A Multilevel Model with Time Series Components for the Analysis of Tribal Art Prices

In the present work we perform an econometric analysis of the Tribal art market. To this aim, we use a unique and original database that includes information on Tribal art market auctions worldwide from 1998 to 2011. In Literature, art prices are modelled through the hedonic regression model, a classic fixed-effect model. The main drawback of the hedonic approach is the large number of parameters, since, in general, art data include many categorical variables. In this work, we propose a multilevel model for the analysis of Tribal art prices that takes into account the influence of time on artwork prices. In fact, it is natural to assume that time exerts an influence over the price dynamics in various ways. Nevertheless, since the set of objects change at every auction date, we do not have repeated measurements of the same items over time. Hence, the dataset does not constitute a proper panel; rather, it has a two-level structure in that items, level-1 units, are grouped in time points, level-2 units. The main theoretical contribution is the extension of classical multilevel models to cope with the case described above. In particular, we introduce a model with time dependent random effects at the second level. We propose a novel specification of the model, derive the maximum likelihood estimators and implement them through the E-M algorithm. We test the finite sample properties of the estimators and the validity of the own-written R-code by means of a simulation study. Finally, we show that the new model improves considerably the fit of the Tribal art data with respect to both the hedonic regression model and the classic multilevel model.

New catalytic processes for the upgrading of furfural and 5-hydroxymethylfurfural to chemicals and fuels

The demand of energy, fuels and chemicals is increasing due to the strong growth of some countries in the developing world and the development of the world economy. Unfortunately, the general picture derived sparked an exponential increase in crude oil prices with a consequent increase of the chemical, by-products and energy, depleting the global market. Nowadays biomass are the most promising alternative to fossil fuels for the production of chemicals and fuels. In this work, the development of three different catalytic processes for the valorization of biomass-derived has been investigated. 5-hydroxymethylfurfural oxidation was studied under mild reaction condition using gold and gold/copper based catalysts synthetized from pre-formed nanoparticles and supported onto TiO2 and CeO2. The analysis conducted on catalysts showed the formation of alloys gold/copper and a strong synergistic effect between the two metals. For this reason the bimetallic catalysts supported on titania showed a higher catalytic activity respect to the monometallic catalysts. The process for the production of 2,5-bishydroxymethyl furan (BHMF) was also optimized by means the 5-hydroxymethylfurfural hydrogenation using the Shvo complex. Complete conversion of HMF was achieved working at 90 °C and 10 bar of hydrogen. The complex was found to be re-usable for at least three catalytic cycles without suffering any type of deactivation. Finally, the hydrogenation of furfural and HMF was carried out, developing the process of hydrogen transfer by using MgO as a catalyst and methanol as a hydrogen donor. Quantitative yields to alcohols have been achieved in a few hours working in mild condition: 160 °C and at autogenous pressure. The only by-products formed were light products such as CO, CO2 and CH4 (products derived from methanol transformation), easily separable from the reaction solution depressurizing the reactor.

A new global wheat marketmodel (GLOWMM) for the analysis of wheat export prices

Food commodity prices fluctuations have important impacts on poverty and food insecurity across the world. Conventional models have not provided a complete picture of recent price spikes in agricultural commodity markets, while there is an urgent need for appropriate policy responses. Perhaps new approaches are needed in order to better understand international spill-overs, the feedback between the real and the financial sectors and also the link between food and energy prices. In this paper, we present results from a new worldwide dynamic model that provides short and long-run impulse responses of wheat international prices to various real shocks.