Unemployment rate remains persistently high after recessions even after job losses subside. Standard search and matching models have difficulty capturing this pattern. In this paper, I argue that noise shocks, which capture agents’ expectational errors due to the noise in received signals about the persistence of aggregate productivity, can generate substantial persistence in the unemployment rate. I first identify these noise shocks using a novel structural VAR and find that unemployment would have recovered to its pre-recession level 7 quarters earlier in the absence of noise shocks in the 1968-2019 period. I then set-up a general equilibrium search and matching model with on-the-job search, endogenous search effort and wage rigidity and consider three shocks: a permanent productivity shock; a transitory productivity shock and a noise shock. The model calibrated to target standard moments and disciplined to match impulse responses identified through SVAR does substantially better compared to a model without imperfect information and noise shocks. It generates 23 percent more volatility in unemployment and vacancies and 6 quarters longer recoveries in unemployment, mainly through two channels. First, responses to productivity shocks become more persistent as it takes time for agents to learn whether a shock is persistent or not. Second, noise shocks provide an additional source of persistence source of persistence, which are amplified through on-the-job search and firms' vacancy posting decisions.