Evaluating DSGE model forecasts of comovements

This paper develops and applies tools to assess multivariate aspects of Bayesian Dynamic Stochastic General Equilibrium (DSGE) model forecasts and their ability to predict comovements among key macroeconomic variables. We construct posterior predictive checks to evaluate conditional and unconditional density forecasts, in addition to checks for root-mean-squared errors and event probabilities associated with these forecasts. The checks are implemented on a three-equation DSGE model as well as the Smets and Wouters (2007) model using real-time data. We find that the additional features incorporated into the Smets–Wouters model do not lead to a uniform improvement in the quality of density forecasts and prediction of comovements of output, inflation, and interest rates.     

The industrial brand personality scale: Building strong business-to-business brands

To remain distinct and build strong business relationships in a competitive business-to-business (B2B) environment, an increasing number of industrial marketers attempt to exploit the potential of branding. However, brand management in the industrial sector is still at its starting point. For this reason, the authors introduce the concept of brand personality to industrial markets. Based on a series of qualitative and quantitative studies, the authors develop and validate an Industrial Brand Personality Scale. Furthermore, they examine whether brand personality perception differences exist among different types of industrial transactions and among different members in the buying center. The analysis yields a framework for theoretical discussion and provides B2B managers with a tool to build strong B2B brands in an increasingly competitive industrial market.     

A sequential Monte Carlo approach to inference in multiple‐equation Markov‐switching models

Vector autoregressions with Markov‐switching parameters (MS‐VARs) offer substantial gains in data fit over VARs with constant parameters. However, Bayesian inference for MS‐VARs has remained challenging, impeding their uptake for empirical applications. We show that sequential Monte Carlo (SMC) estimators can accurately estimate MS‐VAR posteriors. Relative to multi‐step, model‐specific MCMC routines, SMC has the advantages of generality, parallelizability, and freedom from reliance on particular analytical relationships between prior and likelihood. We use SMC's flexibility to demonstrate that model selection among MS‐VARs can be highly sensitive to the choice of prior.     

Servbrand Framework: A Business-To-Business Services Brand Equity Framework

ABSTRACT

Purpose: The research reported on here set out to develop a tailored branding model for business to business (B-to-B) services by applying the brand resonance pyramid to a selected B-to-B services context.

The brand resonance pyramid was developed based on research that was predominantly consumer product or individual brand oriented, though one of the objectives when the model was developed was that “the model had to be versatile and applicable to all possible kinds of brands and industry settings. As more diverse applications of branding continued to emerge for products, services, organizations, people, places, and so forth, the model needed to have far-ranging relevance”. The brand resonance pyramid therefore had to be applicable to any context, including B-to-B services contexts. However, consumer goods branding strategies are not directly transferable to B-to-B or services markets and there are documented differences between the B-to-B and business-to-consumer (B2C) markets and products and services contexts. There is also doubt regarding the validity of the contention that the brand resonance pyramid should be applicable to the B-to-B sector.

Methodology: Using an interpretivist qualitative research approach and an exploratory research strategy, the Servbrand framework was developed empirically by applying the brand resonance pyramid to a selected B-to-B services context. Fourteen useful in-depth interviews were obtained from appropriate and information rich participants that represented more than 14 of the 89 organization that were included in the selection frame. Some of the participants were responsible for the relevant decisions of more than one organization.

Findings: The results from the study reported on here (summarized as Figure 5) prompted the inclusion of a people dimension and elevated the importance of relationships in an amended B-to-B services brand equity framework. The people brand-building block includes the dimensions of attitude and demeanor, personality and values, personableness, product knowledge and client knowledge. Relationships, as the ultimate aim of the framework, concern both interpersonal relationships and partnerships.

The article presents a conceptual framework to guide effective brand building strategies in a selected B-to-B services context. Researchers can use the framework to test its applicability in other contexts, which will contribute to the amendment of a significant brand equity management framework.

The Servbrand framework can assist marketing practitioners to improve the effectiveness of strategic brand management for B-to-B services.

Contribution: The empirical research contributes to three areas of brand equity research, namely: 1) the offering type – by investigating service offerings rather than product offerings; 2) the brand level – by investigating organization-level brands rather than product-level brands; and 3) context – by investigating a B-to-B context rather than a B2C context. A revised brand resonance pyramid is proposed and called the Servbrand framework.     

Integrating machine learning and workflow management to support acquisition and adaptation of workflow models

Current workflow management systems (WFMS) offer little aid for the acquisition of workflow models and their adaptation to changing requirements. To support these activities we propose to apply techniques from machine learning, which enable an inductive approach to workflow acquisition and adaptation. We present a machine learning component that combines two different machine learning algorithms: the first induces the structure of sequential workflows and the second is responsible for the induction of transition conditions. The second task can be solved by applying standard decision rule induction algorithms. In this contribution we focus mainly on the algorithms for the first task. For this purpose we describe two algorithms based on the induction of hidden Markov models. The first algorithm is a bottom‐up, specific‐to‐general algorithm and the other applies a top‐down, general‐to‐specific strategy. Both algorithms have been implemented in a research prototype. In six scenarios we evaluate and compare the two algorithms experimentally. The induced workflow models can be imported by the business process management system ADONIS. Copyright © 2000 John Wiley & Sons, Ltd.