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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.

Researchers in management regularly face modelling issues that involve double-moderated mediation models. Here, the author illustrates how to conceptualise, specify and empirically estimate mediation effects when having to simultaneously account for continuous (Likert type) and nominal (i.e. group) moderator variables. Researchers’ estimates of the mediation effects suffer serious bias because of the effects of unaccounted confounders. This is an issue that plagues management research, and this study aims to show how to address these valid reservations for its focus models. In aiming to inform a wider management audience, the study deliberately uses the rich context of a focus case as this allows the author to clarify the nuances that management researchers face applying double-moderated mediation models. Specifically, the study’s focus case is on professionals’ willingness to implement a new government policy. The study also combines traditional and Bayesian statistical approaches and explains the differences in estimation and interpretation that are associated with the Bayesian approach. Explaining, and exemplifying the use of, the models, the author focuses on how one can substantially increase the robustness of the methods used in management research and can considerably improve the quality of the generated theoretical insights. The study also clarifies important assumptions and solutions.

The study uses a doubled moderated mediation Bayesian approach, and draws the sample data from a population of 5,199 professionals, all members of either the Dutch Association of Psychologists or the Dutch Association for Psychiatry. The data collection process resulted in 1,307 questionnaires being returned, a response rate of 25 per cent. All the items were measured using a Likert scale, ranging from “strongly disagree” to “strongly agree”, unless stated otherwise.

Explaining, and exemplifying the use of, the models the study focuses on how one can substantially increase the robustness of the methods used in management research and can considerably improve the quality of the generated theoretical insights.

This is an original approach exemplified for wider use by management researchers.

The role of trend inflation shocks for the U.S. macroeconomic dynamics is investigated by estimating two DSGE models of the business cycle. Policymakers are assumed to be concerned with a time-varying inflation target, which is modeled as a persistent and stochastic process. The identification of trend inflation shocks (as opposed to a number of alternative innovations) is achieved by exploiting the measure of trend inflation recently proposed by Aruoba and Schorfheide (2011). Our main findings point to a substantial contribution of trend inflation shocks for the volatility of inflation and the policy rate. Such contribution is found to be time dependent and highest during the mid-1970s to mid-1980s.

Several lessons learnt from a Bayesian analysis of basic macroeconomic time-series models are presented for the situation where some model parameters have substantial posterior probability near the boundary of the parameter region. This feature refers to near-instability within dynamic models, to forecasting with near-random walk models and to clustering of several economic series in a small number of groups within a data panel. Two canonical models are used: a linear regression model with autocorrelation and a simple variance components model. Several well-known time-series models like unit root and error correction models and further state space and panel data models are shown to be simple generalizations of these two canonical models for the purpose of posterior inference. A Bayesian model averaging procedure is presented in order to deal with models with substantial probability both near and at the boundary of the parameter region. Analytical, graphical, and empirical results using U.S. macroeconomic data, in particular on GDP growth, are presented.

Recently, it has been suggested that macroeconomic forecasts from estimated dynamic stochastic general equilibrium (DSGE) models tend to be more accurate out-of-sample than random walk forecasts or Bayesian vector autoregression (VAR) forecasts. Del Negro and Schorfheide (2013) in particular suggest that the DSGE model forecast should become the benchmark for forecasting horse-races. We compare the real-time forecasting accuracy of the Smets and Wouters (2007) DSGE model with that of several reduced-form time series models. We first demonstrate that none of the forecasting models is efficient. Our second finding is that there is no single best forecasting method. For example, typically simple AR models are most accurate at short horizons and DSGE models are most accurate at long horizons when forecasting output growth, while for inflation forecasts the results are reversed. Moreover, the relative accuracy of all models tends to evolve over time. Third, we show that there is no support to the common practice of using large-scale Bayesian VAR models as the forecast benchmark when evaluating DSGE models. Indeed, low-dimensional unrestricted AR and VAR forecasts may forecast more accurately.

In this chapter, the authors ask two questions: (i) Is the conduct of monetary policy stable across time and similar across major economies? and (ii) Do policy decisions of major central banks have international spillover effects? To address these questions, the authors build on recent semi-parametric advances in time-varying parameter models that allow us to increase the vector autoregressive () dimension and to jointly model three advanced economies (USA, UK and the Euro Area). The main reduced-form finding of this chapter is an increased connectedness between and within countries during the recent financial crisis. In order to study policy spillovers, we jointly identify three economy-specific monetary policy shocks using a combination of sign and magnitude restrictions. The authors find that monetary policy shocks were larger in magnitude and more persistent in the early 1980s than in subsequent periods. The authors also uncover positive spillover effects of policy between countries in the 1980s and diminished, and sometimes negative ‘beggar-thy-neighbour’ effects in the second half of the sample. Moreover, during the 1980s, the authors find evidence for policy coordination between the Federal Reserve, the Bank of England and the European Central Bank.

This study aims to analyze the effects of cultural orientations (performance and sociality) on the trajectories of innovation inputs and their results in different countries worldwide between 2011 and 2021.

As a technique for data analysis, one of the spatial Bayesian models and Gray forecasting methods is used. This technique is adequate to achieve the objectives of the investigation because it allows analyzing how the variables move in time ranges and allow the generation of forecasts. This model also allows knowing if there are spills, which investing in a country can positively affect countries with geographical proximity. The databases used were the Global Innovation Index with data from 131 nations and the Globe Project with data from 157 countries between 2011 and 2021. The variables analyzed are institutions, human capital, research infrastructure, market sophistication and business sophistication. On the other hand, regarding moderations of cultural orientations, The Globe Project developed two factors: performance orientation (high degree of masculinity, avoidance of ambiguity, power distance and future orientation) and humane orientation (high-level of femininity, institutional and societal collectivism).

The results reveal that all inputs grow at different rates over time. In the case of institutions, it is the most difficult to generate changes over time. However, human capital, market sophistication and business sophistication are the ones that have grown the most over time, regardless of the country’s cultural orientation.

Among the main limitations is the set of data used because it only considers one approach to culture, especially the one considered by Hofstede. However, other approaches could help evaluate the results of this research. Considering the results obtained, the study attempts to provide a different view of the effects of cultural variables on companies’ innovation performance in different countries in the world. In the same way, evaluating these effects allows firms to consider variables associated with the country that will affect the strategies and performance of the firm.

The results achieved make it possible to strengthen the analysis of the countries’ strategies when it comes to innovation, especially in the permanent evaluation of the results that allow to encourage changes in the execution of innovative activities to maintain their performance over time.

The contributions allow us to understand the dynamics of innovation in the knowledge and creative outputs of countries over time.

The trajectory analysis used in the data analysis is perhaps one of the most robust techniques that makes a time series analysis. This allows identifying trajectories for the independent variables of the study and their influence on the innovation of the country.

For over three decades, vector autoregressions have played a central role in empirical macroeconomics. These models are general, can capture sophisticated dynamic behavior, and can be extended to include features such as structural instability, time-varying parameters, dynamic factors, threshold-crossing behavior, and discrete outcomes. Building upon growing evidence that the assumption of linearity may be undesirable in modeling certain macroeconomic relationships, this article seeks to add to recent advances in VAR modeling by proposing a nonparametric dynamic model for multivariate time series. In this model, the problems of modeling and estimation are approached from a hierarchical Bayesian perspective. The article considers the issues of identification, estimation, and model comparison, enabling nonparametric VAR (or NPVAR) models to be fit efficiently by Markov chain Monte Carlo (MCMC) algorithms and compared to parametric and semiparametric alternatives by marginal likelihoods and Bayes factors. Among other benefits, the methodology allows for a more careful study of structural instability while guarding against the possibility of unaccounted nonlinearity in otherwise stable economic relationships. Extensions of the proposed nonparametric model to settings with heteroskedasticity and other important modeling features are also considered. The techniques are employed to study the postwar U.S. economy, confirming the presence of distinct volatility regimes and supporting the contention that certain nonlinear relationships in the data can remain undetected by standard models.

There is a dearth of literature on what are the factors in terms of leaders’ and followers’ characteristics that impact innovation at the country level. The purpose of this paper is to build theoretical argument and provide empirical evidence of these factors using a cross-cultural mode of study across 56 nations.

The Bayesian modelling technique is used on data from the GLOBE survey.

Innovation at the individual, team and organisational levels has generally been associated with the relationship-motivated leadership, as opposed to task-motivated leadership. This study confirms that this premise holds at the societal level of analysis as well. The second finding is that in terms of followers’ cultural characteristics, out of three variables (power distance, collectivism and performance orientation) tested, only power distance orientation is found to have a predictive relationship with aggregate innovation. The moderator slope analysis unveils a nuanced understanding of how the interaction between leadership styles and followers’ cultural traits impact national innovativeness.

Culture and leadership configurations that bolster innovation need to be studied more thoroughly.

This study has implications for multi-country teams involved in research and development activities.

To our knowledge, this is the first study to unpack leader−follower relationships as predictors of national innovation. A leadership-culture fit perspective is advanced.