Risk Analytics in Econometrics

[eng] This thesis addresses the framework of risk analytics as a compendium of four main pillars: (i) big data, (ii) intensive programming, (iii) advanced analytics and machine learning, and (iv) risk analysis. Under the latter mainstay, this PhD dissertation reviews potential hazards known as “extreme events” that could negatively impact the wellbeing of people, profitability of firms, or the economic stability of a country, but which also have been underestimated or incorrectly treated by traditional modelling techniques. The objective of this thesis is to develop econometric and machine learning algorithms that can improve the predictive capacity of those extreme events and improve the comprehension of the phenomena contrary to some modern advanced methods which are black boxes in terms of interpretation. This thesis presents seven chapters that provide a methodological contribution to the existing literature by building techniques that transform the new valuable insights of big data into more accurate predictions that support decisions under risk, and increase robustness for more reliable and real results. This PhD thesis focuses uniquely on extremal events which are trigged into a binary variable, mostly known as class-imbalanced data and rare events in binary response, in other words, whose classes that are not equally distributed. The scope of research tackle real cases studies in the field of risk and insurance, where it is highly important to specify a level of claims of an event in order to foresee its impact and to provide a personalized treatment. After Chapter 1 corresponding to the introduction, Chapter 2 proposes a weighting mechanism to incorporated in the weighted likelihood estimation of a generalized linear model to improve the predictive performance of the highest and lowest deciles of prediction. Chapter 3 proposes two different weighting procedures for a logistic regression model with complex survey data or specific sampling designed data. Its objective is to control the randomness of data and provide more sensitivity to the estimated model. Chapter 4 proposes a rigorous review of trials with modern and classical predictive methods to uncover and discuss the efficiency of certain methods over others, and which and how gaps in machine learning literature can be addressed efficiently. Chapter 5 proposes a novel boosting-based method that overcomes certain existing methods in terms of predictive accuracy and also, recovers some interpretation of the model with imbalanced data. Chapter 6 develops another boosting-based algorithm which is able to improve the predictive capacity of rare events and get approximated as a generalized linear model in terms of interpretation. And finally, Chapter 7 includes the conclusions and final remarks. The present thesis highlights the importance of developing alternative modelling algorithms that reduces uncertainty, especially when there are potential limitations that impede to know all the previous factors that influence on the presence of a rare event or imbalanced-data phenomenon. This thesis merges two important approaches in modelling predictive literature as they are: “econometrics” and “machine learning”. All in all, this thesis contributes to enhance the methodology of how empirical analysis in many experimental and non-experimental sciences have being doing so far

Penalized logistic regression to improve predictive capacity of rare events in surveys

Logistic regression as a modelling technique of rare binary dependent variables with much fewer events (ones) than non-events (zeros) tends to underestimate their probability of occurrence. The vast literature devoted to the prediction of rare binary data identifies several ways to improve predictive performance by making modifications to the likelihood estimation. We propose two weighting mechanisms for incorporation in a pseudo-likelihood estimation that improve the predictive capacity of rare binary responses in data collected in complex surveys. We multiply sampling weights by specific correctors that lead to lower root mean square errors for event observations in almost all deciles. A case study is discussed where this method is implemented to predict the probability of suffering a workplace accident in a logistic regression model that is estimated with data from a survey conducted in Ecuador

A Synthetic penalized logitboost to model mortgage lending with imbalanced cata

Most classical econometric methods and tree boosting based algorithms tend to increase the prediction error with binary imbalanced data. We propose a synthetic penalized logitboost based on weighting corrections. The procedure (i) improves the prediction performance under the phenomenon in question, (ii) allows interpretability since coefficients can get stabilized in the recursive procedure, and (iii) reduces the risk of overfitting. We consider a mortgage lending case study using publicly available data to illustrate our method. Results show that errors are smaller in many extreme prediction scores, outperforming a number of existing methods. Our interpretations are consistent with results obtained using a classic econometric model

RiskLogitboost Regression for Rare Events in Binary Response: An Econometric Approach

A boosting-based machine learning algorithm is presented to model a binary response with large imbalance, i.e., a rare event. The new method (i) reduces the prediction error of the rare class, and (ii) approximates an econometric model that allows interpretability. RiskLogitboost regression includes a weighting mechanism that oversamples or undersamples observations according to their misclassification likelihood and a generalized least squares bias correction strategy to reduce the prediction error. An illustration using a real French third-party liability motor insurance data set is presented. The results show that RiskLogitboost regression improves the rate of detection of rare events compared to some boosting-based and tree-based algorithms and some existing methods designed to treat imbalanced responses

Semi-Solid Dosage Forms Containing Pranoprofen-Loaded NLC as Topical Therapy for Local Inflammation: In Vitro, Ex Vivo and In Vivo Evaluation

Pranoprofen (PRA)-loaded nanostructured lipid carriers (NLC) have been dispersed into blank gels composed of 1% of Carbomer 940 (PRA-NLC-Car) and 3% of Sepigel® 305 (PRA-NLC-Sep) as a novel strategy to refine the biopharmaceutical profile of PRA, for dermal administration in the treatment of skin inflammation that may be caused by possible skin abrasion. This stratagem intends to improve the joining of PRA with the skin, improving its retention and anti-inflammatory effect. Gels were evaluated for various parameters such as pH, morphology, rheology, and swelling. In vitro drug release research and ex vivo permeation through the skin were carried out on Franz diffusion cells. Additionally, in vivo assays were carried out to evaluate the anti-inflammatory effect, and tolerance studies were performed in humans by evaluating the biomechanical properties. Results showed a rheological profile common of semi-solid pharmaceutical forms for dermal application, with sustained release up to 24 h. In vivo studies using PRA-NLC-Car and PRA-NLC-Sep in Mus musculus mice and hairless rats histologically demonstrated their efficacy in an inflammatory animal model study. No signs of skin irritation or modifications of the skin's biophysical properties were identified and the gels were well tolerated. The results obtained from this investigation concluded that the developed semi-solid formulations represent a fitting drug delivery carrier for PRA's transdermal delivery, enhancing its dermal retention and suggesting that they can be utilized as an interesting and effective topical treatment for local skin inflammation caused by a possible abrasion

Quality by Design of Pranoprofen Loaded Nanostructured Lipid Carriers and Their Ex Vivo Evaluation in Different Mucosae and Ocular Tissues

Transmucosal delivery is commonly used to prevent or treat local diseases. Pranoprofen is an anti-inflammatory drug prescribed in postoperative cataract surgery, intraocular lens implantation, chorioretinopathy, uveitis, age-related macular degeneration or cystoid macular edema. Pranoprofen can also be used for acute and chronic management of osteoarthritis and rheumatoid arthritis. Quality by Design (QbD) provides a systematic approach to drug development and maps the influence of the formulation components. The aim of this work was to develop and optimize a nanostructured lipid carrier by means of the QbD and factorial design suitable for the topical management of inflammatory processes on mucosal tissues. To this end, the nanoparticles loading pranoprofen were prepared by a high-pressure homogenization technique with Tween 80 as stabilizer and Lanette® 18 as the solid lipid. From, the factorial design results, the PF-NLCs-N6 formulation showed the most suitable characteristics, which was selected for further studies. The permeability capacity of pranoprofen loaded in the lipid-based nanoparticles was evaluated by ex vivo transmucosal permeation tests, including buccal, sublingual, nasal, vaginal, corneal and scleral mucosae. The results revealed high permeation and retention of pranoprofen in all the tissues tested. According to the predicted plasma concentration at the steady-state, no systemic effects would be expected, any neither were any signs of ocular irritancy observed from the optimized formulation when tested by the HET-CAM technique. Hence, the optimized formulation (PF-NLCs-N6) may offer a safe and attractive nanotechnological tool in topical treatment of local inflammation on mucosal diseases

Semi-Solid Dosage Forms Containing Pranoprofen-Loaded NLC as Topical Therapy for Local Inflammation: In Vitro, Ex Vivo and In Vivo Evaluation

Pranoprofen (PRA)-loaded nanostructured lipid carriers (NLC) have been dispersed into blank gels composed of 1% of Carbomer 940 (PRA-NLC-Car) and 3% of Sepigel® 305 (PRA-NLC-Sep) as a novel strategy to refine the biopharmaceutical profile of PRA, for dermal administration in the treatment of skin inflammation that may be caused by possible skin abrasion. This stratagem intends to improve the joining of PRA with the skin, improving its retention and anti-inflammatory effect. Gels were evaluated for various parameters such as pH, morphology, rheology, and swelling. In vitro drug release research and ex vivo permeation through the skin were carried out on Franz diffusion cells. Additionally, in vivo assays were carried out to evaluate the anti-inflammatory effect, and tolerance studies were performed in humans by evaluating the biomechanical properties. Results showed a rheological profile common of semi-solid pharmaceutical forms for dermal application, with sustained release up to 24 h. In vivo studies using PRA-NLC-Car and PRA-NLC-Sep in Mus musculus mice and hairless rats histologically demonstrated their efficacy in an inflammatory animal model study. No signs of skin irritation or modifications of the skin’s biophysical properties were identified and the gels were well tolerated. The results obtained from this investigation concluded that the developed semi-solid formulations represent a fitting drug delivery carrier for PRA’s transdermal delivery, enhancing its dermal retention and suggesting that they can be utilized as an interesting and effective topical treatment for local skin inflammation caused by a possible abrasion