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We show that in sorting cross-sectional data, the endogeneity of a variable may be successfully detected by graphically examining the cumulative sum of the recursive residuals. Moreover, the sign of the bias implied by the endogeneity may be deducible through such graphs. In general, instrumental variables are needed to implement the graphical test. However, when a continuous or ordered (e.g. years of schooling) variable is suspected to be endogenous, a graphical test for misspecification due to endogeneity (e.g. self-selection) can be obtained without instrumental variables.

At first sight the reader may find it odd that I have grouped the commentaries by Esping-Andersen and Rubinson/Ragin together, given that while the former proposes revamping the use of MR in comparative research, the latter offers a radical alternative. Nevertheless, while suggesting different solutions, both largely agree with my diagnosis of the problems. Also, my principal response to both is that their practical proposals look promising, yet are difficult to judge. That would require, (a) a user-friendly guide to implementing the advocated techniques; and (b) side-by-side comparison of the results obtained by their favored methods, the conventional MR method, and my own suggestions. Let me hasten to add that given (a), I would be ready to undertake the work involved in generating (b).

We analyze spatio-temporal data on U.S. unemployment rates. For this purpose, we present a family of models designed for the analysis and time-forward prediction of spatio-temporal econometric data. Our model is aimed at applications with spatially sparse but temporally rich data, i.e. for observations collected at few spatial regions, but at many regular time intervals. The family of models utilized does not make spatial stationarity assumptions and consists in a vector autoregressive (VAR) specification, where there are as many time series as spatial regions. A model building strategy is used that takes into account the spatial dependence structure of the data. Model building may be performed either by displaying sample partial correlation functions, or automatically with an information criterion. Monthly data on unemployment rates in the nine census divisions of the U.S. are analyzed. We show with a residual analysis that our autoregressive model captures the dependence structure of the data better than with univariate time series modeling.

It is a well-known fact that economic development and rise in the volume of trade due to globalization have led to more production which has further led to the increase in the emission of carbon dioxide in the environment. Under the backdrop, the aim of this chapter is to examine the relationships among per capita CO₂ emissions as the proxy for exploitation of the environment with international trade and per capita GDP in India. It analyzed cointegration and short-run causal relationships between the variables based on a time series data set for the period of 1979–2018. The data found to be stationary at first integration; hence the researchers ran cointegration. The study found that the carbon emissions are an outcome of economic growth and more and more trade with the foreign countries.

Companies in all industries are searching for new sources of competitive advantage since the competition in their marketplace is becoming increasingly intensive. The resource-based view of the firm explains the sources of sustainable competitive advantages. From a resource-based view perspective, relational based assets (i.e., the assets resulting from firm contacts in the marketplace) enable competitive advantage. The relational based assets examined in this work are brand image and corporate reputation, as components of brand equity, and customer value. This paper explores how they create value. Despite the relatively large amount of literature describing the benefits of firms in having strong brand equity and delivering customer value, no research validated the linkage of brand equity components, brand image, and corporate reputation, simultaneously in the customer value–customer loyalty chain. This work presents a model of testing these relationships in consumer goods, in a business-to-business context. The results demonstrate the differential roles of brand image and corporate reputation on perceived quality, customer value, and customer loyalty. Brand image influences the perception of quality of the products and the additional services, whereas corporate reputation actions beyond brand image, estimating the customer value and customer loyalty. The effects of corporate reputation are also validated on different samples. The results demonstrate the importance of managing brand equity facets, brand image, and corporate reputation since their differential impacts on perceived quality, customer value, and customer loyalty. The results also demonstrate that companies should not limit to invest only in brand image. Maintaining and enhancing corporate reputation can have a stronger impact on customer value and customer loyalty, and can create differential competitive advantage.

The current study aims to advance the understanding of the role of performance measurement systems (PMS) in the improvement of companies’ performance, as well to contribute to the limited knowledge of this issue in transition economies. In order to do so, performance effects of PMS are examined, testing both dominant approaches: the performance measurement diversity view and performance measurement alignment view.

A survey using questionnaire was conducted on a sample of large- and medium-sized manufacturing companies in Serbia.

The results of the research support the performance measurement diversity view, as we found evidence that a broader scope PMS is positively associated with performance. However, we also found partial support for the influence of the strategy–PMS alignment on performance.

This study investigates the complex mechanism of PMS effect on performance in a particular context – the transition economy. Moreover, this study represents a pioneering attempt to evaluate the state of performance measurement practice in Serbia and is one of the rare studies of this type on transition economies.

This paper compares alternative estimation procedures for multi-level factor models which imply blocks of zero restrictions on the associated matrix of factor loadings. We suggest a sequential least squares algorithm for minimizing the total sum of squared residuals and a two-step approach based on canonical correlations that are much simpler and faster than Bayesian approaches previously employed in the literature. An additional advantage is that our approaches can be used to estimate more complex multi-level factor structures where the number of levels is greater than two. Monte Carlo simulations suggest that the estimators perform well in typical sample sizes encountered in the factor analysis of macroeconomic data sets. We apply the methodologies to study international comovements of business and financial cycles.