Libero Monteforte

The economic consequences of euro-area macro-modelling shortcuts

Should euro-area economies be modelled in an aggregate (area-wide) fashion or in a disaggregate (multi-country) one? This article tackles that question from both statistical and economic viewpoint. From a statistical viewpoint, aggregation bias criteria are found to signal that the degree of structural heterogeneity among euro-area economies is such that the loss of information entailed by an aggregate modelling approach may be far from trifling. From an economic viewpoint, we investigate the following issue. Are those statistically detectable heterogeneities of any practical relevance when it comes to supporting monetary policy decision-making? To provide an answer to this question, we compute simple optimal monetary policy reaction functions on the basis of either an aggregate model or a disaggregate one, and compare the associated welfare losses. The results suggest that the welfare under-performance of an area-wide-model-based rule is not only nonnegligible, but also robust with respect to a number of sensitivity analyses.

Real Time Forecasts of Inflation: The Role of Financial Variables

We present a mixed-frequency model for daily forecasts of euro area inflation. The model combines a monthly index of core inflation with daily data from financial markets; estimates are carried out with the MIDAS regression approach. The forecasting ability of the model in real-time is compared with that of standard VARs and of daily quotes of economic derivatives on euro area inflation. We find that the inclusion of daily variables helps to reduce forecast errors with respect to models that consider only monthly variables. The mixed-frequency model also displays superior predictive performance with respect to forecasts solely based on economic derivatives.

The Bank of Italy's quarterly model

This book provides a description of the main macroeconomic models used by the European Central Bank and the euro area national central banks (Eurosystem). These models are used to help prepare economic projections and scenario analysis for individual countries and the euro area as a whole.

FaMIDAS: A Mixed Frequency Factor Model with MIDAS Structure

In this paper a dynamic factor model with mixed frequency is proposed (FaMIDAS), where the past observations of high frequency indicators are used following the MIDAS approach. This structure is able to represent with richer dynamics the information content of the economic indicators and produces smoothed factors and forecasts. In addition, the Kalman filter is applied, which is particularly suited for dealing with unbalanced data set and revisions in the preliminary data. In the empirical application for the Italian quarterly GDP the short-term forecasting performance is evaluated against other mixed frequency models in a pseudo-real time experiment, also allowing for pooled forecast from factor models.

EMU sovereign spreads and macroeconomic news

We investigate the relationship between macroeconomic news and sovereign spreads in the euro area at weekly frequency. Our focus lies in the role played by macroeconomic announcements. To this aim we augment a standard GARCH model with a synthetic measure for macroeconomic surprises obtained by aggregating deviations between data releases and market expectations on a set of indicators chosen for being closely watched by economic analysts and financial operators. We find that the dissemination of macroeconomic data on the US economy affects the level of sovereign spreads, i.e. the better the news the lower the spreads. Moreover, the dissemination of bad news on the euro area economy affects negatively the volatility, i.e. the worse the news the higher the volatility.

Heterogeneous Fall in Productive Capacity in Italian Industry During the 2008-13 Double-Dip Recession

Between 2008 and 2013 productive capacity was considerably downsized in the Italian manufacturing sector. This paper analyses the micro-data collected for the Bank of Italy surveys to identify the main drivers of the reduction in the whole 2008-13 period and in four sub-periods (pre-crisis 2001-07, first phase of the crisis 2008-09, recovery 2010-11, and second crisis 2012-13). Our main findings are that i) losses of productive capacity varied widely across manufacturing sub-sectors with differences in pre-crisis trends tending to persist in a few sub-sectors during the double-dip recession; ii) large firms were more successful in avoiding major capacity losses, especially in the first phase of the crisis; iii) the share of sales on foreign markets was negatively correlated with performance in 2008-09, but the correlation turned positive in 2012-13; iv) among the Italian macro-regions, the Centre weathered the long recession better; v) subsidiaries underperformed firms not belonging to any group; and vi) the negative effects on productive capacity of credit constraints, which discouraged investments, were felt by Italian firms particularly in 2012-13.

Using the Payment System Data to Forecast the Italian GDP

Payment systems track economic transactions and therefore could be considered important indicators of economic activity. This paper describes the available monthly data on the retail settlement system for Italy and selects some of them for short-term forecasting. Using a mixed frequency factor model to predict Italian GDP, we find that payment system flows stand out when compared to other standard business cycle indicators.