We provide an applied introduction to Bayesian estimation methods for empirical accounting research. To showcase the methods, we compare and contrast the estimation of accruals models via a Bayesian approach with the literature's standard approach. The standard approach takes a given model of...

Bayesian shrinkage estimation is a familiar concept that arises from minimizing the Bayes risk under the squared error loss. For observations over a period of time, Bayesian shrinkage estimators may be used to predict the value of a response variable for a subject, given previously observed...

Infinitesimal perturbation analysis is a widely used approach to assess the input sensitivities of stochas- tic dynamic systems in the classical simulation context. In this paper, we introduce an efficient nu- merical approach to undertake Infinitesimal perturbation analysis in the context of...

This paper studies when stochastic choices are consistent with behavior from Bayesian expected utility maximization and information acquisition. This is a limited dataset since other characterization require state-dependent stochastic choice data. The conditions that characterize this behavior...

We analyze boundedly rational updating in a repeated interaction network model with binary actions and binary states. Agents form beliefs according to discretized DeGroot updating and apply a decision rule that assigns a (mixed) action to each belief. We first show that under weak assumptions...

The study of cultural evolution has taken on an increasingly interdisciplinary and diverse approach in explicating phenomena of cultural transmission and adoptions. Inspired by this computational movement, this study uses Bayesian networks analysis, combining both the frequentist and the...

We contribute to the non-life experience ratemaking literature by introducing a computationally efficient approximation algorithm for the Bayesian premium in models with dynamic random effect, where the risk of a policyholder is governed by an individual process of unobserved heterogeneity....

Motivated by the increasing use of external instruments to identify structural vector autoregressions SVARs), we develop algorithms for exact finite sample inference in this class of time series models, commonly known as proxy SVARs. Our algorithms make independent draws from the...

This paper proposes a variational Bayes algorithm for computationally efficient posterior and predictive inference in time-varying parameter (TVP) models. Within this context we specify a new dynamic variable/model selection strategy for TVP dynamic regression models in the presence of a large...

This supplementary appendix covers all the proofs that were skipped from the text or the appendix of Bayesian Comparative Statics (Mekonnen and Leal-Vizcaino, 2018). In particular, the proof for Corollary 1, Proposition 3, Theorem 2, and Theorem 3 are contained in this document. Several...