An extreme value analysis of the last century crises across industries in the U.S. economy

The two large scale crises that hit the world economy in the last century, i.e. the Great Depression and the Great Recession, have similar outbreak and recovery patterns with respect to several macroeconomic variables. In particular, the largest depressions are likely to be accompanied by stock-market crashes. This study investigates the behavior of the U.S. stock market before, during and after deep downturns, focusing particularly on the tails of the return distribution. We develop two automatic procedures to identify multiple change-points in the tail of financial time series as well as in the co-crash and co-boom probabilities of different markets. We then apply our methodology to twelve time series representative of the sectors of the U.S. economy. We find that regime shifts in the lower tail of the distribution tend to co-occur before deep downturns. Our results contribute to a better understanding of the origin and systemic nature of large scale events to make policy interventions more timely and effective.     

Environmental strategies in the paint and coatings industry

In general, a proactive strategy entails an anticipatory approach to problems. It is also associated with taking the initiative and doing more than is strictly needed. The nature of proactivity that businesses may display with respect to the environment is considered and also its meaning in terms of strategic behaviour and technological development. A conceptual framework is developed that differentiates between types of proactive strategies. This framework is based on a study of the paint and coatings industry in Great Britain and the Netherlands and is applicable to both countries. Even though the big international paint companies play a decisive role in the development and introduction of cleaner technologies, it is argued that the small- and medium-sized paint companies deserve special attention. Not only is proactive behaviour by the big companies important, but also proactive strategies by the small- and medium-sized companies, even if they are limited in scope, because the latter determine at national and local levels the pace and nature of the transformation that has to take place to produce a sustainable economy. This process is crucial to ecological modernization if it is intended not only to get rid of old-fashioned ways, but also to create new opportunities for economic upgrading and rejuvenation.     

Searching for big data: How incumbents explore a possible adoption of big data technologies

Big data is often described as a new frontier of IT-enabled competitive advantage. A limited number of exemplary firms have been used recurrently in the big data debate to serve as successful illustrations of what big data technologies can offer. These firms are well-known, data-driven organizations that often, but not always, are born digital companies. Comparatively little attention has been paid to the challenges that many incumbent organizations face when they try to explore a possible adoption of such technologies. This study investigates how incumbents handle such an exploration and what challenges they face. Drawing on a four-year qualitative field study of four large Scandinavian firms, we are able to develop a typology of how incumbents handle the exploration of and resistance to adopting big data technologies. Directly affecting the incumbents’ exploration are two aspects that separate the adoption of big data technologies from that of other technologies. First, being an elusive concept, big data technologies can mean different things to different organizations. This makes the technologies difficult to explain before an investing body, while it simultaneously opens up possibilities for creative definitions. Second, big data technologies have a transformative effect on the organization of work in firms. This transformative capability will make managers wary as it might threaten their position in the firm, and it will create ripple effects, transforming other systems besides those directly connected to the technology.     

What Would Milton Friedman Have Thought of the Great Recession?

Milton Friedman died in 2006 , right before the onset of the Great Recession. Unfortunately, we will never know how Friedman would have interpreted this event. However, we can draw some inferences from his published views on the Great Depression, as well as his views on more recent monetary policy, especially in Japan. It seems likely that Friedman would have blamed the Fed for insufficiently expansionary monetary policy during 2008 and 2009, a view that is quite different from the conventional conservative interpretation of events.     

中国大城市发展和都市区的形成

改革开放以来,我国大城市发展迅速,出现了都市区这种新的城市空间形态.都市区将成为新世纪我国经济和城市化发展的重要地域空间形式,同时也对传统的城市发展战略、城市规划和管理提出了新的要求和挑战.分析了中国大城市的发展趋势和空间演变特征,阐述了中国都市区现象的出现及其发展前景,并对中国都市区的概念、界定标准以及都市区规划等问题进行了探讨.     

Book Review

The article deals with Swedish well-educated female entrepreneurs (Swefees), that is entrepreneurs with a education exceeding 12 years. The Swefees seem to differ in some interesting ways both from other female entrepreneurs, from male entrepreneurs and from other women on the labour market. But it also turns out that they are of two kinds, Loner-Swefees and Family-Swefees. The Loner-Swefees are single, living in a big city environment and have an interrupted career in the public or private sector behind them. They are extremely career-oriented. The Family-Swefees are also career-oriented but try to run a family and a firm at the same time. Swefees go into business with strong feelings both of a push and pull kind. They are very professional in their way of running the firm.

The population of Swefees were found through a big questionnaire sent to a sample of 1,440 of the population of over 64,000 Swedish female entrepreneurs. A little more than 1,000 (of the 1,440) were still in business when receiving the questionnaire which around 70% of them answered. Swefees turned out to be only 6% of female entrepreneurs.     

Wilhelm Röpke and Richard C. Koo On Secondary Deflations and Balance Sheet Recessions

The concept of a ‘secondary deflation’ was developed in the 1930s by the German economist Wilhelm Röpke, who saw it as something different from a normal depression. While a primary deflation is a necessary reaction to the inflation from a boom period, a secondary deflation is independent and economically purposeless. Röpke argued that secondary depressions occurred in the US, Germany, France and Switzerland during the 1930s, but was vague on what made them follow primary depressions. Recently, the Taiwanese–American economist Richard C. Koo has claimed to have discovered the ‘Holy Grail of macroeconomics’, that is, what made the Great Depression so deep and long. During the Great Depression, the bursting of the asset price bubble resulted in private sectors having more debt than assets; as they shifted from maximising profits to minimising debt, the consequent debt deflation shrank the economy. According to Koo, Western economies today are suffering from a similar ‘balance sheet recession’. Strengthened by the notion of a balance sheet recession, Röpke's long‐lost insights might advance our understanding of the business cycle in general and the present crisis in the US and the Eurozone in particular.     

Rules for a lender of last resort: An historical perspective

The Federal Reserve was established in 1913 to be a lender of last resort. Paul Warburg, its principal architect had in mind that a U.S. central bank would follow Bagehot׳s strictures ‘to lend freely at a penalty rate’ in the face of a scramble for high powered money. Yet the Federal Reserve Act never spelled out how the Fed was supposed to act as an LLR. This omission came to the fore in the Great Contraction 1929 to 1933 when the Fed failed to prevent four banking panics which turned a serious recession into the Great Contraction. Reforms in the 1930s corrected some of the Fed׳s failures but clamped down on financial activity for 40 years. The financial crisis problem returned in the 1970s with financial liberalization. The Fed abandoned Bagehot׳s strictures and adopted the ‘Too big to fail’ doctrine and ‘creative ambiguity’. This policy shift contributed to moral hazard and created new threats to financial stability with the rise of the ‘shadow banking system’. The subprime mortgage crisis prompted the Fed to take unprecedented LLR activities which have opened up a Pandora׳s box of perils. The Fed has moved away from rules based policy in its LLR function.     

日韩企业管理思想对我国企业的借鉴意义

进入21世纪以来,国际竞争日益激烈。企业间的竞争已从技术、规模的竞争转向核心竞争力的竞争。对我国而言,建立在我国优秀传统文化基础之上的企业管理理念才是我们核心竞争力所在。目前我国在借鉴西方发达国家尤其是美国理论基础上建立的企业管理理论确实促进了我国企业的发展。但其文化与我国有巨大的差异,不能很好地服务于我国企业核心竞争力的塑造,其理论用于指导我们的实践时就出现了许多与我们实际不符的情况。而与我们有近似文化渊源的日韩在企业管理的实践中取得了令人瞩目的成就,相对于欧美而言,其做法对我们有更大的借鉴意义。     

农村人口城市化与农业产业化

农村人口城市化是社会发展的必然规律,农村人口城市化问题已经成为制约我国国民经济持续健康增长的重要因素.我国目前农村人口城市化的两种主要途径——农村剩余劳动力进入当地小城镇从事乡镇企业或从事商业服务业以及进入大中城市从事传统行业,并没有很好地解决我国农村人口城市化的问题;应该在深挖这两种途径的潜力的同时,积极探索其他有效途径;以土地集中经营为基础的农业产业化是解决我国目前农村人口城市化问题的一条重要途径.     

Anticipating special events in Emergency Department forecasting

Accurate daily forecast of Emergency Department (ED) attendance helps roster planners in allocating available resources more effectively and potentially influences staffing. Since special events affect human behaviours, they may increase or decrease the demand for ED services. Therefore, it is crucial to model their impact and use them to forecast future attendance to improve roster planning and avoid reactive strategies. In this paper, we propose, for the first time, a forecasting model to generate both point and probabilistic daily forecast of ED attendance. We model the impact of special events on ED attendance by considering real-life ED data. We benchmark the accuracy of our model against three time-series techniques and a regression model that does not consider special events. We show that the proposed model outperforms its benchmarks across all horizons for both point and probabilistic forecasts. Results also show that our model is more robust with an increasing forecasting horizon. Moreover, we provide evidence on how different types of special events may increase or decrease ED attendance. Our model can easily be adapted for use not only by EDs but also by other health services. It could also be generalised to include more types of special events.     

地铁车站深基坑研究方法的综述

伴随着我国社会经济的发展和城市化进程的发展,城市交通已经称为各大城市面临的重大问题之一。为了解决交通问题,我国的很多大城市都开始修建地铁,例如上海,北京,西安等城市,太原市也已经开始规划修建地铁。地铁车站在开挖过程中所面临的基坑开挖是一个极其复杂的力学过程,这一问题已成为广大学者研究的热点论题。车站深基坑的开挖过程是一个极其复杂的力学状态,其研究的手段也不尽相同。从理论分析的角度,对现有的车站深基坑研究方法作以归纳,分析了其各自的力学模型和计算过程,为设计和计算提供了一定的指导。     

Revisiting IS research practice in the era of big data

Through building and testing theory, the practice of research animates data for human sense-making about the world. The IS field began in an era when research data was scarce; in today's age of big data, it is now abundant. Yet, IS researchers often enact methodological assumptions developed in a time of data scarcity, and many remain uncertain how to systematically take advantage of new opportunities afforded by big data. How should we adapt our research norms, traditions, and practices to reflect newfound data abundance? How can we leverage the availability of big data to generate cumulative and generalizable knowledge claims that are robust to threats to validity? To date, IS academics have largely welcomed the arrival of big data as an overwhelmingly positive development. A common refrain in the discipline is: more data is great, IS researchers know all about data, and we are a well-positioned discipline to leverage big data in research and teaching. In our opinion, many benefits of big data will be realized only with a thoughtful understanding of the implications of big data availability and, increasingly, a deliberate shift in IS research practices. We advocate for a need to re-visit and extend traditional models that are commonly used to guide much of IS research. Based on our analysis, we propose a research approach that incorporates consideration of big data—and associated implications such as data abundance—into a classic approach to building and testing theory. We close our commentary by discussing the implications of this hybrid approach for the organization, execution, and evaluation of theory-informed research. Our recommendations on how to update one approach to IS research practice may have relevance to all theory-informed researchers who seek to leverage big data.     

Nonparametric estimation and inference about the overlap of two distributions

This paper develops methodology for nonparametric estimation of a measure of the overlap of two distributions based on kernel estimation techniques. This quantity has been proposed as a measure of economic polarization between two groups, Anderson (2004) and Anderson et al. (2010). In ecology it has been used to measure the overlap of species. We give the asymptotic distribution theory of our estimator, which in some cases of practical relevance is nonstandard due to a boundary value problem. We also propose a method for conducting inference based on estimation of unknown quantities in the limiting distribution and show that our method yields consistent inference in all cases we consider. We investigate the finite sample properties of our methods by simulation methods. We give an application to the study of polarization within China in recent years using household survey data from two provinces taken in 1987 and 2001. We find a big increase in polarization between 1987 and 2001 according to monetary outcomes but less change in terms of living space.     

国有商业银行不良资产证券化可行性分析

四大国有商业银行的巨额不良资产,不仅关系到自身的生存与发展,还关系到我国金融体系的稳定,必须尽早化解。本文论述信贷资产证券化作为一种转移信用风险、提高资产流动性、安全性和效益性的金融创新工具,能有效地化解不良资产,促进金融市场的发展,从而有助于防范金融危机。我国应借鉴国际经验并结合本国国情,将信贷资产证券化运用到国有商业银行不良资产的实际化解工作中。     

Testing Part of a DSGE Model by Indirect Inference

We propose a way of testing a subset of equations of a DSGE model. The test draws on statistical inference for limited information models and the use of indirect inference to test DSGE models. Using the numerical small sample distribution of our test for two subsets of equations of the Smets–Wouters model we show that the test has accurate size and good power in small samples, and better power than using asymptotic distribution theory. In a test of the Smets–Wouters model on US Great Moderation data, we reject the specification of the wage‐price but not the expenditure sector. This points to the wage‐price sector as the source of overall model rejection.     

Transparent modeling of influenza incidence: Big data or a single data point from psychological theory?

Simple, transparent rules are often frowned upon while complex, black-box models are seen as holding greater promise. Yet in quickly changing situations, simple rules can protect against overfitting and adapt quickly. We show that the surprisingly simple recency heuristic forecasts more accurately than Google Flu Trends (GFT) which used big data analytics and a black-box algorithm. This heuristic predicts that “this week’s proportion of flu-related doctor visits equals the proportion from the most recent week.” It is based on psychological theory of how people deal with rapidly changing situations. Other theory-inspired heuristics have outperformed big data models in predicting outcomes, such as U.S. presidential elections, or other uncertain events, such as consumer purchases, patient hospitalizations, and terrorist attacks. Heuristics are transparent, clearly communicating the underlying rationale for their predictions. We advocate taking into account psychological principles that have evolved over millennia and using these as a benchmark when testing big data models.     

Inductive reasoning: The promise of big data

Theory is a cornerstone of organizational research. Recently, however, some organizational scientists have argued that there is an overemphasis on theory development in our prominent publication outlets, calling for a rejuvenation of empirically driven research. To bring empirical research back to the forefront, the organizational sciences need a shock to the system: the advent of big data analytics in organizations provides just such a shock. The purpose of the following paper is to advocate for big data analytics as tools that can be used to support inductive research methods in the organizational sciences. We then highlight areas of organizational research and practice in which big data analytics can have an impact, provide readers with a tempered perspective on big data in the organizational sciences, and suggest a number of ways that researchers, reviewers, and editors can prepare themselves for the introduction of big data research in the organizational sciences.     

Competitive moment matching of a New-Keynesian and an Old-Keynesian model

The paper considers two rival models referring to the new macroeconomic consensus: a standard three-equation model of the New-Keynesian variety versus dynamic adjustments of a business and an inflation climate in an ‘Old-Keynesian’ tradition. Over the two subperiods of the Great Inflation and Great Moderation, both of them are estimated by the method of simulated moments. An innovative feature is here that the moments do not only include the autocovariances up to eight lags of quarterly output, inflation and the interest rate, but optionally also a measure of the raggedness of the three variables. In short, the performance of the Old-Keynesian model is very satisfactory and similar to the New-Keynesian model, or even better. In particular, the Old-Keynesian model is better suited to match the new moments without deteriorating the original second moments too much.