Estimating equivalence scales and non-food needs in Egypt: Parametric and semiparametric regression modeling

This paper investigated the appropriate specifications of Engel curves for non-food expenditure categories and estimated the deprivation indices of non-food needs in rural areas using a semi parametric examination of the presence of saturation points. The study used the extended partial linear model (EPLM) and adopted two estimation methods—the double residual estimator and differencing estimator—to obtain flexible shapes across different expenditure categories and estimate equivalence scales. We drew on data of the Egyptian Household Income, Expenditure, and Consumption Survey (HIEC). Our paper provides empirical evidence that the rankings of most non-food expenditure categories is of rank three at most. Rural households showed high economies of scale in non-food consumption, with child’s needs accounting for only 10% of adult’s non-food needs. Based on semi-parametrically estimated consumption behavior, the tendency of non-food expenditure categories to saturate did not emerge. While based on parametrically estimated consumption behavior, rural areas exhibited higher deprivation indices in terms of health and education expenditure categories, which indicates the need to design specific programs economically targeting such vulnerable households.

A Typological Concept to Predict the Nitrogen Release from Organic Fertilizers in Farming Systems

The prediction of nitrogen (N) mineralization or immobilization in organic fertilizers is an important tool to optimize fertilizer use, especially in intensive agricultural systems. Our aim was to derive a model to predict the N mineralization/immobilization from readily available information on the properties of organic fertilizers in farming practice. On the basis of a literature review, a characterization of organic fertilizers was performed, revealing a large variance in fertilizer properties within the defined categories and subcategories. A partial linear model was derived and used for the prediction of N mineralization/immobilization based on the type of fertilizer and the carbon (C) to organic nitrogen (Norg) ratio. Depending on the previously defined category, a strong mineralization (e.g., plant- and animal-based commercial fertilizers) or a predominant immobilization (e.g., compost and slurries) was detected. For a total of seven main categories and their subcategories, individual models were developed. This work shows that the mineralization properties of organic fertilizers can be sufficiently predicted through a simple classification into a fertilizer category and through the C to Norg ratio.

Comparison of Some Suggested Estimators Based on Differencing Technique in the Partial Linear Model Using Simulation

In this paper new methods were presented based on technique of differences which is the difference- based modified jackknifed generalized ridge regression estimator(DMJGR) and difference-based generalized jackknifed ridge regression estimator(DGJR), in estimating the parameters of linear part of the partially linear model. As for the nonlinear part represented by the nonparametric function, it was estimated using Nadaraya Watson smoother. The partially linear model was compared using these proposed methods with other estimators based on differencing technique through the MSE comparison criterion in simulation study.

Sparse Semi-Functional Partial Linear Single-Index Regression

The variable selection problem is studied in the sparse semi-functional partial linear model, with single-index type influence of the functional covariate in the response. The penalized least squares procedure is employed for this task. Some properties of the resultant estimators are derived: the existence (and rate of convergence) of a consistent estimator for the parameters in the linear part and an oracle property for the variable selection method. Finally, a real data application illustrates the good performance of our procedure.