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This study aims to evaluate the effectiveness of national and international R&D support programmes on firms’ technology scouting, defined as firms’ use of external knowledge sources.

Drawing on a unique data set on R&D support programmes for small and medium-sized enterprises (SMEs) operating in both manufacturing and service sectors across 28 European countries, this study reports treatment effects estimated by the copula-based endogenous switching model, which takes into account unobserved firm heterogeneity.

Empirical results indicate that R&D support programmes have heterogeneous effects on technology scouting. In particular, a crowding-out effect arises in the case of informal sources of external knowledge, whereas additional effects are reported for formal, strategic sources.

For informal sources of external knowledge, a random distribution of R&D measures would have a substantially larger effect rather than using current selection criteria.

To the best of the authors’ knowledge, this is the first study to explore the policy effects on technology scouting applying a copula-based endogenous switching model. Most cross-sectional empirical studies use matching estimators, although their main disadvantage is the selection on observables.

This paper aims to assess all the Brazilian electric power transmission line auctions occurred between 1999 and 2017.

A copula-based Roy/endogenous switching regression model is used. The suitability of this model is twofold: it takes into account the selection bias problem involving auctions data and it allows more flexibility in modeling the joint distribution between the unobserved components of the selection and outcome equations; thus, normal distribution assumptions are not needed.

The main results suggest that stated-owned companies have the highest probability of winning an auction, and there is a non-competitive behavior among the players in the auction. The results also suggest some departure from joint normality in the data.

The copula-based sample selection approach used in this paper is consistent under non-normality and allows one to address different types of nonlinearities in the data such as asymmetry and heavy tails.

We discuss a class of copula-based ordered probit models with endogenous switching. Such models can be useful for the analysis of self-selection in subjective well-being equations in general, and job satisfaction in particular, where assignment of regressors may be endogenous rather than random, resulting from individual maximization of well-being. In an application to public and private sector job satisfaction, and using data on male workers from the German Socio-Economic Panel for 2004, and using two alternative copula functions for dependence, we find consistent evidence for endogenous sector selection.

Portfolio construction and diversification is a prominent challenge for investors. It reflects market agents’ behavior and response to market conditions. This paper aims to investigate the stock-bond nexus in the case of two emerging and two mature markets, India, South Africa, the UK and the USA, using long-term historical monthly data.

To address the issue at hand, copula quantile-on-quantile regression (C-QQR) is used to model the correlation structure. Although this technique is driven by copula-based quantile regression model, it retains more flexibility and delivers more robust and accurate estimates.

Results suggest that there is substantial heterogeneity in the bond-stock returns correlation across the countries under study point to different investors’ behavior in the four markets examined. Additionally, the findings reported herein suggest that using C-QQR in portfolio management can enable the formation of tailored response strategies, adapted to the needs and preferences of investors and traders.

To the best of the authors’ knowledge, no previous study has addressed in a comparative setting the stock-bond nexus for the four countries used here using long-term historical data that cover the periods 1920:08-2017:02, 1910:01-2017:02, 1933:01-2017:02 and 1791:09-2017:02 for India, South Africa, the UK and the USA, respectively.

In the face of growing demand for care and tightening resource constraints, hospitals need to ensure access to care that is affordable and effective. Yet, the multiplicity of objectives is a key challenge in this industry. An understanding of the interrelationships (tradeoffs) between the multiple outcome objectives of care (throughput, experiential and financial performance) and returns to operational inputs (diversification of care) is fundamental to improving access to care that is effective and affordable. This study serves to address this need.

The empirical analysis in the study builds on an output-oriented distance function model and uses a longitudinal panel dataset from 153 hospitals in California.

This study results point to key insights related to output–output tradeoffs along the production frontier. Specifically, the authors find that higher throughput rates may lead to significantly lower levels of experiential quality, and net revenue from operations, accounting for the clinical quality of care. Similarly, the authors’ findings highlight the resource intensity and operational challenges of improving experiential quality of care. In regards to input–output relationships, this study finds diversification of care is associated with increased throughput, improvements in service satisfaction and a corresponding increase in the net revenue from operations.

Highlighting the tradeoffs along the production frontier among the various outcomes of interest (throughput, experiential quality and net revenue from operations), and highlighting the link between diversification of care and care delivery outcomes at the hospital level are key contributions of this study. An understanding of the tradeoffs and returns in healthcare delivery serves to inform policy-making with key managerial implications in the delivery of care.

This study aims to investigate the impact of the COVID-19 pandemic on the time-frequency connectedness between green bonds, stock markets and commodities (Brent and Gold), with a particular focus on China and its implication for portfolio diversification across different frequencies.

To this end, the authors implement the frequency connectedness approach of Barunik and Krehlik (2018), followed by the network connectedness before and during the COVID-19 outbreak. In particular, the authors implement more involvement in portfolio allocation and risk management by estimating hedge ratios and hedging effectiveness for green bonds and other financial assets.

The time-frequency domain spillover results show that gold is the net transmitter of shocks to green bonds in the long run, whereas green Bonds are the net recipients of shocks, irrespective of time horizons. The subsample analysis for the pandemic crisis period shows that green bonds dominate the network connectedness dynamic, mainly because it is strongly connected with the SP500 index and China (SSE). Thus, green bonds may serve as a potential diversifier asset at different time horizons. Likewise, the authors empirically confirm that green bonds have sizeable diversification benefits and hedges for investors towards stock markets and commodity stock pairs before and during the COVID-19 outbreak for both the short and long term. Gold only offers diversification gains in the long run, while Brent does not provide the desired diversification gains. Thus, the study highlights that green bonds are only an effective diversified.

This study contributes to the existing literature by improving the understanding of the interconnectedness and hedging opportunities in short- and long-term horizons between green bonds, commodities and equity markets during the COVID-19 pandemic shock, with a particular focus on China. This study's findings provide more implications regarding portfolio allocation and risk management by estimating hedge ratios and hedging effectiveness.

The purpose of this paper is to obtain a comprehensive structure of past empirical studies on financial contagion which can provide the present growth and future scope of research work on the field of contagion analysis.

Present study identifies 151 empirical studies on financial contagion and summarises all the studies on the basis of tools and methodology used, year of the studies, origin of the studies, sample period and sample countries taken, studies undertaken on the basis of different crisis period and markets considered and finally sources of the studies.

The results of the analysis show that the empirical studies on contagion increased continuously over the past five years. Higher order test of contagion with more number of sample countries may provide more accurate picture on financial contagion.

This paper collects, classifies and summarises past empirical studies on financial contagion and provides valuable conclusion on present growth and future scope of studies on financial contagion. The information given in this paper can be helpful for future researchers and academicians on this particular field; the summary of the conclusion (from past reviews) may be helpful for the policy makers for asset allocation and risk management.

The authors examine cryptocurrency market behavior using a hidden Markov model (HMM). Under the assumption that the cryptocurrency market has unobserved heterogeneity, an HMM allows us to study (1) the extent to which cryptocurrency markets shift due to interactions with social sentiment during a bull or bear market and (2) the heterogeneous pattern of cryptocurrency market behavior under these two market conditions.

The authors advance the HMM model based on two six-month datasets (from November 2017 to April 2018 for a bull market and from December 2018 to May 2019 for a bear market) collected from Google, Twitter, the stock market and cryptocurrency trading platforms in South Korea. Social sentiment data were collected by crawling Bitcoin-related posts on Twitter.

The authors highlight the reaction of the cryptocurrency market to social sentiment under a bull and a bear market and in two hidden states (an upward and a downward trend). They find: (1) social sentiment is relatively relevant during a bull compared to a bear market. (2) The cryptocurrency market in a downward state, that is, with a local decreasing trend, tends to be more responsive to positive social sentiment. (3) The market in an upward state, that is, with a local increasing trend, tends to better interact with negative social sentiment.

The proposed HMM model contributes to a theoretically grounded understanding of how cryptocurrency markets respond to social sentiment in bull and bear markets through varied sequences adjusted for cryptocurrency market heterogeneity.

This paper examined the factors influencing the participation of rice processors in short supply chains and the participation impact on the amount of rice processed, per capita expenditure of household and value of sales.

The Seemingly Unrelated Regression and Doubly Robust Augmented Inverse Probability Weighting Model (AIPW) were used to analyse the determinants of short supply chain participation and the impact of short supply.

From the results, the mean value of rice processed was GH₵18385 (US$ 3,069.28), with the minimum value being GH₵ 25 (US$ 4.17) and the maximum GH₵ 67200 (US$ 1,1218.70) per annum. Processed rice aroma and grade characteristics positively influence the value of processed rice sold via short supply chains as well as the expertise rate of the processor, Farmer-Based Organisation membership, and marketing information availability. Women rice processors' per capita expenditure, total sales value and the value of processed rice was positively influenced by the short supply chain participation.

Even though the sample size was appropriate, a larger sample size could further support the study's finding since a limited geographical area with predominant domestic rice processors was studied. Again, future studies should consider behavioural theories, such as the Theory of Planned Behaviour, amongst others, in understanding the reasons for the choices of short supply chains compared to other sales outlets.

Although there is a growing body of literature on rice, most of the studies focussed on the marketing outlet of rice producers, rice processing, constraints and opportunities faced by rice farmers and processors and an out-grower scheme involving rice processors amongst rice producers with none of these on the choice of short supply chains amongst women processors. Also, amongst all the studies on rice producers, none applied a theory; however, the Women in Development (WID) Theory was used to analyse the impact of the short supply chain on the impact on household per capita expenditure (poverty), the value of sales and amount of rice processed, a modest theoretical contribution of the paper to literature.

We consider the Bayes estimation of a multivariate sample selection model with p pairs of selection and outcome variables. Each of the variables may be discrete or continuous with a parametric marginal distribution, and their dependence structure is modeled through a Gaussian copula function. Markov chain Monte Carlo methods are used to simulate from the posterior distribution of interest. The methods are illustrated in a simulation study and an application from transportation economics.