The more, the better? Forecasting gains from high-frequency housing prices in a Markov-switching dynamic factor model

This paper extracts housing boom-bust cycle signals from metropolitan statistical area (MSA)-level housing prices using a Markov-switching dynamic factor model. To mitigate the estimation bias, it utilizes high-frequency housing prices that follow the methodology of the monthly Case–Shiller house price indices. The housing bust phases specified from weekly and daily housing prices precede those based on monthly prices by approximately 2 years. MSAs with top signal-to-noise ratios offer greater marginal contributions to improvements in forecasting housing cycles than MSAs with bottom ratios for all frequencies. The results highlight the importance of indicator quality and provide evidence against “The more, the better” since incorporating more MSA-level housing prices into housing factors does not guarantee more satisfactory housing cycle forecasts.

Understanding binary classifier model structure based on Shapley feature interaction patterns

There is increasing emphasis on the interpretability of machine learning models, including in understanding biological systems. The well-known Shapley value framework based on game theory works in principle with any models to attribute feature importance. While feature interactions are critical to understand and can be interpreted within this framework, much attention is paid in practice on global feature importance and general trends of interactions. The inter-relationships between underlying model structure and Shapley value and its decomposition is less clear. Here we use binary classifiers to systematically examine how logical and additive interactions affect marginal contributions. These decomposed main and interaction effects are reflected in resulting Shapley dependence plots. The directionality of inequalities or logical/additive operators influence independently the main and marginal/interaction effects. Lastly, we show that these principles are applicable for models with noise.

Multiplayer Allocations in the Presence of Diminishing Marginal Contributions: Cooperative Game Analysis and Applications in Management Science

We use cooperative game theory to investigate multiplayer allocation problems under the almost diminishing marginal contributions (ADMC) property. This property indicates that a player’s marginal contribution to a non-empty coalition decreases as the size of the coalition increases. We develop ADMC games for such problems and derive a necessary and sufficient condition for the non-emptiness of the core. When the core is non-empty, at least one extreme point exists, and the maximum number of extreme points is the total number of players. The Shapley value may not be in the core, which depends on the gap of each coalition. A player can receive a higher allocation based on the Shapley value in the core than based on the nucleolus, if the gap of the player is no greater than the gap of the complementary coalition. We also investigate the least core value for ADMC games with an empty core. To illustrate the applications of our results, we analyze a code-sharing game, a group buying game, and a scheduling profit game. This paper was accepted by Chung Piaw Teo, optimization.

Political Connections and Enterprise Performance of Private Enterprises in China: Impacts and Mechanisms

Researchers have not come to an agreement on the impact of political connection on enterprise performance although this issue draws much attention. This paper attributes the above phenomenon to lack of precise classification of various political connection types in China. Based on existing literature, this paper divides political connections into current political connections and former political connections, and identifies their own hierarchy. Empirical study using Chinese Private Enterprise Survey data shows that different sorts of political connections all contribute to enterprise performance, and internal governance plays the mediating role. Overall, this paper may make marginal contributions to the study on the relationship between political connections and enterprise performance.

Measuring and Allocating Systemic Risk

In this paper, we develop a framework for measuring, allocating and managing systemic risk. SystRisk, our measure of total systemic risk, captures the a priori cost to society for providing tail-risk insurance to the financial system. Our allocation principle distributes the total systemic risk among individual institutions according to their size-shifted marginal contributions. To describe economic shocks and systemic feedback effects, we propose a reduced form stochastic model that can be calibrated to historical data. We also discuss systemic risk limits, systemic risk charges and a cap and trade system for systemic risk.

Hard Cases

Chapter 6 applies the theory of responsibility and blameworthiness developed in Chapter 5 to a number of difficult cases. We begin with a discussion of child soldiers, and raise doubts about the intuition that child soldiers always lack moral responsibility for their actions. The second half of the chapter focuses on various causally complex cases. These cases include instances in which perpetrators are at a significant physical distance from those they harm, cases involving harm caused by actors in hierarchical institutions, and scenarios in which individual wrongdoers make marginal contributions to collective efforts. For all of these cases, our view requires that we look closely at how agents understand the meaning of their actions, at the interpersonal significance those actions may have from the viewpoints of victims, and at whether we agree with the moral perspective that victims take on the treatment they received.

Determinants of Airbnb demand in Vienna and their implications for the traditional accommodation industry

This study identifies key determinants of Airbnb demand and quantifies their marginal contributions in terms of demand elasticities. A comprehensive cross-sectional data set of all Viennese Airbnb listings that were active between July 2015 and June 2016 is examined. Estimation results, which are obtained by cluster-robust ordinary least squares, show that Airbnb demand in Vienna is price-inelastic. Significant positive drivers include listing size, number of photos, and responsiveness of the host. Significant negative drivers include listing price, distance from the city center, and response time of the host. Implications for the traditional accommodation industry are that, on the one hand, it should better communicate its sought-after advantages (e.g. lower average minimum duration of stay). On the other hand, it should increase its offer of bigger and better equipped hotel rooms since hosting more than two guests at a time is one of the major benefits of Airbnb.

The International Determinants of Military Coup Behavior

Why do coups d’état happen? Although many studies have investigated this question, they pay relatively little attention to the international causes and ramifications of coups. Especially, empirical studies on the external determinants of coup risk and outcomes still remain limited. There are two current lines of research in this direction. The first line studies international linkages and coup risk, looking at the external determinants of coups: regional spillover effects, foreign linkage, and foreign leverage. A promising angle on this front is focusing on the role of post-coup reactions from international actors to illuminate how coup plotters shape their incentives under outside pressure. The second line of research investigates interstate conflict and coup risk, considering diversionary behavior and external threats as potential coup-proofing strategies. In this effort, studying the relationship between external threat environment and coup risk can be fruitful, whereas empirical tests of the classical diversionary war theory will yield relatively marginal contributions. Currently, three issues stand out in the empirical coup literature that should be further addressed by scholars. First is the need for more extensive and systematic data collection efforts to obtain detailed information about the identities, targets, and motives of coup perpetrators. Second, the external sources of leader insecurity beyond interstate conflicts remain an underexplored area. Third, although many studies have tried to determine when coup attempts happen, scholarly knowledge of when and how they succeed remains very limited. More work is needed to uncover the determinants of coup success across different regimes and leader survival scenarios.

Research on Critical Nodes Algorithm in Social Complex Networks

Discovering critical nodes in social networks has many important applications and has attracted more and more institutions and scholars. How to determine the K critical nodes with the most influence in a social network is a NP (define) problem. Considering the widespread community structure, this paper presents an algorithm for discovering critical nodes based on two information diffusion models and obtains each node’s marginal contribution by using a Monte-Carlo method in social networks. The solution of the critical nodes problem is the K nodes with the highest marginal contributions. The feasibility and effectiveness of our method have been verified on two synthetic datasets and four real datasets.