This paper studies the asymptotic validity of sieve bootstrap for nonstationary panel factor series. Two main results are shown. Firstly, a bootstrap Invariance Principle is derived pointwise in i, obtaining an upper bound for the order of truncation of the AR polynomial that depends on n and...

Castagnetti, Rossi and Trapani (2014) propose two max-type statistics to test for the presence of a factor structure in a large stationary panel data model. We investigate the use of alternative approaches as average-type and Hausman-type statistics. We show that both approaches can not be used....

This paper considers measures of uncertainty used in economic estimation. Our first contribution is to address the theoretical relationship between cross-section and time series measures, highlighting the reasons why these might diverge. In a subsequent empirical section, we compare measures of...

This paper develops an estimation and testing framework for a stationary large panel model with observable regressors and unobservable common factors. We allow for slope heterogeneity and for correlation between the common factors and the regressors. We propose a two stage estimation procedure...

The asymptotic t-test for the long-run average in a heterogeneous nonstationary panel model is derived. The asymptotics of the Least Squares Dummy Variable (LSDV) and of the Pooled-OLS (POLS) estimators for the slope parameter are studied under various circumstances (serial correlation, strong...

This paper considers bootstrapping nonstationary panel factor mod- els when possible time dependence is present in the factors dynamics. The analysis does not assume any speci.c DGP, and a sieve bootstrap algorithm is proposed to approximate the autocorrelation structure of the processes...

We complement existing inferential theory for panel factor models by deriving the asymptotics for the first differences of the estimated factors and common components obtained from a non-stationary panel factor model. As an application, we propose an estimator for the long run variance of the...

We propose a new approach to detect and quantify informal employment resulting from irregular migration shocks. Focusing on a largely informal sector, agriculture, and on the exogenous variation from the Arab Spring wave on southern Italian coasts, we use machine-learning techniques to document...