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...

This paper considers the problem of testing cross-sectional correlation in large panel data models with serially-correlated errors. It finds that existing tests for cross-sectional correlation encounter size distortions with serial correlation in the errors. To control the size, this paper...

COVID-19 has affected almost all sectors of the economy, including the real estate markets across different countries in the world. A rich body of literature has emerged in analyzing real estate market trends and revealing important information. However, few studies have used a spatial...

We investigate the impact of differences in privatisation method on national economic performance in transition economies. Our approach is to estimate, using dynamic panel data methods, a growth equation over 23 countries for the period 1990-2001. Among our results, we find that mass...

This paper analyses the informational role of the trading activity when jumps occur in the US Treasury market. As jumps mark the arrival of new information to the market, we explore the contribution of jumps in reducing the informational asymmetry. We identify jumps using a combination of jump...

Dynamic panel data methods are used to estimate a growth model using data from 23 transition countries for the period 1990 to 2003. The estimating equation is augmented with country- and time-specific variables for methods of privatization and other factors potentially relevant to growth in...