Can we predict fine wine and alcohol prices? Yes, but it depends on the forecasting horizon. We make this point by considering the Liv-ex Fine Wine 100 and 50 Indices, the retail and wholesale alcohol prices in the United States for the period going from January 1992 to March 2022. We use rich and diverse datasets of economic, survey, and financial variables as potential price drivers and adopt several combination/dimension reduction techniques to extract the most relevant determinants. We build a comprehensive set of models and compare forecast performances across different selling levels and alcohol categories. We show that it is possible to predict fine wine prices for the 2-year horizon and retail/wholesale alcohol prices at horizons ranging from 1 month to 2 years. Our findings stress the importance of including consumer survey data and macroeconomic factors, such as international economic factors and developed markets equity risk factors, to enhance the precision of predictions of retail/wholesale (fine wine) prices.

A careful measure of fitness represents a crucial target in crop pest management and becomes fundamental considering extremely prolific insects. In the present paper, we describe a standardized rearing protocol and a bioinformatics tool to calculate aphid fitness indices and invasiveness starting from life table data. We tested the protocol and the bioinformatic tool using six Myzus persicae (Sulzer) asexual lineages in order to investigate if karyotype rearrangements and ecotype could influence their reproductive performances. The tool showed that different karyotypes do not influence adaptive success and put in evidence a marked invasive potential of the M. persicae lineage 64. The presence of a similar fitness rate of 33H and 7GK asexual lineages (both possessing intra-individual karyotype variations) in respect to the asexual lineage 1 (with a standard karyotype) represents an important demonstration of the potentiality of holocentric chromosomes to reduce the effects of chromosome rearrangements.

Most of the methods of mortality forecasting have been assessed using performance on overall mortality, and few studies address the issue of identifying the appropriate forecasting models for specific causes of deaths. This study analyses trends and forecasts mortality rates for three major causes of death — lung cancer, influenza-pneumonia-bronchitis, and motor vehicle accidents — using Lee–Carter, Booth–Maindonald–Smith, Age-Period-Cohort, and Bayesian models, to assess how far different causes of death need different forecasting methods. Using data from the Twentieth and Twenty-First Century Mortality databases for England and Wales, results show major differences among the different forecasting techniques. In particular, when linearity is the main driver of past trends, Lee–Carter-based approaches are preferred due to their straightforward assumptions and limited need for subjective judgment. When a clear cohort pattern is detectable, such as with lung cancer, the Age-Period-Cohort model shows the best outcome. When complete and reliable historical trends are available the Bayesian model does not produce better results than the other models.