
on Discrete Choice Models 
By:  Fabien Leurent (CIRED  Centre International de Recherche sur l'Environnement et le Développement  Cirad  Centre de Coopération Internationale en Recherche Agronomique pour le Développement  EHESS  École des hautes études en sciences sociales  AgroParisTech  ENPC  École des Ponts ParisTech  Université ParisSaclay  CNRS  Centre National de la Recherche Scientifique) 
Abstract:  Mobility systems in urbanized territories have been featured out in Travel Demand Models by state variables of landuse occupation, trip generation, trip distribution, modal split and network assignment, with emphasis on causal relationships between the variables and on spatial detail for each kind of variables. The article is aimed to provide notional averages, say ratios, for each kind of variables, and to state the causal relationships between the variables as simple analytical formulas between the ratios. This is achieved by going along the classical four steps of travel demand modeling, in a theoretical way for an idealized territory satisfying three postulates of homogeneity: namely, at block level, at link level and of indefinite spatial extension. The said formulas constitute rules of thumb linking the mobility ratios of spatial density of human occupation, trip emission rates, average trip lengths, modal shares, generalized trip cost per length unit, together with traffic variables of speed, flow rate and vehicular density at the link level. The model is stated in eight steps, namely (i) territorial composition, (ii) trip generation, (iii) trip lengths and traffic formation, (iv) quality of service, (v) trip distribution using a gravity model, (vi) modal split by multinomial logit, (vii) traffic laws, (viii) traffic equilibrium. It is followed by a Discussion of the model outreach and limitations. Areas of further research include traffic laws, impact assessment and economic analysis. 
Keywords:  Spatial homogeneity,State laws,Fourstep travel demand model,Traffic equilibrium Highlights 
Date:  2022–10–12 
URL:  http://d.repec.org/n?u=RePEc:hal:wpaper:hal03805030&r=dcm 
By:  von Kalckreuth, Ulf 
JEL:  Q56 Q51 C81 
Date:  2022 
URL:  http://d.repec.org/n?u=RePEc:zbw:vfsc22:264036&r=dcm 
By:  Fernando Payró Chew 
Abstract:  The literature on temptation and selfcontrol is motivated by evidence of a preference for commitment. This literature has typically put forth models for preferences over menus of lotteries that satisfy the Independence axiom. Independence requires that the ranking of two menus is not affected if each is mixed (probabilistically) with a common third menu. In particular, the preference for commitment is invariant under Independence. We argue that intuitive behavior may require that the preference for commitment be affected by such mixing, and hence be mixturedependent. To capture such behavior, we generalize Gul and Pesendorfer (2001) by replacing their Independence axiom with a suitably adapted version of the MixtureBetweenness axiom of Chew (1989) Dekel (1986). Axiomatizing the model involves a novel extension of the Mixture Space Theorem to preferences that satisfy MixtureBetweenness. 
Keywords:  temptation, selfcontrol, mixture space, independence 
JEL:  D11 
Date:  2022–09 
URL:  http://d.repec.org/n?u=RePEc:bge:wpaper:1365&r=dcm 
By:  Jérôme GarnierBrun; J.P. Bouchaud; Michael Benzaquen (LadHyX  Laboratoire d'hydrodynamique  X  École polytechnique  CNRS  Centre National de la Recherche Scientifique) 
Abstract:  The Slutsky equation, central in consumer choice theory, is derived from the usual hypotheses underlying most standard models in Economics, such as full rationality, homogeneity, and absence of interactions. We present a statistical physics framework that allows us to relax such assumptions. We first derive a general fluctuationresponse formula that relates the Slutsky matrix to spontaneous fluctuations of consumption rather than to response to changing prices and budget. We then show that, within our hypotheses, the symmetry of the Slutsky matrix remains valid even when agents are only boundedly rational but noninteracting. We then propose a model where agents are influenced by the choice of others, leading to a phase transition beyond which consumption is dominated by herding (or "fashion") effects. In this case, the individual Slutsky matrix is no longer symmetric, even for fully rational agents. The vicinity of the transition features a peak in asymmetry. 
Date:  2022–10–04 
URL:  http://d.repec.org/n?u=RePEc:hal:wpaper:hal03797176&r=dcm 
By:  Jules Sadefo Kamdem (MRE  Montpellier Recherche en Economie  UM  Université de Montpellier); Danielle Selambi (African Institute for Mathematical Sciences (AIMSCameroon)) 
Abstract:  In this paper, we estimate the cost of a data breach using the number of compromised records. The number of such records is predicted by means of a machine learning model, particularly the Random Forest. We further analyse the fat tail phenomena which capture the underlying dynamics in the number of affected records. The objective is to calculate the maximum loss in order to answer the question of the insurability of cyber risk. Our results show that the total number of affected records follow a Frechet distribution, and we then estimate the Generalized Extreme Value (GEV) parameters to calculate the value at risk (VaR). This analysis is critical because it gives an idea of the maximum loss that can be generated by an enterprise data breach. These results are usable in anticipating the premiums for cyber risk coverage in the insurance markets. 
Keywords:  Cyber insurance,Cyber risk,Machine Learning,Regression Trees,Random Forest,Generalized Extreme Value 
Date:  2022–10–13 
URL:  http://d.repec.org/n?u=RePEc:hal:wpaper:hal03814979&r=dcm 
By:  Takaki Sato; Yuta Kuroda; Yasumasa Matsuda 
Abstract:  This paper proposes a spatial extension of the mixed models of the analysis of variance(MANOVA) models, which are called mixed spatial ANOVA (MSANOVA) models. MSANOVA models have been used to evaluate spatial correlations between random effects in multilevel data which is a kind of cluster data in which observations belong to some kinds of nested clusters. Because the proposed model can be regarded as a Bayesian hierarchical models, we have introduced empirical Bayesian estimation methods in which hyper parameters are estimated by quasimaximum likelihood estimation methods in the first step and posterior distributions for the parameters are evaluated with the estimated hyperparameters in the second step. Moreover, we have justified the asymptotic properties of the first step estimators. The proposed models are applied to happiness survey data in Japan and empirical results show that social capital which can be interpreted as "the beliefs and norms by which a community values collective action and pursues activities worthy for the entire community" significantly increases people's happiness, even after controlling for a variety of individual characteristics and spatial correlations. 
Date:  2022–10 
URL:  http://d.repec.org/n?u=RePEc:toh:dssraa:132&r=dcm 
By:  Takaki Sato 
Abstract:  This study considers the generalized method of moment (GMM) estimation of spatial autoregressive (SAR) models with unknown cluster correlations among error terms. In the presence of cluster correlations within errors, nonlinear moment conditions suitable for independent errors lose their validity and GMM estimators obtained from the moment condition are inconsistent. In this paper, we propose the GMM estimator obtained from another nonlinear moment condition suitable for cluster dependent error terms and show its asymptotic properties. Because the asymptotic variance of the GMM estimator depends on the choice of the weight matrix for GMM estimation, we also discuss the optimal weight which minimizes the asymptotic variance, and introduce the feasible optimal GMM estimator based on the consistent estimator of the weight. Monte Carlo experiments indicate that the proposed GMM estimator has a small bias and root mean squared errors when error terms in SAR models have cluster correlation compared to two stage least squares estimators and GMM estimators for independent errors. 
Date:  2022–10 
URL:  http://d.repec.org/n?u=RePEc:toh:dssraa:131&r=dcm 