YEAR

2024

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Econometrics , Applied Research

Predicting Full Retirement Attainment of NBA Players

The aim of this analysis is to predict whether an NBA player will be active in the league for at least 10 years so as to be qualified for NBA’s full retirement scheme which allows for the maximum benefit payable by law. We collected per game statistics for players during their second year, drafted during the years 1999 up to 2006, for which information on their career longetivity is known. By feeding these statistics of the sophomore players into statistical and machine learning algorithms we select the important statistics and manage to accomplish a satisfactory predictability performance. Further, we visualize the effect of each of the selected statistics on the estimated probability of staying in the league for more than 10 years

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Statistics , Theory

Constrained Least Squares Simplicial-Simplicial Regression

Simplicial-simplicial regression refers to the regression setting where both the responses and predictor variables lie within the simplex space, i.e. they are compositional. For this setting, constrained least squares, where the regression coefficients themselves lie within the simplex, is proposed. The model is transformation-free but the adoption of a power transformation is straightforward, it can treat more than one compositional datasets as predictors and offers the possibility of weights among the simplicial predictors. Among the model’s advantages are its ability to treat zeros in a natural way and a highly computationally efficient algorithm to estimate its coefficients. Resampling based hypothesis testing procedures are employed regarding inference, such as linear independence, and equality of the regression coefficients to some pre-specified values. The performance of the proposed technique and its comparison to an existing methodology that is of the same spirit takes place u

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Agricultural Economics , Applied Research

Using Synthetic Farm Data to Estimate Individual Nitrate Leaching Levels

he paper develops a novel synthetic population generation scheme to deal with the NPS pollution problem of nitrate leaching from agricultural farms. The scheme relies upon estimation of the joint distribution of the variables using Bayesian network learning which, coupled with the use of non-parametric regression models facilitate the generation of realistic synthetic populations. Then building upon the sequential GME model suggested by Kaplan et al., (2003) in line with the multiple production relations model suggested by Murty et al., (2012) we obtain econometric estimates of both the production technology and nature's residual generating mechanism for the synthetic population of farms. These estimates are used to proxy a reliable optimal taxation scheme that corresponds to local environmental and economic conditions. The methodology is applied to the Greek FADN dataset for the Greek NUTS II region of Thessaly during the 2017-18 cropping year.

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