This paper develops a theory of expectations-driven business cycles based on learning. Agents have incomplete knowledge about how market prices are determined and shifts in expectations of future prices affect dynamics. Learning breaks the tight link between fundamentals and equilibrium prices, inducing periods of erroneous optimism or pessimism about future returns to capital and wages which subsequent data partially validate. In a real business cycle model, the theoretical framework amplifies and propagates technology shocks. Moreover, it produces agents forecast errors consistent with business cycle properties of forecast errors for a wide range of variables from the Survey of Professional Forecasters.