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The main purpose of this paper is to estimate the resistance and inductor in the RL electrical circuit when these are unavailable or missing data that it is a concern in electrical engineering. The input voltage is assumed to be corrupted by the noise and the current is observed at discrete time points.

The authors propose a computationally efficient framework for parameters estimation using least square estimator and Bayesian Monte Carlo scheme.

The explicit formulas for least square estimator are derived and the strong consistency of resistance estimator is verified when inductor is a known parameter, then Bayesian estimation of parameters governed by using Markov chain Monte Carlo methods. The applicability of the results is demonstrated by using numerical examples. Several numerical results and figures are presented via Matlab and R programming to illustrate the performance of the estimators.

The paper can be used in various types of electrical engineering real time projects. The projects include electrical circuits, electrical machines theory and drives, especially when the parameters are uncertain that it is a worry in electrical engineering.

To the author's best knowledge, least square and Bayesian estimation of resistance and inductor have not been studied before. The proposed model is nonlinear with respect to inductor (L); therefore the present work has fundamental difference in comparison with the similar models.

Heterogeneity in choice models is typically assumed to have a normal distribution in both Bayesian and classical setups. In this paper, we propose a semiparametric Bayesian framework for the analysis of random coefficients discrete choice models that can be applied to both individual as well as aggregate data. Heterogeneity is modeled using a Dirichlet process, which varies with consumers’ characteristics through covariates. We develop a Markov Chain Monte Carlo algorithm for fitting such model, and illustrate the methodology using two different datasets: a household-level panel dataset of peanut butter purchases, and supermarket chain-level data for 31 ready-to-eat breakfast cereal brands.

Several inferential procedures are advocated in the literature. The most commonly used techniques are the frequentist and the Bayesian inferential procedures. Bayesian methods afford inferences based on small data sets and are especially useful in studies with limited data availability. Bayesian approaches also help incorporate prior knowledge, especially subjective knowledge, into predictions. Considering the increasing difficulty in data acquisition, the application of Bayesian techniques can be hugely beneficial to managers, especially in analysing limited data situations like a study of expert opinion. Another factor constraining the broader application of Bayesian statistics in business was computational power requirements and the availability of appropriate analytical tools. However, with the increase in computational power, connectivity and the development of appropriate software programmes, Bayesian applications have become more attractive. This chapter attempts to unravel the applications of the Bayesian inferential procedure in marketing management.

Factor analysis is the most used tool in organizational research and its widespread use in scale validations contribute to decision-making in management. However, standard factor analysis is not always applied correctly mainly due to the misuse of ordinal data as interval data and the inadequacy of the former for classical factor analysis. The purpose of this paper is to present and apply the Bayesian factor analysis for mixed data (BFAMD) in the context of empirical using the Bayesian paradigm for the construction of scales.

Ignoring the categorical nature of some variables often used in management studies, as the popular Likert scale, may result in a model with false accuracy and possibly biased estimates. To address this issue, Quinn (2004) proposed a Bayesian factor analysis model for mixed data, which is capable of modeling ordinal (qualitative measure) and continuous data (quantitative measure) jointly and allows the inclusion of qualitative information through prior distributions for the parameters’ model. This model, adopted here, presents considering advantages and allows the estimation of the posterior distribution for the latent variables estimated, making the process of inference easier.

The results show that BFAMD is an effective approach for scale validation in management studies making both exploratory and confirmatory analyses possible for the estimated factors and also allowing the analysts to insert a priori information regardless of the sample size, either by using the credible intervals for Factor Loadings or by conducting specific hypotheses tests. The flexibility of the Bayesian approach presented is counterbalanced by the fact that the main estimates used in factor analysis as uniqueness and communalities commonly lose their usual interpretation due to the choice of using prior distributions.

Considering that the development of scales through factor analysis aims to contribute to appropriate decision-making in management and the increasing misuse of ordinal scales as interval in organizational studies, this proposal seems to be effective for mixed data analyses. The findings found here are not intended to be conclusive or limiting but offer a useful starting point from which further theoretical and empirical research of Bayesian factor analysis can be built.

After briefly reviewing the past history of Bayesian econometrics and Alan Greenspan's (2004) recent description of his use of Bayesian methods in managing policy-making risk, some of the issues and needs that he mentions are discussed and linked to past and present Bayesian econometric research. Then a review of some recent Bayesian econometric research and needs is presented. Finally, some thoughts are presented that relate to the future of Bayesian econometrics.

This paper aims to popularize the Bayesian methods among novice management researchers. The paper interprets the results of Bayesian method of confirmatory factor analysis (CFA), structural equation modelling (SEM), mediation and moderation analysis, with the intention that the novice researchers will apply this method in their research. The paper made an attempt in discussing various complex mathematical concepts such as Markov Chain Monte Carlo, Bayes factor, Bayesian information criterion and deviance information criterion (DIC), etc. in a lucid manner.

Data collected from 172 pharmaceutical sales representatives were used. The study will help the management researchers to perform Bayesian CFA, Bayesian SEM, Bayesian moderation analysis and Bayesian mediation analysis using SPSS AMOS software.

The interpretation of the results of Bayesian CFA, Bayesian SEM and Bayesian mediation analysis were discussed.

The management scholars are non-statisticians and are not much aware of the benefits offered by Bayesian methods. Hitherto, the management scholars use predominantly traditional SEM in validating their models empirically, and this study will give an exposure to “Bayesian statistics” that has practical advantages.

This is one paper, which discusses the following four concepts: Bayesian method of CFA, SEM, mediation and moderation analysis.

This study aims to more deeply examine the various types of testing mediations and use the comparison test by using test-based mediation Sobel models and Bayesian approach. The purpose of this study are to apply the traditional (using indirect effect) and Sobel test, extend Yuan and MacKinnon (2009) work on Bayesian mediation analysis. Both analysis methods of mediation (Traditional, Sobel Test and Bayesian estimation) should apply in the research of management, by using structural equation modeling (SEM) in a structural model, with one mediation, one exogenous (independent) and one endogenous variable. The meta-analysis approximation has been used to investigate the job satisfaction as a mediation in the relationship between employee competence and performance (endogenous).

Data were collected from ten dissertations of students of the Management Doctoral Program at the Brawijaya University from 2009 until 2013; data were analyzed for the mediation variable of job satisfaction (M) in the relationship between employee competence (X) and employee performance (Y) (Muindi and Obonyo, 2015; Olcer, 2015; Sattar et al., 2015; Khan and Ahmed, 2015). A researcher can determine the mediating variable and whether it is complete or partial or if mediation exists in several ways.

The results of the above findings using meta-analysis showed that 60% of previous research states that job satisfaction is a partial mediation on relationship competence of the performance, 10% of previous research states that job satisfaction is a full mediation on relationship competence of the performance and 30% stated that job satisfaction is not pemediasi (pemediasi means Mediation variable) on the relationship between competence and performance. This research found that all three approaches provide similar conclusions for ten previous research.

The findings showed that the Sobel approach and the Bayesian approach provide results that are more sensitive than the traditional approach.

In my opinion, the rule to investigate the mediation variable should be completed with the conditions (1) q (theta) is not statistically significant, (2) α (alpha) and β (beta) are significant, and (3) q’ (theta) is significant, and increase when M is include as an additional predictor. This condition called partial mediation.

The traditional method is simpler and easy. The method is less sensitive and is not sufficient for investigating the mediating variables. In general, the method results in a mediation variable, but it cannot be used to determine either partial or complete mediation variables. So, investigation by Baron and Kenny Methods (in Hair et al., 2010), the rule or testing called Sobel Test and another approach such as Bayesian to determine the mediation variable is necessary.

Various methods for detecting mediating/intervening have been widely used in previous research as a method of measurement using indirect effect (Hair et al., 2010), and calculations have been performed using Sobel test (Baron and Kenny, 1986) and Bayesian approach (Enders, 2013). In this study, I wanted to more deeply examine the various types of testing mediations, and use the comparison test by using the test-based mediation Sobel models and Bayesian approach (Baron and Kenny, 1986; Enders, 2013). The statistical application should not be complicated and difficult, it but must rather be simple and easy, so that it is user-friendly. The traditional method is simpler and easier than the other methods, but how sensitive is it? This research is conducted to investigate this problem. The evaluation of mediating mechanisms has become a critical element of behavioral science research (Enders, 2013), especially in the field of management, not only to assess whether (and how) interventions achieve their effects but also, more, broadly, to understand the cause of behavioral change. Methodologists have developed mediation analysis techniques for a broad range of substantive applications. However, methods for estimating mediation mechanisms with various methods have been understudied. The purpose of this study is to apply the traditional (using indirect effect) and Sobel tests and extend Yuan and MacKinnon’s (2009) work on the Bayesian mediation analysis. Both analyses methods of mediation (traditional and Sobel test and Bayesian estimation) should apply in the research of management, by using structural equation modeling (SEM) in a structural model, with one mediation, one exogenous (independent) and one endogenous variable. The meta-analysis approximation has been used to investigate job satisfaction as the mediation in the relationship between employee competence and performance (endogenous). This study uses software R to complete the mediating effect (Enders, 2013). R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers et al. R provides a wide variety of statistical analyses such as SEM and Mediation test. R provides an open source route for participation in that activity. The Bayesian estimation approach provides an R function and a macro that applies the method of mediation analysis.

Bayesian Econometrics is a volume in the series Advances in Econometrics that illustrates the scope and diversity of modern Bayesian econometric applications, reviews some recent advances in Bayesian econometrics, and highlights many of the characteristics of Bayesian inference and computations. This first paper in the volume is the Editors’ introduction in which we summarize the contributions of each of the papers.

The latest financial crisis has stressed the need of understanding the world financial system as a network of interconnected institutions, where financial linkages play a fundamental role in the spread of systemic risks. In this paper we propose to enrich the topological perspective of network models with a more structured statistical framework, that of Bayesian Gaussian graphical models. From a statistical viewpoint, we propose a new class of hierarchical Bayesian graphical models that can split correlations between institutions into country specific and idiosyncratic ones, in a way that parallels the decomposition of returns in the well-known Capital Asset Pricing Model. From a financial economics viewpoint, we suggest a way to model systemic risk that can explicitly take into account frictions between different financial markets, particularly suited to study the ongoing banking union process in Europe. From a computational viewpoint, we develop a novel Markov chain Monte Carlo algorithm based on Bayes factor thresholding.

The purpose of this study is to unfold the existing information channel in the higher moments of currency futures for different time horizons.

The authors use a quasi-Bayesian local likelihood approach within a time-varying parameter vector autoregression (TVP-VAR) framework and a dynamic connectedness measure to study the volatility, skewness and kurtosis of most traded currency futures.

The authors’ results suggest a time-varying presence of dynamic connectedness within higher moments of currency futures. Most spillovers pertain to shorter time horizons. The authors find that in net terms, CHF, EUR and JPY are the most important contributors to the system, while the authors emphasize that the role of being a transmitter or a receiver varies for pairwise interactions and time windows.

To the best of the authors’ knowledge, this is the first study that looks upon the connectivity vis-á-vis uncertainty, asymmetry and fat tails in currency futures within a dynamic Bayesian paradigm. The authors extend the current literature by proposing new insights into asset distributions.