Suchergebnisse

In this paper, we discuss the most recent developments in temporal disaggregation techniques carried out at the Istituto Nazionale di Statistica (ISTAT). They concern the extension from static to dynamic autoregressive distributed lag regressions and the adoption of the state‐space framework for the statistical treatment of temporal disaggregation. Beyond the development of a unified procedure for both static and dynamic methods from one side and the treatment of the logarithmic transformation from the other, we provide short guidelines for model selection. The inclusion of stochastic trends in the regressions is also discussed. We evaluate the new dynamic methods by implementing a large‐scale exercise using the ISTAT annual value added data jointly with quarterly industrial production over the 1995–2013 period. The main finding is that autoregressive distributed lag disaggregations reduce forecast errors in comparison to static variants, at a price of lower correlations with related high‐frequency indicators. Moreover, problematic outcomes are limited to few cases.

This article analyzes wage dispersion in a sample of Italian firms, by taking advantage of a unique Linked Employer-Employee dataset (LEED) merging four data sources from the National Institute of Statistics. An in-depth descriptive analysis conveys that knowledge-intensive services record the highest within- and between-firms wage dispersion in the sample. Regression-based results show that innovation does not drive up inequality in large companies. However, it can contribute to enlarge the within-firm wage dispersion as well as the wage gap across small firms.