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The authors propose the information matrix test to assess the constancy of mean and variance parameters in vector autoregressions (VAR). They additively decompose it into several orthogonal components: conditional heteroskedasticity and asymmetry of the innovations, and their unconditional skewness and kurtosis. Their Monte Carlo simulations explore both its finite size properties and its power against i.i.d. coefficients, persistent but stationary ones, and regime switching. Their procedures detect variation in the autoregressive coefficients and residual covariance matrix of a VAR for the US GDP growth rate and the statistical discrepancy, but they fail to detect any covariation between those two sets of coefficients.

Management and especially strategy research rely heavily on the use of ratios to measure a variety of firm, industry, and societal characteristics. Most often, these ratios are created simply to control for size effects (i.e., scaling) emanating from differences in the size of firms, industries, populations, or national economies on the variables of interest. In addition, ratios may also hold theoretical meaning apart from that of their components. Despite the popularity of ratios and regardless of their purpose, the use of ratios is not without controversy. In particular, several studies have demonstrated that the use of ratio measures in correlations and multiple regressions can exaggerate relations of interest leading to biased and unstable results. In this chapter, I review the debate surrounding the use of ratio measures, discuss the problems for estimation and inference that are likely to arise when ratios are used in statistical estimation, and provide alternatives to the use of ratio variables that still satisfy the purpose for which ratio measures are often created.

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.

This chapter extends the work of Baltagi, Bresson, Chaturvedi, and Lacroix (2018) to the popular dynamic panel data model. The authors investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, the authors consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner’s (1986) g-priors for the variance–covariance matrices. The authors propose a general “toolbox” for a wide range of specifications which includes the dynamic panel model with random effects, with cross-correlated effects à la Chamberlain, for the Hausman–Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using a Monte Carlo simulation study, the authors compare the finite sample properties of the proposed estimator to those of standard classical estimators. The chapter contributes to the dynamic panel data literature by proposing a general robust Bayesian framework which encompasses the conventional frequentist specifications and their associated estimation methods as special cases.

This study looks at the value-relevance of accounting data and measures of web-traffic for Internet firms listed on the Neuer Markt. We show that earnings and cash flows cannot explain the valuation of Internet companies, while we report a positive association between total sales and market capitalisation. In addition, sales and marketing as well as research and development expenses are relevant value-drivers. Furthermore, we find a positive relation between market values and a number of web-metrics such as customer loyalty, reach, page impressions, and unique visitors. We conclude that during the Internet bubble, measures of web-traffic provided at least as much explanatory power for market values as financial statement information.

This study investigates the linkage between the Hong Kong stock market and Singapore stock market and the U.S. stock market during the pre- and post-East Asia Financial Crisis in 1997 and 1998. It uses multivariate regression models to study the impact of Hong Kong’s fixed exchange rate system and Singapore’s free-floating exchange rate system on their respective stock markets. The results indicate that the exchange rate is not a significant determinant of linkage between the U.S. and the two Asian stock markets, but the evidence suggests that stronger post-crisis relationships between the U.S. and the two Asian stock markets. The evidence also supports a stronger short-run relationship between the U.S. and Hong Kong stock markets relative to that between the U.S. and Singapore stock markets.

Purpose – To assess the following question: Do large Protestant congregations in the United States exert social and political influence simply as a function of their size, or do other characteristics amplify their influence?

Methodology/Approach – Using the U.S.-based National Congregations Study and the General Social Survey, the chapter employs a multivariate regression model to control for other factors related to church size.

Findings – Larger congregations contain a larger proportion of regular adult participants living in high-income households and possessing college degrees, and a smaller proportion of people living in low-income households. In congregations located in relatively poor census tracts, the relationship between high socioeconomic status (SES) and congregation size remains significant. Across Protestant groups, size and proportion of the congregation with high SES are correlated. Individual-level analyses of linked data from the General Social Survey confirm the positive relationship between the size of congregation the respondent attends with both high household income and possessing a college degree. These analyses also reveal a negative relationship between size and low household income.

Social implications – Size is an important factor when considering the social impact of congregations.

Originality/Value of chapter – This chapter identifies a systematic difference between churches of different sizes based on SES. This relationship has not been previously identified in a nationally representative sample.