Bayesian decisions are observationally identical to decisions with judgment. Decisions with judgment test whether a judgmental decision is optimal and, in case of rejection, move to the closest boundary of the confidence interval, for a given confidence level. The resulting decisions condition...

A decision maker tests whether the gradient of the loss function evaluated at a judgmental decision is zero. If the test does not reject, the action is the judgmental decision. If the test rejects, the action sets the gradient equal to the boundary of the rejection region. This statistical...

A statistical decision rule incorporating judgment does not perform worse than a judgmental decision with a given probability. Under model misspecification, this probability is unknown. The best model is the least misspecified, as it is the one whose probability of underperforming the judgmental...

In this paper we propose a method for estimating and conducting inference on categorical effects of random variables that are characterize by more that two categories. We focus on a class of parametric asymptotically normal estimators in deriving the the properties which allows for inference on...

This paper describes a modelling methodology for multivariate stochastic processes. The concept of multiple causality is discussed and a procedure to detect multiple causality is suggested. The data of a major Canadian supermarket is analyzed and a multivariate autoregressive model for this...

In this paper we consider inference procedures for two types of dynamic linear panel data models with fixed effects. First, we show that the closure of the stationary ARMA panel model with fixed effects can be consistently estimated by the First Difference Maximum Likelihood Estimator and we...

Following Lancaster (2002), we propose a strategy to solve the incidental parameter problem. The method is demonstrated under a simple panel Poisson count model. We also extend the strategy to accomodate cases when information orthogonality is unavailable, such as the linear AR(p) panel model....

The paper investigates inference in nonlinear and non-Gaussian models with moderately time varying parameters. We show that for many decision problems, the sample information about the parameter path can be summarized by an artificial linear and Gaussian model, at least asymptotically. The...

In this paper we investigate the forecasting performance of the median Consumer Price Index (CPI) in a variety of Bayesian vector autoregressions (BVARs) that are often used for monetary policy. Until now, the use of trimmed-mean price statistics in forecasting inflation has often been relegated...

Multiple regression is frequently used across the various social sciences to analyze cross-sectional data. However, it can often times be challenging to justify the assumption of common regression coefficients across all respondents. This manuscript presents a heterogeneous Bayesian regression...