This paper develops a new methodology that makes use of the factor structure of large dimensional panels to understand the nature of nonstationarity in the data. We refer to it as PANIC—Panel Analysis of Nonstationarity in Idiosyncratic and Common components. PANIC can detect whether the nonstationarity in a series is pervasive, or variable-specific, or both. It can determine the number of independent stochastic trends driving the common factors. PANIC also permits valid pooling of individual statistics and thus panel tests can be constructed. A distinctive feature of PANIC is that it tests the unobserved components of the data instead of the observed series. The key to PANIC is consistent estimation of the space spanned by the unobserved common factors and the idiosyncratic errors without knowing a priori whether these are stationary or integrated processes. We provide a rigorous theory for estimation and inference and show that the tests have good finite sample properties.