A synthesis of pure demand analysis

Abstract

§ 1. Summary and comments. Since Augustin Cournot, the pure theory of economic equilibrium consists in a separate analysis of demand and supply, the chief problem being- to prove the existence of a price constellation for which demand equals supply. In the theory of demand involved, it is a fundamental principle that demand is uniquely determined by income and prices. This principle is the central theme of the present paper.     

An economic analysis of mixing wastes

Using a general equilibrium model with heterogeneous waste, this paper studies optimal waste policy when households have to exert separation effort to produce homogeneous waste streams suitable for recycling. Our model explicitly allows for changes in the composition (quality) of waste streams depending on how much effort households are willing to spend on separating different types of waste. Accordingly, we are able to generalize some earlier contributions to the waste management literature and demonstrate that with both mixing and effort included, a first-best optimum is feasible under reasonable conditions. In particular, we find that a (modified) deposit-refund system still provides the optimal incentives to guide recycling as well as legal disposal (landfilling) and illegal dumping. Both the number and level of taxes and subsidies needed to reach the first-best depend crucially on the socially optimal level of dumping as well as the socially optimal composition of the mix. We are grateful to two anonymous referees, Leon Bettendorf, Elbert Dijkgraaf, Don Fullerton and Matthieu Glachant for constructive comments and useful discussions on an earlier version of this paper.     

A class of adaptive importance sampling weighted EM algorithms for efficient and robust posterior and predictive simulation

A class of adaptive sampling methods is introduced for efficient posterior and predictive simulation. The proposed methods are robust in the sense that they can handle target distributions that exhibit non-elliptical shapes such as multimodality and skewness. The basic method makes use of sequences of importance weighted Expectation Maximization steps in order to efficiently construct a mixture of Student-t$t$ densities that approximates accurately the target distribution–typically a posterior distribution, of which we only require a kernel–in the sense that the Kullback–Leibler divergence between target and mixture is minimized. We label this approach Mixture of  t$t$by Importance Sampling weighted Expectation Maximization (MitISEM). The constructed mixture is used as a candidate density for quick and reliable application of either Importance Sampling (IS) or the Metropolis–Hastings (MH) method. We also introduce three extensions of the basic MitISEM approach. First, we propose a method for applying MitISEM in a sequential manner, so that the candidate distribution for posterior simulation is cleverly updated when new data become available. Our results show that the computational effort reduces enormously, while the quality of the approximation remains almost unchanged. This sequential approach can be combined with a tempering approach, which facilitates the simulation from densities with multiple modes that are far apart. Second, we introduce a permutation-augmented MitISEM approach. This is useful for importance or Metropolis–Hastings sampling from posterior distributions in mixture models without the requirement of imposing identification restrictions on the model’s mixture regimes’ parameters. Third, we propose a partial MitISEM approach, which aims at approximating the joint distribution by estimating a product of marginal and conditional distributions. This division can substantially reduce the dimension of the approximation problem, which facilitates the application of adaptive importance sampling for posterior simulation in more complex models with larger numbers of parameters. Our results indicate that the proposed methods can substantially reduce the computational burden in econometric models like DCC or mixture GARCH models and a mixture instrumental variables model.     

Living Labs as an Approach to Strengthen Agricultural Knowledge and Innovation Systems

Climate and ecosystem changes, economic and policy imperatives, food system pressures, and multiple societal expectations pose complex challenges for sustainable farming. A key problem is determining an effective and efficient approach to enable innovation in complex, multi-stakeholder settings. One approach currently receiving much policy attention and investment in the EU is ‘Living Labs’ which bring together public and private stakeholders to co-create, validate, and test new services, business ideas, markets and technologies. However, the analysis, monitoring and evaluation of Living Labs and their effectiveness and policy value in different contexts is limited. The AgriLink Living Labs were undertaken between 2018 and 2021 in Italy, Latvia, The Netherlands, Norway, Romania and Spain to co-create innovation support for improving agricultural sustainability. Our evaluation suggests that four inter-related critical conditions are needed for the success of Living Labs. These relate to the complexity of the challenge, the enabling environment, the proficient facilitation and the energy to move. These conditions influence the functioning of the Living Lab and we discuss the implications for policymakers and practitioners for the deployment of Living Labs in agricultural settings.     

Modeling complex network patterns in international trade

Review of World Economics - This paper examines the role of complex network patterns in determining international trade. The author proposes two empirical approaches to better identify the...     

Correction to: Modeling complex network patterns in international trade

Review of World Economics -