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Research and development (R&D) is increasingly considered to be a key driver of economic growth. The relationship between these variables is commonly examined using linear models and thus relies only on single-point estimates. Against this background, this paper provides new evidence on the impact of R&D on economic growth using a machine learning approach that makes it possible to go beyond single-point estimation.

The authors use the kernel regularized least squares (KRLS) approach, a machine learning method designed for tackling econometric models without imposing arbitrary functional forms on the relationship between the outcome variable and the covariates. The KRLS approach learns the functional form from the data and thus yields consistent estimates that are robust to functional form misspecification. It also provides pointwise marginal effects and captures non-linear relationships. The empirical analyses are conducted using a sample of 101 countries over the period 2000–2020.

The estimates indicate that R&D expenditure and high-tech exports positively and significantly influence economic growth in a non-linear manner. The authors also find a positive and statistically significant relationship between economic growth and greenhouse gas emissions. In both cases, the effects are higher for upper-middle-income and high-income countries. These results suggest that a substantial effort is needed to green economic growth. Internet access is found to be an important factor in supporting economic growth, especially in high-income and middle-income countries.

This paper contributes to underlining the importance of investing in R&D to support growth and shows that the disparity between countries is driven by the determinants of economic growth (human capital in R&D, high-tech exports, Internet access, economic freedom, unemployment rate and greenhouse gas emissions). Moreover, since the authors find that R&D expenditure and greenhouse gas emissions are positively associated with economic growth, technological progress with green characteristics may be an important pathway for green economic growth.

This paper uses an innovative machine learning method to provide new evidence that innovation supports economic growth.

The purpose of this paper is to explain how eventual pressures from national lobbies may lead governments to shift from an optimal into a non‐optimal innovation policy.

A theoretical model is developed in order to examine and explain the growth and welfare effects of optimal and non‐optimal innovation policies. The non‐optimal policy corresponds to a subsidy for national innovators that is equivalent to an optimal policy of incentives (tax cuts) to foreign investors. Since we are assessing what can nationals do with the support that could be oriented to foreign firms, we are measuring what the economy loses for not supporting foreign firms.

The authors find welfare loss when supporting national R&D instead of foreign R&D and conclude that the same support given to innovation can produce strikingly different outcomes depending on who receives the support.

The analysis allows the impact of the inefficiency caused by policies that are not sound, from a strictly economic point of view, to be measured.

The originality of the paper is related to the assessment of the implications and to the measurement of the effects of non‐optimal R&D policies.

The purpose of this paper is to compare investment in innovation (e.g. R&D) between new venture start‐ups before commercialization and operating businesses after commercialization.

Real options methods were used to model a new venture start‐up as a perpetual call option on an operating business that grows with R&D. The operating business uses R&D to improve actual earnings while the start‐up uses R&D to improve prospective earnings. When the start‐up entrepreneur commercializes his/her new product, device, or service with conventional investment (e.g. plant, property, and equipment to begin production), prospective earnings convert into actual earnings.

The ability of the start‐up entrepreneur to avoid commercialization costs upon failed R&D makes R&D more valuable to the start‐up entrepreneur than to the manager of the already operating business (for whom commercialization costs are sunk) and despite R&D costs that the start‐up incurs without the revenues that only commercialization generates. The value of R&D to the start‐up can be so great that the entrepreneur invests in R&D before the manager of an otherwise similar operating business in similar business conditions.

Without favoring either a priori, the authors show that under broad circumstances, a new venture start‐up undertakes R&D before an already operating business. The authors also discuss the empirical implications of the results.

The paper proposes using modified duration in calculating the proper risk-adjusted discount rate (RADR) to account for downside risk scenarios in capital budgeting.

The paper shows how to use modified duration to summarize in a single number the bidimensional information about the inflows and terms in which they are charged in the use of the RADR. If a short modified duration characterizes the project, that is, the most relevant inflows are charged in short times, then discounting at RADR has mild effects on net present value (NPV). Else, if a long modified duration characterizes the project, discounting at RADR may have severe effects on NPV. The study proves that RADR's effectiveness increases with the project's modified duration.

The study builds a bridge between the regular NPV method used in academia and the RADR method used in the managerial context by identifying the proper RADR that leads the same NPV risk-adjustments, whichever method is used by including modified duration into the analysis.

The results show how to select the proper RADR by reducing the subjectivity and increasing financial precision based on modified duration, thus providing an alternative to the norm. Simulations are used to make sensitivity analysis more effective and spotlight the main drivers in the risk-adjustments providing robust results.

This paper fulfils the gap between the RADR method and the expected net present value method by providing simple relations between the characteristic parameters.

Recently, Stein et al. (2016) studied theoretical properties and parameter estimation of continuous time processes derived as solutions of a generalized Langevin equation (GLE). In this paper, the authors extend the model to a wider class of memory kernels and then propose a bond and bond option valuation model based on the extension of the generalized Langevin process of Stein et al. (2016).

Bond and bond option pricing based on the proposed interest rate models presents new difficulties as the standard partial differential equation method of stochastic calculus for bond pricing cannot be used directly. The authors obtain bond and bond option prices by finding the closed form expression of the conditional characteristic function of the integrated short rate process driven by a general Lévy noise.

The authors obtain zero-coupon default-free bond and bond option prices for short rate models driven by a variety of Lévy processes, which include Vasicek model and the short rate model obtained by solving a second-order Langevin stochastic differential equation (SDE) as special cases.

Bond and bond option pricing plays an important role in capital markets and risk management. In this paper, the authors derive closed form expressions for bond and bond option prices for a wider class of interest rate models including second-order SDE models. Closed form expressions may be especially instrumental in facilitating parameter estimation in these models.

Good management strives to align and corporate processes for more attention being paid to supply chain management. Firms realize that greater co-operation and improved coordination can help to manage the entire supply chain more efficiently. The imperfect quality item is one of the most important issues that affect the expected profit of green supply chain. The imprecise cost with screening process of poor quality items posed in supply chain is the subject of this study.

The present study explores production model for imperfect items having uncertain cost parameters with three-layer supply chain encompassing supplier, manufacturer and retailer. The model is considering the impact of business tactics such as order size, production rate, production cost and appropriate times in various sectors on collaborative marketing systems. Due to imprecise cost parameters, the pentagonal fuzzy numbers are set to fuzzify the total cost and defuzzifition by using graded mean integration.

This study offers an explicit condition in uncertain environment to manage the imperfect quality item to increase the potential profit of the supply chain. The influence of changes in parameter values on the optimal inventory policy under fuzziness is provided managerial insights.

This model makes a significant contribution to fuzzy inference. The results of the study provide a trading strategy for the industry to avoid losses. The prescribed study can be suitable for the industries like sculpture, jewelry, pottery, etc.

Channel coordination has become an essential part of researching hotel supply chain management practices. This paper develops an improved channel coordination approach to coordinate the profit distribution between hotels and online travel agencies (OTAs) achieved through an introduction of advertising fees. This direction further improves the decentralization of cooperation and achieves Pareto improvement to achieve mutual profitability.

The methodology used in this study involves Stackelberg game theory employed for the decision-making and analysis of both the hotel and OTA. The OTA, acting as the leader, offers a hotel a contract specifying the commission rate that the hotel will pay to the respective OTA. The hotel, acting as a follower, sets a self-interested room rate as a given response. A deterministic, price-sensitive linear demand function is utilized to derive possible analytical solutions once centralized, noncooperative decentralization and cooperative decentralized channel occurs.

Results show that a new channel coordination approach is possible, namely via advertising fees. Prior to channel coordination, the OTA tends to set a higher commission rate, and the hotel sets a higher room rate in response under noncooperative decentralization. As such, this results in a lower channel-wide profit for all. One way to reduce channel-wide profit loss is to use a method of cooperative decentralization, which can, and will result in optimal profit as centralization takes place. However, the lack of incentives makes cooperative decentralization unfeasible. Further improvement is possible by using advertising fees based on a cooperative decentralization agreement, which can reach Pareto improvement.

This paper helps the OTA industry and hotel owners cooperate by way of smoother coordination. This study provides practitioners with two important practical implications. The first one is that the coordination between the hotel industry and OTA through cooperative decentralization allows for the achievement of higher profitability than that of noncooperative decentralization. The second one is that this paper solves the outstanding problem of insufficient incentives characteristic of cooperative decentralization by means of an advertising fee as a new supply chain coordination approach.

This paper offers both the problem and solution regarding the lack of incentives that hamper cooperative decentralization without the use of advertising fees. This paper is unique in that it derives analytical solutions regarding commissions levied in a typical hotel supply chain under noncooperative decentralization.

The purpose of this chapter is to analyze the impact of public agricultural Research and Development (R&D) investments on agricultural productivity and long-term food security to derive policy recommendations. The methodological approach is based on the application of the state-of-the art Computable General Equilibrium (CGE) model to R&D. By endogenizing R&D in global CGE models, it is possible to assess the impact of different public R&D policies on the food availability and food access of food security. This study found that R&D investments bring positive effects on the food access dimension of food security, particularly in places such as Sub-Saharan Africa where prices are expected to grow significantly by 2050, as agricultural land becomes scarcer and more expensive. Doubling the R&D intensity would soften the land constraints and substantially decelerate food prices, thus preventing the deterioration of living standards of rural households and leading to a gain in daily caloric consumption. The impact of alternative agricultural R&D policies on the various dimensions of food security has not been analyzed using a CGE framework, which enables capturing both the benefits and costs from R&D investments. Modeling the dynamic accumulation of R&D stocks makes it possible to analyze the effects of R&D on food security over time.

This paper attempts to reconcile an apparent contradiction between short‐run and long‐run movements in the price of gold. The theoretical model suggests a set of conditions under which the price of gold rises over time at the general rate of inflation and hence be an effective hedge against inflation. The model also demonstrates that short‐run changes in the gold lease rate, the real interest rate, convenience yield, default risk, the covariance of gold returns with other assets and the dollar/world exchange rate can disturb this equilibrium relationship and generate short‐run price volatility. Using monthly gold price data (1976–1999), and cointegration regression techniques, an empirical analysis confirms the central hypotheses of the theoretical model.