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This chapter measures financial integration in 10 industries over 4 different periods. We use two robust measures of integration: (i) the Pukthuanthong and Roll (2009)’s multi-factor R-square and (ii) the Volosovych (2011)’s integration index. Both measures, based on PCA, indicate that the difference between the level of integration over the period 2009–2012 (“Post-Lehman” era) and the level of integration over the period 1994–1998 (“Post-Liberalizations” era) is relatively high. In addition, the level of financial integration across international equity markets decreased during the late 1990s. This suggests that de jure integration does not necessarily improve de facto integration. Overall, our findings give rise to a “diversification benefits-insurance benefits trade-off.”

We address the problem of selecting the common factors that are relevant for forecasting macroeconomic variables. In economic forecasting using diffusion indexes, the factors are ordered, according to their importance, in terms of relative variability, and are the same for each variable to predict, that is, the process of selecting the factors is not supervised by the predictand. We propose a simple and operational supervised method, based on selecting the factors on the basis of their significance in the regression of the predictand on the predictors. Given a potentially large number of predictors, we consider linear transformations obtained by principal components analysis. The orthogonality of the components implies that the standard t-statistics for the inclusion of a particular component are independent, and thus applying a selection procedure that takes into account the multiplicity of the hypotheses tests is both correct and computationally feasible. We focus on three main multiple testing procedures: Holm's sequential method, controlling the familywise error rate, the Benjamini–Hochberg method, controlling the false discovery rate, and a procedure for incorporating prior information on the ordering of the components, based on weighting the p-values according to the eigenvalues associated to the components. We compare the empirical performances of these methods with the classical diffusion index (DI) approach proposed by Stock and Watson, conducting a pseudo-real-time forecasting exercise, assessing the predictions of eight macroeconomic variables using factors extracted from an U.S. dataset consisting of 121 quarterly time series. The overall conclusion is that nature is tricky, but essentially benign: the information that is relevant for prediction is effectively condensed by the first few factors. However, variable selection, leading to exclude some of the low-order principal components, can lead to a sizable improvement in forecasting in specific cases. Only in one instance, real personal income, we were able to detect a significant contribution from high-order components.

Quality Function Deployment (QFD) proposes to take into account the “voice of the customer,” through a list of customer needs, which are (qualitatively) mapped to technical requirements in House One. But customers do not perceive products in this space, nor do they not make purchase decisions in this space. Marketing specialists use statistical models to map between a simpler space of customer perceptions and the long and detailed list of needs. For automobiles, for example, the main axes in perceptual space might be categories such as luxury, performance, sport, and utility. A product’s position on these few axes determines the detailed customer requirements consistent with the automobiles’ position such as interior volume, gauges and accessories, seating type, fuel economy, door height, horsepower, interior noise level, seating capacity, paint colors, trim, and so forth. Statistical models such as factor analysis and principal components analysis are used to describe the mapping between these spaces, which we call House Zero.

This paper focus on House One. Two important steps of the product development process using House One are: (1) setting technical targets; (2) identifying the inherent tradeoffs in a design including a position of merit. Utility functions are used to determine feature preferences for a product. Conjoint analysis is used to capture the product preference and potential market share. Linear interpolation and the slope point formula are used to determine other points of customer needs. This research draws from the formal mapping concepts developed by Nam Suh and the qualitative maps of quality function deployment, to present unified information and mapping paradigm for concurrent product/process design. This approach is the virtual integrated design method that is tested upon data from a business design problem.

Technological progress is a determining factor in the factors leading to economic and social well-being. Simultaneously, the development of a sustainable economy is based on the conservation of resources. In the energy sector, this fact can be corroborated with the reduction of energy consumption, thus increasing economic efficiency. On the one hand, improving energy efficiency contributes to increasing the quality of life, productivity, and, implicitly, the economy, but on the other hand, it leads to excess energy use – this behavioral change is known as rebound. The research estimates the rebound effect at the macroeconomic level for European countries in the period 2000–2019, referring the analysis to each country's gross domestic product (GDP) and energy consumption, as well as comparing the preaccession and postaccession periods of Romania in the EU space. The rebound effect is determined using multidimensional analysis methods, depending on the GDP of each country as well as the behavior of each in the use of energy resources in industry, agriculture, and services. Although the study results confirm the strong link between energy consumption and GDP at the level of each state, they did not show considerable changes between countries at the level of the two periods.

Purpose — The chapter assesses the linkages between unreported economic activities and different individualistic and non-individualistic motives as perceived by firm management.Design/methodology/approach — The empirical research is based on a survey of the management of firms operating in the Baltic States. The survey contains information on the perceived extent of unreported activities and on a large number of firm-, sector-, and country-specific factors. A principal component analysis identifies clusters of motives for unreported activity. Regression analyses ascertain the importance of motives individually and as principal components on the extent of unreported activities.Findings — Both individualistic and non-individualistic motives are important for the prevalence of unreported activities. The individualist motives refer to the management being solely profit-oriented and self-interested. Among possible non-individualist motives, measures of government performance and perceptions of reciprocity towards the government appear to play important roles for the extent of unreported activities, but broader societal norms may also play a role.Research limitations/implications — The study considers the perceptions that managers have of unreported activities and other features. These perceptions are subjective and subject to substantial uncertainty. All results should be interpreted in light of the subjective nature of the survey answers.Social implications — Taken literally, the results suggest that stronger government performance is associated with a reduction in unreported activities, at least as perceived by the management. Broader societal developments may also be of importance.Originality/value — The inclusion of variables capturing individualistic as well as non-individualistic motives gives a comprehensive picture of factors behind unreported activities. We employ principal component analysis which allows us to cluster individual survey answers and to produce composite measures of different explanatory factors.

This chapter aims to examine the influence of governance on entrepreneurship in several countries, members of the European Union, in 2012–2017. The selection of the countries was based on human development index and expected years of schooling criteria, thus considering several sustainable development goals, involving the governments’ roles, the private sector, civil society, and citizens. The empirical analysis consisted of correlations, principal component analysis, and regression models. The Pearson correlation coefficient evidenced a series of negative relationships, statistically significant, between the governance indicators and business demography. The principal component analysis returned two main components for our database: the main one incorporates five governance proxies (control of corruption, rule of law, regulatory framework, government effectiveness, and political stability), while the second component is based on the voice and accountability. Therefore, the first governance component is more related to the public sector, while the second one reflects the involvement of civil society. The regression analysis considered besides the ordinary least squares model, the fixed effects and random effects model to emphasize whether or not differences across countries would impact the regression results. Several entrepreneurship variables were employed as dependent variables: business demography, high growth enterprise rates by employment, employer enterprise net growth, and business demography by size class. The consistent regression results emphasized an indirect impact from public governance toward high growth enterprise rates by employment and employer enterprise net growth. Based on our findings, the main conclusion is that public policies do not support entrepreneurship or the national context for entrepreneurs’ development. Moreover, the citizens’ involvement and their opportunities to participate in public decisions in terms of supporting entrepreneurship are also limited.

This chapter explores the yield curve movements in the interest rate swap markets of four major currencies, the Japanese yen (JPY), the US dollar (USD), the pound sterling (GBP), and the Swiss franc (CHF), by principal component analysis (PCA), focusing on the explanatory power of each driver. Comparing the cumulative proportions of the first three principal components, the “level” changes seem to explain the yield curve movements far better than the “ratio” changes in the case of the JPY (96.1% vs. 38.3%) and CHF (97.2% vs. 41.9%), and they are only marginally worse for the USD (97.7% vs. 98.5%) and GBP (96.5% vs. 98.3%). In all markets, the explanatory power (proportion) of the first PC (PC1) is over 82%, and most of the movements can be explained by it. Furthermore, the explanatory power (cumulative proportion) from PC1 up to the third PC (PC3) is over 96%. Thus, it can be considered that most of the movements can be explained by the first three PCs. In addition, we investigate whether there is a structural change in yield curve movements before and after the global financial crisis of 2007–2008 (GFC). If we use daily “level” changes for the PCA, the GFC has no impact on the yield curve movements for all major currencies. The three PCs retain good explanatory power.

This chapter measures stock market liquidity with three low-frequency liquidity estimators, and investigates the long-term behaviour of commonality in liquidity on the Shanghai Stock Exchange (SSE) through principal component analyses and panel regressions. The findings provide strong evidence of liquidity co-movement on the SSE from its inception to the present. The extent of commonality in liquidity varies significantly over time. Remarkably, it surged in 2007, which corresponds to the onset of the subprime mortgage-triggered financial crisis; however, the subsequent behaviour is divergent among our different liquidity proxies.

Aim: This chapter aims to examine the connection between government policies and entrepreneurial dimensions present in 13 European Union member countries, over the period 2002–2019. As long as the policies represent a set of decisions and actions issued by state-run structures, bodies with political, legislative, and financial authority to act to deal with a matter of public interest, this study overviews how intervention channels and policy instruments act upon supporting entrepreneurship.

Method: The methodology employed consists of correlations, principal component analysis (PCA), and regression models, in order to emphasise the statistically significant relationships between governance indicators and several entrepreneurial dimensions (financing for entrepreneurs, taxes and bureaucracy, basic school entrepreneurial education and training), and also the robustness of the results.

Results and Discussion: After observing the correlations evidencing strong relationships between the governance indicators, the results from PCA returned two main components for the Worldwide Governance Indicators: one incorporates the direct effect of control of corruption, government effectiveness, voice and accountability, regulatory quality, and rule of law, while the second component is based on political stability and absence of violence/terrorism factor. Results proved that governance has a significant impact on the financing available for entrepreneurs, especially from the first principal component, while taxes and regulations applied to new businesses have more impact in supporting entrepreneurship in countries with lower political stability levels. The consistent regression results emphasised that entrepreneurs feel more support from an institutional environment and more financing opportunities in an economy characterised by good governance, and taxes and regulations applied to new businesses have more impact in supporting entrepreneurship in countries with lower political stability levels.

Originality/Value: This study contributes to the literature studying the role of government policies on economic growth, by bringing more insights on the governance aspects and policies which are more favourable to productive entrepreneurship.