Simple Models and Biased Forecasts
This paper proposes a general framework in which agents are constrained to use simple time-series models to forecast economic variables and characterizes the resulting bias in the agents’ forecasts. It considers agents who can only entertain state-space models with no more than d states, where d measures the agents’ cognitive abilities. Agents’ models are otherwise unrestricted a priori and disciplined endogenously by maximizing the fit to the true process. When the true process does not have a d-state representation, agents end up with misspecified models and biased forecasts. If the true process satisfies an ergodicity assumption, the bias manifests itself as persistence bias: a tendency to attend to the most persistent observables at the expense of less persistent ones. The bias causes agents’ foreword-looking decisions to mimic the dynamics of backward-looking, persistent variables in the economy. It also dampens the response of agents’ actions to shocks and leads to additional co-movements between various choices. The paper then proceeds to study the implications of the theory in the context of three calibrated workhorse macro models: the new-Keynesian, real business cycle, and Diamond–Mortensen–Pissarides models. In each case, constraining agents to use simple models brings the model’s predictions more in line with the data, without adding any parameters other than the integer d.
Molavi, Pooya. 2022. Simple Models and Biased Forecasts.LINK