has to our knowledge never been applied so far to estimate agentbased models in economics and finance: Markov chain Monte Carlo methods designed for state space models or models with latent variables. This later class of models seems particularly relevant as agentbased models typically consist of some latent and some observable variables since not all the characteristics of agents would mostly be observable. Indeed, one might often not only be interested in estimating the parameters of a model, but also to infer the time development of some latent variable. However, agentbased models when interpreted as latent variable models would be typically characterized by nonlinear dynamics and nonGaussian fluctuations and, thus, would require a computational approach to statistical inference. Here we resort to Sequential Monte Carlo (SMC) estimation based on a particle filter. This approach is used here to numerically approximate the conditional densities that enter into the likelihood function of the problem. With this approximation we simultaneously obtain parameter estimates and filtered state probabilities for the unobservable variable(s) that drive(s) the dynamics of the observable time series. In our examples, the observable series will be asset returns (or prices) while the unobservable variables will be some measure of agents' aggregate sentiment. We apply SMC to two selected agentbased models of speculative dynamics with somewhat different flavor. The empirical application to a selection of financial data includes an explicit comparison of the goodnessoffit of both models. Keywords: agentbased models, estimation, Markov chain Monte Carlo, particle filter JEL classification: G12, C15, C58  
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