  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.
