Core Facility Hohenheim

Permanent URI for this collectionhttps://hohpublica.uni-hohenheim.de/handle/123456789/16626

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Browsing Core Facility Hohenheim by Classification "330"

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    Household food waste quantification and cross-examining the official figures: A study on household wheat bread waste in Shiraz, Iran
    (2022) Ghaziani, Shahin; Ghodsi, Delaram; Schweikert, Karsten; Dehbozorgi, Gholamreza; Faghih, Shiva; Mohabati, Shabnam; Doluschitz, Reiner
    The global consumer food waste (FW) estimates are mainly based on modeling data obtained from governments. However, a major data gap exists in FW at the household level, especially in developing countries. Meanwhile, the reliability of the existing data is questionable. This study aimed to quantify wheat bread waste (HBW) in Shiraz, Iran, and cross-examine the governmental HBW data. Face-to-face waste recall questionnaire interviews were conducted in 419 households from December 2018 to August 2019. A multistage sampling strategy consisting of stratification, clustering, and systematic sampling was employed. Moreover, we carried out a comprehensive document review to extract and analyze the official HBW data. The results revealed that the HBW in Shiraz is 1.80%—the waste amounts for traditional bread and non-traditional bread were 1.70% and 2.50%, respectively. The survey results were compared with the previous official data, revealing a substantial contradiction with the 30% HBW reported between 1991 and 2015. Possible reasons for this disparity are explored in this paper. Although our results cannot be generalized to other food commodities and locations, our findings suggest that considering the substantial likelihood of bias in the official data, policymakers should conduct more FW measurements and re-evaluate the accuracy of the existing data.
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    Predictor preselection for mixed‐frequency dynamic factor models: a simulation study with an empirical application to GDP nowcasting
    (2025) Franjic, Domenic; Schweikert, Karsten; Franjic, Domenic; Core Facility Hohenheim and Institute of Economics, University of Hohenheim, Stuttgart, Germany; Schweikert, Karsten; Core Facility Hohenheim and Institute of Economics, University of Hohenheim, Stuttgart, Germany
    We investigate the performance of dynamic factor model nowcasting with preselected predictors in a mixed‐frequency setting. The predictors are selected via the elastic net as it is common in the targeted predictor literature. A simulation study and an application to empirical data are used to evaluate different strategies for variable selection, the influence of tuning parameters, and to determine the optimal way to handle mixed‐frequency data. We propose a novel cross‐validation approach that connects the preselection and nowcasting step. In general, we find that preselecting provides more accurate nowcasts compared with the benchmark dynamic factor model using all variables. Our newly proposed cross‐validation method outperforms the other specifications in most cases.