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A technique called dynamic programming can be used to identify an optimal rebalancing schedule, which significantly reduces rebalancing and sub-optimality costs. Dynamic programming provides solutions to multi-stage decision processes in which the decisions made in prior periods affect the choices available in later periods. Dynamic programming provides the optimal year-by-year decision policy by working backwards from year 10. The results of the test of the relative efficacy of dynamic programming and the MvD heuristic with data on domestic equities, domestic fixed income, non-US equities, non-US fixed income, and emerging market equities, show that the MvD heuristic performs quite well compared to the dynamic programming solution for the two-asset case and substantially better than other heuristics. The increase in the number of assets reduces the advantage of dynamic programming over the MvD heuristic and is reversed at the level of five assets. Dynamic programming cannot be applied beyond five assets, but the MvD heuristic can be extended up to 100 assets. The MvD heuristic reduces total costs relative to all of the other heuristics by substantial amounts. The performance of the MvD heuristic improves relative to the dynamic programming solution as more assets are added but this improvement reflects a growing reliance on an approximation for the dynamic programming approach.

This chapter introduces the Eurace@Unibi model, one of the agent-based simulation models that are relatively new additions to the toolbox of macroeconomists, and the research that has been done within this framework. It shows how an agent-based model can be used to identify economic mechanisms and how it can be applied to spatial policy analysis. The assessment is that agent-based models in economics have passed the proof-of-concept phase and it is now time to move beyond that stage. It has been shown that new kinds of insights can be obtained that complement established modeling approaches. The chapter concludes by pointing toward some potentially fruitful areas of agent-based macroeconomic research.

This chapter discusses a step in the evolution of agent-based model (ABM) research in finance. Agent-based modeling has concentrated on the development of stylized market models, which have been extremely useful for understanding how complex macro-scale phenomena emerge from micro-rules. In order to further develop ABMs from proof of concept into robust tools for policy makers, to control and forecast complex real-world financial markets, it is essential to permit agents to behave as active data-gathering decision makers with sophisticated learning capabilities. The main focus of this chapter is to show how agent based models (ABMs) in financial markets have evolved from simple zero- intelligence agents that follow arbitrary rules of thumb into sophisticated agents described by microfounded rules of behavior. The chapter then briefly looks at the challenges posed by and approaches to model calibration and provides examples of how ABMs have been successful at offering useful insights for policy making.

With the help of two examples, this chapter illustrates the usefulness of agent-based models as tools for economic policy design. The first example applies a financial market model in which the order flow of speculators, relying on technical and fundamental analysis, generates intricate price dynamics. The second example applies a Keynesian-type goods market model in which the investment behavior of firms, relying on extrapolative and regressive predictors, generates complex business cycles. It adds a central authority to these two setups and explores the impact of simple intervention strategies on the model dynamics. On the basis of these experiments, the chapter concludes that agent-based models may help us understand how markets function and evaluate the effectiveness of various stabilization policies.

The chapter reviews the literature concerning agent-based labor market models by tracing its roots to the microsimulation literature and surveying a selection of con- tributions made since the work by Bergmann and Eliasson et al. Agent-based models have been applied to explain stylized facts of labor markets as well as labor market policy evaluations. They also constitute a major part of agent-based macroeconomic models. Besides reviewing the various results achieved, the chapter discusses modeling choices with respect to agents' behavior and the structure of interaction. The overall assessment is that agent-based labor market models have given us valuable insights into the functioning of labor markets and the consequences of labor market policies, and that they will increasingly become an essential tool of analysis, in particular, when the construction of large macro-models is involved.

This article explores the tendency for poverty and conflict, as well as for prosperity and peace, to reinforce one another, examining two specific factors. One is the type of rents that adversaries may contest, as rents can differ in terms of the vulnerability of their value to conflict; more vulnerable rents tend to induce more peace, whereas less vulnerable rents have the opposite effect. The second factor concerns the relationship between the elites and the entrepreneurs in their respective groups—specifically, the extent to which the elites care about their entrepreneurs. While these two factors can predispose countries to either virtuous or vicious circles, multiple equilibria are also possible.

The chapter discusses algorithmic trading, which refers to any automated process, consisting of a number of interconnected components, whose main aim is to perform financial transactions of any kind. Its chief advantage lies in the fact that human intervention is minimized to an acceptable extent. This is quite desirable because nowadays numerous factors affect financial decisions. Financial managers are able to deal with a limited amount of information. There are many ways to implement algorithmic trading systems. This chapter aims to highlight the efficiency of biologically inspired methodologies when incorporated in such systems. Biologically inspired intelligence comprises a range of algorithms whose common philosophy is based on the behavior of real-world, natural systems and networks. What is more, the performance of the applied nature-inspired intelligence (NII) methodologies is compared to traditional benchmark approaches such as the random portfolio construction.

This article discusses the portfolio optimization with market impact costs, combining execution and portfolio risk, and dynamic portfolio analysis. A multi-period portfolio optimization model is proposed that incorporates permanent and temporary market impact costs, and alpha decay. There are five popular algorithmic trading strategies that include arrival price, market-on-close, participation, time-weighted average price (TWAP), and volume-weighted average price (VWAP). For a VWAP benchmark, the lowest risk execution is obtained by trading one's own shares in the same fractional volume pattern as the market. VWAP execution is expected to result in the lowest temporary market impact costs. The temporary market impact in a rate of trading model is a function of one's own rate of trading expressed as a fraction of the absolute trading activity of the market. One popular interpretation of the model is that the markets are relatively efficient with respect to the relationship between trading volume and volatility, which are typical inputs of the model. Any reduction in impact that results from more trading volume would be offset by an increase in impact due to increased volatility. The lowest absolute rate of trading can be realized by distributing one's orders evenly over time. This is called a time-weighted average price (TWAP) execution.

This chapter reviews recent developments in the analysis of macroeconomic panel data which typically involve aggregate variables from various countries. In contrast to the large N, small T framework that characterizes microeconomic panels, the two dimensions of a macroeconomic data set are more balanced, often providing a comparable number of time periods and countries (regions). Although this is inconsequential for the analysis based on the linear static panel data framework, it becomes crucial when estimating a dynamic model. A second important feature of macroeconomic data is cross-section dependence among countries. In many cases this dependence cannot be accommodated by a simple function of the geographical distance but also depends on trade relations and the level of economic development. Furthermore, cross-country data often exhibit a much richer pattern of heterogeneity that cannot be represented just by letting the intercept vary across countries. While it is often infeasible to allow for individual specific regression coefficients in a large N, small T panel framework, this may be a reasonable option when analyzing macroeconomic data.

This article illustrates how frameworks built around multidimensional panel data of forecasts can be used not only to test the rational expectations hypothesis correctly, but also to study alternative expectations-formation mechanisms, to distinguish anticipated from unanticipated shocks, and to distinguish forecast uncertainty from disagreement.

The use of laboratory and field economic experiments to explore issues in land economics is increasingly popular as researchers identify problems that cannot be adequately addressed by traditional economic theory or empirical techniques. This article reviews this area’s growing literature as well as offers a framework to understand the trade-offs between issues of experimental control, problem context, and representativeness of participants. The key experiment design elements related to land economics are discussed. These elements are then illustrated by a study of the efficiency of reverse auctions for land conservation given different structures of private and public information. The study results suggest that different levels of public information affect sellers’ bidding behavior as well as competitiveness. Overbidding and too little market competition leads to significant loss of efficiency. These results have implications to how to design ecosystem service market cost effectively.

The earlier literature regarding the effects of military expenditures on economic growth had initially shown a positive relationship between the two variables. This article examines this topic, taking account of more recent models of growth. The second section considers the alternative general economic theories that inform the development of models to undertake empirical analyses. The third section considers estimation issues. The fourth section considers the alternative formal models that are common in the literature: the Feder–Ram model, the modified Solow model, and the endogenous growth models. The fifth section presents some empirical results to illustrate the issues involved in estimating the models and to compare their performance. The estimation of more sophisticated models indicates, contrary to the early studies, that the effect of military expenditures on growth is negative.

This chapter provides a targeted review and assessment of current empirical methods most commonly used in economics to model spatially explicit land use and land use change. Empirical models are broadly defined as those that use data on land use and the underlying demand and supply processes to specify model parameters in some way. Four main types of modeling methods are considered: reduced-form econometric, structural econometric, spatial equilibrium simulation, and agent-based simulation. Key strengths and weaknesses of each method are discussed, and the applicability of each method for answering various research questions, including policy scenarios, is illustrated with a few recent examples from the literature. The chapter concludes with a discussion of potential complementarities among these various approaches and several critical research gaps.

The article aims to discuss the optimal asset allocation for sovereign wealth funds (SWF). The main purpose of a commodity based sovereign wealth fund is to create a permanent income stream out of a temporary one and so allow consumption smoothing over time. The asset allocation framework typically consists of an objective function that implies a preference for the highest return for a given level of risk. The ultimate objective of a SWF is to smooth consumption and achieve intergenerational transfers. The accumulation of financial assets presupposes functioning markets for consumption goods such as food products. Another consideration that may guide the investment behavior of sovereign wealth funds and that highlights the role of liabilities is food security. Future food imports are a key component of the balance of payments identity. A rigorous analysis of the commodity fund's optimal asset allocation policy must take into account the role of liabilities and therefore requires an analysis of the country's balance of payments. The ALM takes into account the role of liabilities and the resulting additional hedging demands. The asset liability management (ALM) examines both assets and financial liabilities and models the return on assets and the return on liabilities.

This chapter studies the estimation of φ in linear inverse problems Tφ = r, where r is only observed with error and T may be given or estimated. The unknown element φ belongs to a Hilbert space E. Four examples are relevant for econometrics: the density estimation, the deconvolution problem, the linear regression with an infinite number of possibly endogenous explanatory variables, and the nonparametric instrumental variables estimation. In the first two cases T is given, whereas it is estimated in the two other cases, respectively at a parametric or nonparametric rate. This chapter will recall the main results on these models: concepts of degree of ill-posedness, regularity of φ, regularized estimation, and the rates of convergence usually obtained. The main contributions are, moreover, related to the asymptotic normality of the regularized solution φ obtained with a regularization parameter α. If α → 0, we particularly consider the asymptotic normality of inner products <φ, ϕ>, where ϕ is an element of E. These results can be used to construct (asymptotic) tests on φ.

This article describes and analyzes the Austrian approach to law and economics within the context of the law and economics discipline. The important and distinctive feature of the Austrian approach is the emphasis on economic and legal processes. The article focuses on four themes within the Austrian approach to law and economics: the spontaneous origin of legal institutions; the analysis of implications of ignorance, decentralization of knowledge, and static and dynamic uncertainty; the interaction between the changes in legal institutions and the market process and coordination; and entrepreneurship in market and non-market settings.

This article considers the strategic transmission of information, though in a variety of ways, including not only costly signaling through actions to convey information, but also engaging in limited war as a means to learn about the opponent's strength. It provides a useful review of the mechanism design approach to the problem. The article implies that conflict and inefficiencies are to be expected whenever parties have optimistic prospects on the outcome of the all-out conflict. It also examines the recent literature that has challenged this view. These new contributions consider the existence of limited confrontations that allow bargaining to resume. In the presence of private information on the balance of strengths, that means that conflict can convey information and potentially become a bargaining instrument.

This article describes various discrete choice models of consumers who may be heterogeneous both in terms of their preferences and in their sensitivities to marketing variables such as price. It addresses a distinct set of challenges that are being posed through the use of hierarchical priors. It considers standard statistical approach that generates discreteness by applying a censoring function to underlying continuous latent variables. This approach generates models that can be employed in situations where more descriptive models are required. Nonparametric and flexible parametric models involving Dirichlet processes and other mixtures are also accepted and favored in marketing. This article outlines several utility specifications that incorporate discreteness and other important aspects of consumer decisions. Computational issues are important when dealing with large marketing data sets and this article discusses on how to implement posterior simulation methods in marketing models.

This article discusses the Bayesian approach to decision theory. It focuses on the case of an individual deciding between treatments. It deals with the role of information that is available about other individuals through a propensity score. It also shows the reason for absence of propensity score in the likelihood function but its appearance in the prior. A prior distribution leads to a closed-form expression for the decision rule. The parametric model plays the role of a prior distribution that can be dominated by the data. The next section examines the role of the propensity score in a random effects model with normal distributions for the outcomes and the random effects. It takes up the extension to the case of treatment selection based on unobservables. The main aim of this article is to estimate an average treatment effect for a particular covariate cell.