Antonio A. Golpe

Pedro Cadahia Delgado, Emilio Congregado, Antonio A. Golpe et al.

Most representative decision tree ensemble methods have been used to examine the variable importance of Treasury term spreads to predict US economic recessions with a balance of generating rules for US economic recession detection. A strategy is proposed for training the classifiers with Treasury term spreads data and the results are compared in order to select the best model for interpretability. We also discuss the use of SHapley Additive exPlanations (SHAP) framework to understand US recession forecasts by analyzing feature importance. Consistently with the existing literature we find the most relevant Treasury term spreads for predicting US economic recession and a methodology for detecting relevant rules for economic recession detection. In this case, the most relevant term spread found is 3 month to 6 month, which is proposed to be monitored by economic authorities. Finally, the methodology detected rules with high lift on predicting economic recession that can be used by these entities for this propose. This latter result stands in contrast to a growing body of literature demonstrating that machine learning methods are useful for interpretation comparing many alternative algorithms and we discuss the interpretation for our result and propose further research lines aligned with this work.

Pedro Cadahia, Antonio A. Golpe, Juan M. Martín Álvarez et al.

There is literature that questions the veracity of the studies commissioned by the transnational tobacco companies (TTC) to measure the illicit tobacco trade. Furthermore, there are studies that indicate that the Empty Pack Surveys (EPS) ordered by the TTCs are oversized. The novelty of this study is that, in addition to detecting the anomalies analyzed in the EPSs, there are provinces in which cigarette sales are higher than reasonable values, something that the TTCs ignore. This study analyzed simultaneously, firstly, if the EPSs established in each of the 47 Spanish provinces were fulfilled. Second, anomalies observed in provinces where sales exceed expected values are measured. To achieve the objective of the paper, provincial data on cigarette sales, price and GDP per capita are used. These data are modeled with machine learning techniques widely used to detect anomalies in other areas. The results reveal that the provinces in which sales below reasonable values are observed (as detected by the EPSs) present a clear geographical pattern. Furthermore, the values provided by the EPSs in Spain, as indicated in the previous literature, are slightly oversized. Finally, there are regions bordering other countries or with a high tourist influence in which the observed sales are higher than the expected values.