Does the spatial representation affect criteria weights in environmental decision-making? Insights from a behavioral experiment

The need and interest to consider cognitive and motivational biases has been recognized in different disciplines (e.g. economics, decision theory, risk analysis) and has recently reached environmental decision-making. Within this domain, the intrinsic presence of a spatial dimension of both alternatives and criteria calls for the use of maps throughout the decision-making process to properly represent the spatial distribution of the features under analysis. This makes spatial Multi Criteria Decision Analysis (MCDA) a particularly interesting domain to explore new dimensions of cognitive biases. This study proposes a behavioral experiment aimed at discovering to what extent the spatial visualization (i.e. maps) of criteria versus the non-spatial one (i.e. tables) can bias the weight elicitation phase of a spatial MCDA process. The experiment simulates a very common analysis in environmental and land use planning: land suitability analysis. Our findings show that there are significant consequences on how important we perceive a certain criterion to be, depending on whether it is represented as a map or as a table among a mix of maps and tables. Indeed, the map representation of the same criterion leads to higher weights attributed to that criterion compared to the table representation. Visualizing the same information as a map or as a table, although technically equivalent, is thus not psychologically equivalent for Decision Makers. The results of this experiment are expected to have implications for spatial decision-making processes, by generating better awareness on the impacts of map-mediated land suitability analysis.     

An information-theoretic approach for forecasting interval-valued SP500 daily returns

We develop an iterative and efficient information-theoretic estimator for forecasting interval-valued data, and use our estimator to forecast the SP500 returns up to five days ahead using moving windows. Our forecasts are based on 13 years of data. We show that our estimator is superior to its competitors under all of the common criteria that are used to evaluate forecasts of interval data. Our approach differs from other methods that are used to forecast interval data in two major ways. First, rather than applying the more traditional methods that use only certain moments of the intervals in the estimation process, our estimator uses the complete sample information. Second, our method simultaneously selects the model (or models) and infers the model’s parameters. It is an iterative approach that imposes minimal structure and statistical assumptions.     

Forecasting using heterogeneous panels with cross-sectional dependence

In this paper, we focus on forecasting methods that use heterogeneous panels in the presence of cross-sectional dependence in terms of both spatial error dependence and common factors. We propose two main approaches to estimating the factor structure: a residuals-based approach, and an approach that uses a panel of auxiliary variables to extract the factors. Small sample properties of the proposed methods are investigated through Monte Carlo simulations and applied to predict house price inflation in OECD countries.     

Forecasting value at risk and expected shortfall with mixed data sampling

I propose applying the Mixed Data Sampling (MIDAS) framework to forecast Value at Risk (VaR) and Expected shortfall (ES). The new methods exploit the serial dependence on short-horizon returns to directly forecast the tail dynamics of the desired horizon. I perform a comprehensive comparison of out-of-sample VaR and ES forecasts with established models for a wide range of financial assets and backtests. The MIDAS-based models significantly outperform traditional GARCH-based forecasts and alternative conditional quantile specifications, especially in terms of multi-day forecast horizons. My analysis advocates models that feature asymmetric conditional quantiles and the use of the Asymmetric Laplace density to jointly estimate VaR and ES.     

Navigating Through the Spatial and Institutional Contradictions of Public Audit Oversight

Abstract

This study revisits the actions of the French audit regulator (H3C) and the French profession over the 2003–2013 period in response to the regulatory transformations that occurred both in the national and transnational space. Our analysis highlights how these actors have navigated through the spatial and institutional contradictions that characterize public audit oversight in order to manage variable and opposing interests from one space to another. In particular, we show how conflicting national and transnational conceptions of professionalism and commercialism have developed to support different regulatory agendas. Our analysis of the spatial dynamics of public audit oversight allows us to propose a much less confrontational representation of the relationships between public audit regulators and the audit profession. We maintain that more attention should be paid to the spatial variants of commercialism and professionalism to better understand the development of audit regulation.     

外商直接投资、区域创新与城市化发展研究

基于2000—2017年省际面板数据,运用静态面板与门限面板模型,通过建立以政府干预、市场化进程为门限变量的回归模型,实证分析了外商直接投资与区域创新对我国城市化发展的影响。研究结果表明:外商直接投资、区域创新对城市化发展有积极的带动作用,同时,外商直接投资对区域创新的作用由替代效应向互补效应转变;政府干预对外商直接投资存在双重门限效应,对区域创新存在单一门限效应;市场化对外商直接投资存在单一门限效应,但对区域创新存在双重门限效应。基于以上结论,进一步提出促进城市化发展的建议。     

网络强度、组织冗余与创新模式——对200家样本企业问卷调研数据的实证检验

通过社会网络带来资源冗余进而对创新选择产生影响，这一机制已成为当今企业可持续发展的重要路径。运用200份企业样本调研数据，对网络强度、不同类型组织冗余及两种创新模式进行实证检验，结果发现，网络强度显著正向直接影响利用式创新和探索式创新;网络强度还通过正向影响未吸收冗余对两种创新产生正向间接影响作用;未吸收冗余与创新模式间具有显著正向影响关系,而吸收冗余在三者影响路径关系中检验结果不显著，可能是受企业自身因素或条件影响所致。最后，根据研究结论提出3点重要管理启示。     

产军协同、政府科技支持与技术创新效率研究

运用空间数据分析方法，检验产军协同、政府科技支持与技术创新效率在我国省域间的分布格局。研究表明，三者均存在显著的正向空间相关性，即正向空间溢出效应，并且在不同地区形成局部空间集聚，存在高低非均衡的“俱乐部”现象。基于空间误差模型和空间滞后模型，实证检验产军协同、政府科技支持对技术创新效率的影响。结果发现，从全国和分地区看，产军协同、政府科技支持均能够显著促进技术创新效率提升，并且二者存在显著的交互作用，能够发挥彼此间的互补效应，进一步促进技术创新效率提升；不同地区产军协同、政府科技支持对技术创新效率的影响程度存在较大差异，西部地区产军协同、政府科技支持对技术创新效率的促进作用比东部和中部地区显著。     

The geography of suppliers and retailers

In the last decades, supply chains have increasingly transcended national boundaries developing into global supply chains. Along with the many opportunities arising from international sourcing and the extended commercial presence over the world, the management of a globally dispersed supply chain is highly complex. A key issue to consider when dealing with the global supply chain design is the location of facilities, not only with respect to firms' owned facilities but also the supply and distribution side as factors that affect supply chain complexity and operational performance. This paper sets out a methodological framework to characterize the geographical configuration of a firm's suppliers and retailer networks. Quantitative indexes of network spatial concentration and relative proximity measures based on a nonparametric kernel density estimator are developed to identify both intra- and inter-firm patterns between the supply and point of sales' distributions. The method is first described by means of a series of theoretical-illustrative examples and exemplified by analyzing the geographical dispersion of four practical cases from the fashion-textile industry (i.e., Adidas, Benetton, C&A, and Puma). Subsequently, managerial implications and potential use of the metrics are discussed, showing how the proposed approach can support researchers and practitioners to improve supply chain location decisions and logistic integration, and evaluate changes in either the purchasing or distribution strategy.     

Relationship between city size and firm productivity – A new interpretation using the Chinese experience

This paper contributes to the literature on the relationship between city size and firm productivity by focusing on agglomeration, selection (market competition), and sorting (presence of firms with diverse productivity) effects using Chinese firm-level data for 1998–2013. Contrary to the existing literature, our parametric regression estimates from nearly two million firms show that when the selection effect is controlled, productivity advantage in big cities is reversed. This outcome is explained through a quantile regression showing the existence of left-sided sorting (inefficient firms sort themselves to larger cities) in big cities which is not captured in existing empirical literature. We further find that (1) left-sided sorting is stronger in exporters than non-exporters; (2) is also generated mainly in enterprises with high asset-liability ratios; and (3) selection has a positive effect on firm productivity, suggesting that market competition is key in an explanation of the rapid growth of big cities in China.