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We put forward the idea that for model selection the intrinsic priors are becoming a center of a cluster of a dominant group of methodologies for objective Bayesian Model Selection.

The intrinsic method and its applications have been developed in the last two decades, and has stimulated closely related methods. The intrinsic methodology can be thought of as the long searched approach for objective Bayesian model selection and hypothesis testing.

In this paper we review the foundations of the intrinsic priors, their general properties, and some of their applications.

This chapter extends the work of Baltagi, Bresson, Chaturvedi, and Lacroix (2018) to the popular dynamic panel data model. The authors investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, the authors consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner’s (1986) g-priors for the variance–covariance matrices. The authors propose a general “toolbox” for a wide range of specifications which includes the dynamic panel model with random effects, with cross-correlated effects à la Chamberlain, for the Hausman–Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using a Monte Carlo simulation study, the authors compare the finite sample properties of the proposed estimator to those of standard classical estimators. The chapter contributes to the dynamic panel data literature by proposing a general robust Bayesian framework which encompasses the conventional frequentist specifications and their associated estimation methods as special cases.

Repetitive transcranial magnetic stimulation (rTMS) is a neurostimulatory technique used to modulate orbital frontal corticostriatal (OFC) activity and clinical symptomatology for psychiatric disorders involving OFC dysfunction. We examined the effectiveness of rTMS in the treatment of major depressive disorder in an applied clinical setting (Awakening KC CNI) to assess efficacy and optimize rTMS parameters within clinical practice. A retrospective review of medical records was carried out on patients with major depressive disorder undergoing rTMS therapy at Awakenings KC Clinical Neuroscience Institute (CNI), a suburban tertiary psychiatric clinic. A detailed de-identified data set of clinical outcomes was compiled. Patient Health Questionnaire 9 (PHQ-9) total score, clinical remission rate and week achieved were evaluated over 6 weeks of treatment to assess clinical response referencing two different rTMS instruments (MagVenture; NeuroStar). Our survey included 247 participants from males (N=98) and females (N=149) with average baseline PHQ-9 scores of 21.7±4, classified as severe depression. Clinically rated remission rates of 72% were achieved in 3.1±1.0 weeks and associated with prior history of psychiatric hospitalization, suicide attempts and substance use disorder. Average baseline PHQ-9 scores decreased significantly over time with proportionately greater remission rates achieved for patients treated using the MagVenture over NeuroStar instrument. rTMS in applied clinical practice is efficacious over a wide range of settings and patients. Clinical response was related to severity of depression symptoms (e.g., prior hospitalization; suicide attempts) validating efficacy in critically ill groups. Clinical response may be impacted by rTMS instrument, magnetic field parameters or individual factors.

The purpose of this paper is to understand how augmented reality (AR) try-on experiences facilitate consumers’ shopping decision. Focusing on the immersion and psychological ownership, the study investigated how the properties of AR experiences (environmental embedding (EE) and simulated physical control (SPC)) affect decision comfort.

This research theoretically and empirically analyzes how each property of AR experiences affects consequential psychological states and then further increases decision comfort by employing an existing AR try-on mobile application. A total of 99 valid responses were used for the partial least square structural equation modeling analysis. One’s prior AR try-on experience was predicted as a moderator and analyzed using SPSS-based PROCESS macro.

The results demonstrated that EE and SPC evoke immersion and the feeling of ownership of a virtual product, which increased decision comfort. The moderating effect of one’s prior AR try-on experience showed that the impact of EE and SPC on immersion was attenuated for those with prior experience. Further, immersion mediated the effect of EE but SPC on the feeling of ownership, which corroborated the direct effect of SPC on the feeling of ownership.

Firms must consider technological and user-experience features that can induce users to perceive high levels of AR characteristics such as EE and SPC. Practitioners should develop realistic content that can correctly place virtual products on users to enhance EE. Including more interactive features is encouraged to provide users with a feeling of control toward the virtual product that directly leads to ownership and positively affects decision making. Further, practitioners need to be cautious about consumers getting used to the new technology; retailers and marketers need to focus on creating new and innovative content to continually engage customers.

This study adopted EE and SPC to determine how each property of AR experience forms the consequential psychological states, particularly depending on one’s prior experience. Methodologically, the study provided external validity in conducting an experiment by adopting an existing AR mobile application available in the market and employing an objective measure of respondents (e.g. prior AR try-on experience).

This paper aims to give a brief review on behavioral economics and behavioral finance and discusses some of the previous research on agents' utility functions, applicable risk measures, diversification strategies and portfolio optimization.

The authors also cover related disciplines such as trading rules, contagion and various econometric aspects.

While scholars could first develop theoretical models in behavioral economics and behavioral finance, they subsequently may develop corresponding statistical and econometric models, this finally includes simulation studies to examine whether the estimators or statistics have good power and size. This all helps us to better understand financial and economic decision-making from a descriptive standpoint.

The research paper is original.

We consider a semiparametric panel stochastic frontier model where one-sided firm effects representing inefficiencies are correlated with the regressors. A form of the Chamberlain-Mundlak device is used to relate the logarithm of the effects to the regressors resulting in a lognormal distribution for the effects. The function describing the technology is modeled nonparametrically using penalized splines. Both Bayesian and non-Bayesian approaches to estimation are considered, with an emphasis on Bayesian estimation. A Monte Carlo experiment is used to investigate the consequences of ignoring correlation between the effects and the regressors, and choosing the wrong functional form for the technology.

The Barbican Library is the headquarters of the City of London's lending library service. It includes a music library and a children's library as well as a special section devoted to the fine and performing arts. Anyone who lives, works or studies in the City may apply to be a member. Others who wish to join may do so under the interavailability scheme.

For this discussion, assume there are n sample observations of the dependent variable y at unique locations. In spatial samples, often each observation is uniquely associated with a particular location or region, so that observations and regions are equivalent. Spatial dependence arises when an observation at one location, say y i is dependent on “neighboring” observations y j, y j∈ϒi. We use ϒi to denote the set of observations that are “neighboring” to observation i, where some metric is used to define the set of observations that are spatially connected to observation i. For general definitions of the sets ϒi,i=1,…,n, typically at least one observation exhibits simultaneous dependence, so that an observation y j, also depends on y i. That is, the set ϒj contains the observation y i, creating simultaneous dependence among observations. This situation constitutes a difference between time series analysis and spatial analysis. In time series, temporal dependence relations could be such that a “one-period-behind relation” exists, ruling out simultaneous dependence among observations. The time series one-observation-behind relation could arise if spatial observations were located along a line and the dependence of each observation were strictly on the observation located to the left. However, this is not in general true of spatial samples, requiring construction of estimation and inference methods that accommodate the more plausible case of simultaneous dependence among observations.