Time-varying combinations of predictive densities using nonlinear filtering

We propose a Bayesian combination approach for multivariate predictive densities which relies upon a distributional state space representation of the combination weights. Several specifications of multivariate time-varying weights are introduced with a particular focus on weight dynamics driven by the past performance of the predictive densities and the use of learning mechanisms. In the proposed approach the model set can be incomplete, meaning that all models can be individually misspecified. A Sequential Monte Carlo method is proposed to approximate the filtering and predictive densities. The combination approach is assessed using statistical and utility-based performance measures for evaluating density forecasts of simulated data, US macroeconomic time series and surveys of stock market prices. Simulation results indicate that, for a set of linear autoregressive models, the combination strategy is successful in selecting, with probability close to one, the true model when the model set is complete and it is able to detect parameter instability when the model set includes the true model that has generated subsamples of data. Also, substantial uncertainty appears in the weights when predictors are similar; residual uncertainty reduces when the model set is complete; and learning reduces this uncertainty. For the macro series we find that incompleteness of the models is relatively large in the 1970’s, the beginning of the 1980’s and during the recent financial crisis, and lower during the Great Moderation; the predicted probabilities of recession accurately compare with the NBER business cycle dating; model weights have substantial uncertainty attached. With respect to returns of the S&P 500 series, we find that an investment strategy using a combination of predictions from professional forecasters and from a white noise model puts more weight on the white noise model in the beginning of the 1990’s and switches to giving more weight to the professional forecasts over time. Information on the complete predictive distribution and not just on some moments turns out to be very important, above all during turbulent times such as the recent financial crisis. More generally, the proposed distributional state space representation offers great flexibility in combining densities.     

Testing the convergence hypothesis: a longitudinal and cross-sectional analysis of the world trade web through social network and statistical analyses

Different theoretical perspectives support opposite views on convergence: although the dominant view is that convergence is the inevitable outcome of globalization, divergentists (that is, world-system economists and, potentially, also evolutionary geographic ones) argue that convergence forces could be annihilated by the need to keep power relationships within the international division of labor. Even when limiting the convergence issue to international trade, the debate has so far been inconclusive, because various studies have dealt with different and/or short time series or selected too small and different sets of countries. Moreover, none of these studies have analyzed trade patterns and have instead been limited to the aggregate value. Here, through a social network analysis, we examine the world trade patterns from 1980 to 2016 (1980–1992, 1993–2007 and 2008–2016) of at least 164 countries, which have been divided into import and export patterns and into four groups of countries: from core countries to far periphery ones. We test the convergence hypothesis in two directions: the level and trend of convergence, and its possible determination by means of structural or economic globalization, measured in terms of exchanges density and economic values, respectively. We have found that the convergence hypothesis only seems to be confirmed when considering the pure structural aspect and core countries. Conversely, economic convergence—which also includes the structural dimension—has been found to be high for core countries and to increase over time. Moreover, our analysis shows that economic globalization influences convergence, albeit in a strongly negative way. Therefore, our findings seem to support divergentists and the convergence hypothesis should be rejected.

     

Predicting turning points in the housing market

We identify leading indicators of changes in the housing market and compare their performance in predicting turning points. Being able to predict turning points is of importance to the home building industry, homeowners, and makers of housing policy. Our leading indicators include the Wells Fargo/NAHB Housing Market Index, two of its forward looking components, and an index of consumer sentiment regarding purchasing a home. Our comparison tests include Granger causality and a Bayesian predictor of the probability of a turning point. We find that the measure of consumer sentiment performs relatively well compared to the HMI in predicting home permits, housing starts, and new home sales.     

A panel data heterogeneous Bayesian estimation of environmental Kuznets curves for CO2 emissions

This article investigates the Environmental Kuznets Curves (EKC) for CO₂ emissions in a panel of 109 countries during the period 1959 to 2001. The length of the series makes the application of a heterogeneous estimator suitable from an econometric point of view. The results, based on the hierarchical Bayes estimator, show that different EKC dynamics are associated with the different sub-samples of countries considered. On average, more industrialized countries show evidence of EKC in quadratic specifications, which nevertheless are probably evolving into an N-shape based on their cubic specification. Nevertheless, it is worth noting that the EU, and not the Umbrella Group led by US, has been driving currently observed EKC-like shapes. The latter is associated to monotonic income–CO₂ dynamics. The EU shows a clear EKC shape. Evidence for less-developed countries consistently shows that CO₂ emissions rise positively with income, though there are some signs of an EKC. Analyses of future performance, nevertheless, favour quadratic specifications, thus supporting EKC evidence for wealthier countries and non-EKC shapes for industrializing regions.     

Assessing techno-economic alternatives for rural development in Northeast Mexico

In the arid zone of Northeast Mexico are located some of the most socially deprived and economically marginal communities in the nation. This paper analyzes the current status of their social and economic organization and suggests alternative techno-economic subsistence activities that could improve productive capacity while preserving the fragile ecological balance between communities and exploitable natural resources. It shows how recent advances in computer mapping, linear programming, and techno-social modelling (SOPA technique) can assist decision makers and planners in choosing the best mix of social, economic, and technical advantages from an array of scenarios of change. A major finding is that the use of cooperatives as a managerial format is not always the best one to utilize, given the variability of community cohesiveness and varying organizational requirements of different techniques of exploiting animal and plant resources.     

Model risk in credit risk

We provide sharp analytical upper and lower bounds for value‐at‐risk (VaR) and sharp bounds for expected shortfall (ES) of portfolios of any dimension subject to default risk. To do so, the main methodological contribution of the paper consists in analytically finding the convex hull generators for the class of exchangeable Bernoulli variables with given mean and for the class of exchangeable Bernoulli variables with given mean and correlation in any dimension. Using these analytical results, we first describe all possible dependence structures for default, in the class of finite sequences of exchangeable Bernoulli random variables. We then measure how model risk affects VaR and ES.     

Judicial Efficiency and Immigrant Entrepreneurs

Extensive research exists on the individual determinants of being an immigrant entrepreneur, concerning both social environment and human capital. However, the role of the judiciary has not been investigated yet. Analyzing more than 160,000 new micro enterprises owned by immigrants, our paper aims to fill this gap by focusing on the relation between justice and immigrant entrepreneurship. Results show that judicial efficiency is one of the determinants of self‐employment, although some differences among immigrant groups are identified. Therefore, the study confirms the key role of judicial enforcement in promoting not only growth but also the integration of these new citizens.     

A study on the attributes involved in the hedonic pricing of Brazilian clothing

The objective of this study is to identify the weighted importance of intrinsic and extrinsic attributes. Intrinsic attributes are defined as the specific features of products. Extrinsic attributes refer to other characteristics that are also important for price composition, such as brand, store layout and services, and purchasing experiences. In a survey with 1,923 collected responses, it was possible to identify and quantify intrinsic and extrinsic influencing factors beyond price strategy in the Brazilian fashion market for blue jeans. The statistical analysis was based on the hedonic price method. The idea of expressing the price based on a series of intrinsic and extrinsic variables avoids the problem of using the regression technique. In this research, multiple linear regression and quantile regression were applied. The results show that extrinsic attributes have greater influence than intrinsic features on explaining the final market prices using log-linear and quantile regression statistical methods.     

“Made in Italy”: The Italian Contribution to the Development of Business Marketing Discipline and Practices

Purpose: The authors analyze the main contributions of Italian literature in the business-to-business field as it relates to the characteristics of the Italian industrial manufacturing system. Even if not directly labeled as business-to-business marketing, Italian and other scholars who have studied Italian business experiences have largely faced the topic of business marketing management with innovative approaches and distinctive benchmark examples. In particular, the analysis of the Italian contribution to business marketing relies on the specific nature of the Italian industrial structure as recognized by the international literature. In this respect, the authors argue that three main areas of interest progressively arose from the peculiarities of the Italian industrial system: industrial districts, subcontractors/subcontracting relationships, and mid-sized manufacturing companies. These three research topics have been thoroughly investigated by both Italian and international scholars. They analyze the contributions that Italian researchers provided to advance business marketing discipline and practices.

Methodology/approach: The authors reviewed the literature of the main contributions developed in the three individual areas of research that have a strong focus on the Italian experience. The methodology was in three steps. First, the review planning process was guided by the three areas of research that were assumed to have a major contribution to business marketing discipline. Second, the SCOPUS database was used as a primary source for paper identification by using a keywords search method. Third, the dataset was refined by excluding non relevant contributions (not focused on business management and marketing or not specifically dealing with the three areas of interest) and validating/integrating the original dataset by using other complementary sources (e.g., Google Books, Google Scholar). Finally, a set of 268 products were taken into account in the analysis.

Findings: The results of the analysis suggest that the Italian contribution to the field of business marketing is valuable in light of the three chosen areas of research. These three empirical arenas—industrial districts, subcontractors, and mid-sized firms—contribute to providing a better understanding of business market structure and dynamics as well as the improvement of business marketing studies.

Originality/value: The article is an original and first attempt to elaborate and reflect on the contribution of the Italian business experience literature to the advances of business marketing discipline. The authors develop an original comprehensive review of “Made in Italy” business marketing and management research that has not been used in previous studies. The review will increase international knowledge of Italian practices and theoretical frameworks that can contribute to the reinforcement of the business-to-business marketing discipline.     

Innovation and University‐Firm R&D Collaboration in the European Food and Drink Industry

In the National Innovation System (NIS), knowledge is produced and accumulated through interactive innovation processes that are embedded in a national context, which in turn may help determine innovation. This paper investigates how product and process innovations in the European food and drink industry are affected by: (i) NIS structure; (ii) NIS output in terms of scientific publications and the supply of graduates; (iii) NIS cohesion and coordination; (iv) NIS scientific impact and specialisation. The main source of data on innovation by firms is the EU‐EFIGE/Bruegel‐UniCredit dataset. This is supplemented by information from the International Handbook of Universities, Eurostat and a bibliometric analysis of academic research output. Our results suggest that large research institutions in the public sector may well be detrimental to interaction between university and industry and to process innovation. The indicators used for public research assessment are not necessarily the most appropriate proxies of local knowledge spillovers.