Giancarlo Bruno

Fifty Years of Business Confidence Surveys on Manufacturing Sector

In this work the evolution of the Italian Business Confidence Survey on manufacturing sector is presented starting from the preliminary European project for harmonized statistics launched in the late fifties of the last century. Survey changes are described, focusing in particular on the so-called confidence indicator. The continuing increase of statistical accuracy in sampling is recalled, from the initial purposive sample and controls, up to the present state of the art. Specific attention is devoted to the role of administrative archives in the sampling plan. Emphasis is also given to the increasing use of computer simulation in assessing the validity of the estimates. The role of cyclical analysis is finally highlighted with regard to two aspects: (1) the business confidence has not a corresponding variable in the economic system—the validation can only be performed in comparison with correlated variables (e.g. IP, GDP); (2) confidence shows forecasting capability for the economic system.

The cross-country effects of EU holidays on domestic GDP's

The number and the distribution of non-working days during the year has recently entered the policy debate related to the slow pace of the European economy.The fact that the number of non-working days can affect the quarter to quarter performance of GDP is well known and hardly disputable. It has recently been argued that not only domestic holidays can in principle be important in each single economy, but also foreign ones, as far as there exist strict connections among the national economies. Given the existing evidence at the national level relative to the influence of calendar effects on GDP, the first step of the econometric analysis in the present research is a check on the existence (and significance) of international spillover effects. Our investigation uses both structural time series models and the ARIMA model-based approach. These two different approaches are used jointly and their specific features are exploited to represent and estimate the time series components of our interest. The empirical evidence does not support the spillover hypothesis.