Two model averaging approaches are used and compared in estimating and forecasting dynamic factor models, the well-known BMA and the recently developed WALS. Both methods propose to combine frequentist estimators using Bayesian weights. We apply our framework to the Armenian economy using...

Abstract: In specifying a regression equation, we need to determine which regressors to include, but also how these regressors are measured. This gives rise to two levels of uncertainty: concepts (level 1) and measurements within each concept (level 2). In this paper we propose a hierarchical...

Auditing a large population of recorded values is usually done by means of sampling.Based on the number of incorrect records that is detected in the sample, a point estimate and a confidence limit for the population fraction of incorrect values can be determined.In general it is (implicitly)...

Empirical growth research faces a high degree of model uncertainty. Apart from the neoclassical growth model, many new (endogenous) growth models have been proposed. This causes a lack of robustness of the parameter estimates and makes the determination of the key determinants of growth...

We consider the problem of estimating the first k coeffcients in a regression equation with k + 1 variables.For this problem with known variance of innovations, the neutral Laplace weighted-average least-squares estimator was introduced in Magnus (2002).We investigate properties of this...

In categorical repeated audit controls, fallible auditors classify sample elements in order to estimate the population fraction of elements in certain categories.To take possible misclassifications into account, subsequent checks are performed with a decreasing number of observations.In this...

Deciding upon the optimal sample size in advance is a difficult problem in general. Often, the investigator regrets not having drawn a larger sample; in many cases additional observations are done. This implies that the actual sample size is no longer deterministic; hence, even if all sample...

Risk assessments often encounter extreme settings with very few or no occurrences in reality.Inferences about risk indicators in such settings face the problem of insufficient data.Extreme value theory is particularly well suited for handling this type of problems.This paper uses a multivariate...

Inventory models need information about the demand distribution. In practice, this information is not known with certainty and has to be estimated with often relatively few historical demand observations. Using these estimates leads to underperformance. This paper focuses on gamma distributed...

Inventory models need some specification of the distribution of demand in order to find the optimal order-up-to level or reorder point.This distribution is unknown in real life and there are several solutions to overcome this problem.One approach is to assume a distribution, estimate its...