The paper studies Non-Stationary Dynamic Factor Models such that: (1) the factors Ft are I(1) and singular, i.e. Ft has dimension r and is driven by a q-dimensional white noise, the common shocks, with q r, and (2) the idiosyncratic components are I(1). We show that Ft is driven by r-c...

The paper studies Non-Stationary Dynamic Factor Models such that: (1) the factors F_t are I(1) and singular, i.e. F_t has dimension r and is driven by a q-dimensional white noise, the common shocks, with q r, and (2) the idiosyncratic components are I(1). We show that F_t is driven by r − c...

The paper studies Non-Stationary Dynamic Factor Models such that: (1) the factors Ft are I(1) and singular, i.e. Ft has dimension r and is driven by a q-dimensional white noise, the common shocks, with q r, and (2) the idiosyncratic components are I(1). We show that Ft is driven by r-c...

This paper considers a non-stationary dynamic factor model for large datasets to disentangle long-run from short-run co-movements. We first propose a new Quasi Maximum Likelihood estimator of the model based on the Kalman Smoother and the Expectation Maximisation algorithm. The asymptotic...