Extreme weather events and high Colombian food prices: A non-stationary extreme value approach

Given the importance of climate change and the increase of its severity under extreme weather events, we analyze the main drivers of high food prices in Colombia between 1985 and 2020 focusing on extreme weather shocks like a strong El Ni˜no.We estimate a non-stationary extreme value model for Colombian food prices. Our findings suggest that perishable foods are more exposed to extreme weather conditions in comparison to processed foods. In fact, an extremely low precipitation level explains only high prices in perishable foods. The risk of high perishable food prices is significantly larger for low rainfall levels (dry seasons) compared to high precipitation levels (rainy seasons). This risk gradually results in higher perishable food prices. It is non linear and is also significantly larger than the risk related to changes in the US dollar-Colombian peso exchange rate and fuel prices. Those covariates also explain high prices for both perishable and processed foods. Finally, we find that the events associated with the strongest El Ni˜no in 1988 and 2016 are expected to reoccur once every 50 years.

A New Extreme Value Model with Different Copula, Statistical Properties and Applications

In this article, we defined and studied a new distribution for modeling extreme value. Some of its mathematical properties are derived and analyzed. Simple types copula is employed for proposing many bivariate and multivariate type extensions. Method of the maximum likelihood estimation is employed to estimate the model parameters. Graphically, we perform the simulation experiments to assess of the finite sample behavior of the maximum likelihood estimations. Three applications are presented for measuring the flexibility of the new model is illustrated using three real data applications.

Extreme rainfall events in the Northeastern USA become more frequent with rising temperatures, but their intensity distribution remains stable

Both intensities of individual extreme rainfall events and the frequency of such events are important for infrastructure planning. We develop a new statistical extreme value model, the PGEV model, which makes it possible to use high quality annual maximum series data instead of lesswell checked daily data to estimate trends in intensity and frequency separately. The method is applied to annual maxima data from the NOAA Atlas 14, Volume 10, dating from approximately 1900 to 2014, showing that in the majority of 333 rain gauge stations in the Northeastern USA the frequency of extreme rainfall events increases as mean temperature increases, but that there is little evidence of trends in the distribution of the intensities of individual extreme rainfall events. The median of the frequency trends corresponds to extreme rainfalls becoming 83% more frequent for each centigrade degree of temperature increase. Naturally, increasing trends in frequency also increase the yearly or 10-yearly risks of very extreme rainfall events. Three other large areas in the contiguous USA, the Midwest, the Southeast, and Texas, are also studied, and show similar but weaker trends than those in the Northeast.

Exploring Multiple‐discreteness in Freight Transport. A Multiple Discrete Extreme Value Model Application for Grain Consolidators in Argentina

There are some examples where freight choices may be of a multiple discrete nature, especially the ones at more tactical levels of planning. Nevertheless, this has not been investigated in the literature, although several discrete-continuous models for mode/vehicle type and shipment size choice have been developed in freight transport. In this work, we propose that the decision of port and mode of the grain consolidators in Argentina is of a discrete-continuous nature, where they can choose more than one alternative and how much of their production to send by each mode. The Multiple Discrete Extreme Value Model (MDCEV) framework was applied to a stated preference data set with a response variable that allowed this multiple-discreteness. To our knowledge, this is the only application of the MDCEV in regional freight context. Free alongside ship price, freight transport cost, lead-time and travel time were included in the utility function and observed and random heterogeneity was captured by the interaction with the consolidator’s characteristics and random coefficients. In addition, different discrete choice models were used to compare the forecasting performance, willingness to pay measures and structure of the utility function against.

Towards a reproducible snow load map – an example for Austria

<p>The European Committee for Standardization provides coarse rules for the estimation of snow load maps for structural design. European countries can apply their own methodologies, resulting in inconsistencies for the 50-year return level of snow load at national borders. Commonly used approaches base on more or less sophisticated interpolation of snow depths with a subsequent assignment of snow density, or spatial extreme value interpolation of snow load measurements.  </p><p>We propose a novel methodology for Austria, where snow load observations are not available. It is based on (1) modeling yearly snow load maxima with the specially developed ∆SNOW model, and (2) a generalized additive model, where explaining covariates and their combinations are represented by penalized regression splines, fitted to such derived snow load series. Results show an RMSE of 0.7 kN/m<sup>2</sup>, and a BIAS of -0.2 kN/m<sup>2</sup> over all altitudes, thereby outperforming a smooth spatial extreme value model and the actual Austrian standard, when compared to locally estimated, “quasi-observed “ 50-year snow load maxima at 870 stations in and tightly around Austria.</p><p>The new approach requires no zoning and provides a reproducible and transparent approach. Due to the relatively ease of use and snow depth measurements as single prerequisite, the method is applicable in other countries as well. Negative BIASes, that significantly underestimate 50-year snow loads at a small number of stations, are the only objective problem that has to be solved before the new map can be proposed as a successor of the actual Austrian snow load map.</p>

A escolha de não ser discreto: discutindo a utilização de modelos discretos contínuos no transporte de carga

O transporte de carga tem utilizado os modelos herdados do transporte de passageiros e com eles todos seus pressupostos tradicionais. Mas são todos eles válidos? O presente artigo visa discutir sobre um dos pressupostos menos contestados neste processo de modelagem: o da exclusividade mútua das alternativas no contexto do transporte de carga. Para isso, este artigo apresenta uma aplicação de Multiple Discrete Extreme Value Model (MDCEV) para a escolha de modo e porto para os consolidadores de grãos na Argentina. O modelo é desenvolvido a partir de uma pesquisa de Preferência Declarada que permitia a escolha de mais de uma alternativa simultaneamente. A escolha é descrita pelo Tempo de Viagem, Tempo de Espera do Serviço, Preço de venda no porto e Custo do Frete. O MDCEV permitiu obter informação sobre o efeito da saciedade das diferentes alternativas. De esta maneira, o MDCEV pode ser uma ferramenta valiosa para a modelagem de escolhas táticas e estratégicas.