This paper investigates whether agents can learn how to form rational expectations using standard econometric techniques in the case of a linear stochastic supply and demand model with a production lag. This model has a unique rational expectations equilibrium in which the expected price is a linear function of an observable exogenous random variable. Outside of rational expectations equilibrium agents predict the price by using a regression of past prices on the exogenous random variable where the regression is estimated by either ordinary least squares or Bayesian methods. If the agents are Bayesians, they may have diverse prior beliefs on the mean of the estimated parameter, but all have the same precision. This estimation procedure would be appropriate for an outside observer estimating the parameters of the model in rational expectations equilibrium the coefficient of the equation relating the mathematical conditional expectation of the price to the exogenous variable is constant through time. Outside rational expectations equilibrium this coefficient, which changes each time new data change the regression coefficient. The data are generated by a time-varying parameter model where the varying parameter is determined by past data and the estimation procedure. Agents fail to take this feedback into account and so are estimating a misspecific model.