This paper proposes a novel robustness framework for studying the unit elasticity of intertemporal substitution (EIS) assumption based on the Perron-Frobenius sieve estimation model by Christensen, 2017. The sieve nonparametric decomposition is a cen...
14 November 2026
Choosing a restaurant for a group is hard: tastes conflict, some members cannot safely eat certain foods, and one member often bears the compromise. We study a group recommender that admits only restaurants where a fail-closed, ingredient-level filte...
This study addresses the crucial but under-explored topic of ambiguity aversion, i.e., model misspecification, in the area of environmental, social, and corporate governance (ESG) within portfolio decisions. It considers a risk- and ambiguity-averse...
Structural Equation Modeling (SEM) is a key framework for analyzing complex economic relationships involving latent variables, mediation effects, and endogeneity, yet the choice between frequentist and Bayesian estimation remains theoretically and pr...
This paper extends the canonical small open-economy real-business-cycle model, when considering model uncertainty. Domestic households have multiplier preferences, which leads them to take robust decisions in response to possible model misspecificati...
This paper investigates the approximated arbitrage bounds of option prices in an incomplete market setting and draws implications for option pricing and risk management. It gives consideration to periods of global financial crisis and European sovere...
Estimating the non-market monetary values of urban amenities has become commonplace in urban planning research, particularly following Rosen’s seminal article on hedonic theory in 1974. As a revealed preference method, the hedonic approach decouples...
11 December 2023
Often in the planning phase of a clinical trial, a researcher will need to choose between a standard versus weighted log-rank test (LRT) for investigating right-censored survival data. While a standard LRT is optimal for analyzing evenly distributed...
Factor analysis is one of the most important statistical tools for analyzing multivariate data (i.e., items) in the social sciences. An essential case is the comparison of multiple groups on a one-dimensional factor variable that can be interpreted a...
Background/Objectives: We evaluated six-predictor cause-specific models for 45-day in-hospital COVID-19 mortality, including departures from proportional hazards and linearity and transport to Mexican data. Methods: The oxygenation-expanded Barcelona...
Expert–public selection systems often reveal expert scores, institutional rules, and discrete elimination outcomes while concealing public-vote information. This paper develops a constrained Bayesian inverse-inference framework for inferring ru...