Microconsistency in Simple Empirical Agent-Based Financial Models

Blake LeBaron ()
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Blake LeBaron: Brandeis University

Computational Economics, 2021, vol. 58, issue 1, No 5, 83-101

Abstract: Abstract Models with small numbers of agents have recently been simplified for direct empirical estimation. Parameters are estimated at the macro level to get a best fit to the data. However, little analysis is done at the micro level to examine the choices made by agents for forecasting rules. This paper explores one of these recent models from the standpoint of micro agent behavior. It is shown that at the fitted forecasting rules, agents would prefer deviating to other nearby rules. The simple two type model is then compared with several multi-type models allowing for agents to use a broader set of rules. This can impact the dynamics of the generated time series, but it also may not if one takes the parameter estimates of the original model as an exogenous restriction on a reasonable support for the forecasting rules. This result emphasizes that these models may be imposing some hidden micro assumptions about agent behavior.

Keywords: Learning; Heterogeneous agent models; Asset pricing; Financial time series; Adaptive behavior (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10614-019-09917-8

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