By representing a system of budget shares as an approximate factor model we determine its rank, i.e. the number of common functional forms, or factors, spanning the space of Engel curves. Once the common factors are estimated via approximate principal components, we identify them by imposing...

We study the community structure of the multi-network of commodity-specific trade relations among world countries over the 1992-2003 period. We compare structures across commodities and time by means of the normalized mutual information index (NMI). We also compare them with exogenous community...

We revisit the usefulness of long-run money demand equations for the European Central Bank. We first conduct a model evaluation exercise by means of a recent timeóvarying cointegration test. A stable relation for euro area M3 is not rejected by data only when accounting for both a speculative...

We propose a refinement of the criterion by Bai and Ng [2002] for determining the number of static factors in factor models with large datasets. It consists in multi-plying the penalty function by a constant which tunes the penalizing power of the function itself as in the Hallin and Liška...

We review, under a historical perspective, the development of the problem of nonfundamentalness of Moving Average (MA) representations of economic models. Nonfundamentalness typically arises when agents’ information space is larger than the econometrician’s one. Therefore it is impossible...

This paper explores the statistical properties of house-hold consumption-expenditure budget share distributions —defined as the share of household total expenditure spent for purchasing a specific category of commodities— for a large sample of Italian households in the period 1989-2004. We...

We propose a new method for multivariate forecasting which combines Dynamic Factor and multivariate GARCH models. The information contained in large datasets is captured by few dynamic common factors, which we assume being conditionally heteroskedastic. After presenting the model, we propose a...

We propose a new method for multivariate forecasting which combines Dynamic Factor and multivariate GARCH models. The information contained in large datasets is captured by few dynamic common factors, which we assume being conditionally heteroskedastic. After presenting the model, we propose a...