Simple Models and Biased Forecasts

This paper proposes a framework in which agents are constrained to use simple models to forecast economic variables and characterizes the resulting biases. It considers agents who can only entertain state-space models with no more than d states, where d measures the intertemporal complexity of a model. Agents are boundedly rational in that they can only consider models that are too simple to nest the true process, yet they use the best model among those considered. I show that using simple models adds persistence to forward-looking decisions and increases the comovement among them. I then explain how this insight can bring the predictions of three workhorse macroeconomic models closer to data. In the new-Keynesian model, forward guidance becomes less powerful. In the real business cycle model, consumption responds more sluggishly to productivity shocks. The Diamond–Mortensen–Pissarides model exhibits more internal propagation and more realistic comovement in response to productivity and separation shocks.