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For every municipality I c i with no housing insured (it happens for some small municipalities), we replace HT ht with HT ht , computed over all the HT ht insured housing units of the province I c i SB I P i sb to which municipality I c i belongs. For municipality I c i , if I N i SB I c i sb is the total number of housing units, an estimate I v i SB I c i sb of the housing stock's value is: HT ht Graph where I k i is a scaling factor to obtain HT ht billion euros. [Extracted from the article]

We examine two sources of bias for the Bank of Italy's panel business survey of Industrial and Services Firms: 1) the bias caused by panel attrition; and 2) the bias created by delays in the distributional data on the reference population, needed for computing the survey weights. As for the first source of bias, the estimates strongly dependent on big firms' values are less affected by panel attrition than those representing firms' average behavior, independent of their sizes. Positive economic results make it easier to enroll new firms in the survey, in order to replace firms dropping out because of bad economic performances. However, the economic results of new entrances become more aligned to those of the population, once they enter the sample. A very different result emerges for the second source of bias, since, when the population size is highly variable, the information delays produce a bias for the estimates influenced by the contribution of great firms, but the effect is negligible for the estimates not dependent on firm size. [ABSTRACT FROM AUTHOR]

This paper presents an exploratory agent-based model of a real time gross settlement (RTGS) payment system. Banks are represented as agents who exchange payment requests, which are then settled according to a set of simple rules. The model features the main elements of a real-life system, including a central bank acting as liquidity provider, and a simplified money market. A simulation exercise using synthetic data of BI-REL (the Italian RTGS) predicts the macroscopic impact of a disruptive event on the flow of interbank payments. In our reduced-scale system, three hypothetical distinct phases emerge after the disruptive event: 1) a liquidity sink effect is generated and the participants' liquidity expectations turn out to be excessive; 2) an illusory thickening of the money market follows, along with increased payment delays; and, finally 3) defaulted obligations dramatically rise. The banks cannot staunch the losses accruing on defaults, even after they become fully aware of the critical event, and a scenario emerges in which it might be necessary for the central bank to step in as liquidity provider. [ABSTRACT FROM AUTHOR]