We study a Large-Dimensional Non-Stationary Dynamic Factor Model where (1) the factors Ft are I (1) and singular, that is Ft has dimension r and is driven by q dynamic shocks with q less than r, (2) the idiosyncratic components are either I (0) or I (1). Under these assumption the factors Ft are...

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...

We estimate the oil price pass-through into consumer prices both in the US and in the euro area. In particular, we disentangle the specific effect that an oil price change might have on each disaggregate price, from the effect on all prices that an oil price change might have since it affects...

We use a dynamic factor model to disentangle changes in prices due to economy-wide (common) shocks, from changes in prices due to idiosyncratic shocks. Using 146 disaggregated individual price series from the U.S. PCE price index, we find that most of the fluctuations in core PCE prices observed...

This paper decomposes consumer price inflation into pure inflation, relative price inflation, and idiosyncratic inflation by estimating a dynamic factor model á la Reis and Watson (2010) on a data set of 146 monthly disaggregated prices from 1995 to 2019. We find that pure inflation is the...

We produce predictions of the current state of the Indonesian economy by estimating a dynamic factor model on a dataset of eleven indicators (also followed closely by market operators) over the time period 2002 to 2014. Besides the standard difficulties associated with constructing timely...