《算学启蒙》中的香料经济新探

《算学启蒙》中有关香料方面的算题,涉及元代市舶司的“双抽”和“单抽”、纸钞与铜银钱并用,以及各种香料的价格等问题。诚然,这些问题的解决,确实需要综合多种史籍来参互求证,但毋庸置疑的是《算学启蒙》起到了为其他史籍所无法取代的独特价值和作用。实际上,元代大德年间所发生的许多经济现象,都或多或少在《算学启蒙》一书中有所体现。随着学界对中国古代算书之史学价值的认识愈来愈明确,经济数学、数学社会学等新的交叉学科应运而生,我们不能仅仅囿于从纯数学的角度去解读《算学启蒙》,因为《算学启蒙》本身具有更重要的经济史和社会史的研究价值和意义。     

Higher Intensity,Higher Profit? Empirical Evidence from Dairy Farming in New Zealand

Over the last two decades there have been significant and well‐documented changes in the nature and structure of New Zealand dairy production. One particular feature has been a marked shift in the ‘input intensity’ of dairy farming systems through increased use of supplementary feed. These changes have generated debate about the impact of dairy farm intensity on the performance of farm businesses and the competitiveness of the New Zealand dairy sector. Using a novel econometric approach, we assess statistically the impact of three types of dairy farming systems on milk production and financial performance, using farm business data provided by DairyBase^®. Our empirical results show that higher input systems perform significantly better physically than lower input systems, but not financially. The disaggregated analysis suggests that the average treatment effects differ by region and performance quantiles.     

A sequential Monte Carlo approach to inference in multiple‐equation Markov‐switching models

Vector autoregressions with Markov‐switching parameters (MS‐VARs) offer substantial gains in data fit over VARs with constant parameters. However, Bayesian inference for MS‐VARs has remained challenging, impeding their uptake for empirical applications. We show that sequential Monte Carlo (SMC) estimators can accurately estimate MS‐VAR posteriors. Relative to multi‐step, model‐specific MCMC routines, SMC has the advantages of generality, parallelizability, and freedom from reliance on particular analytical relationships between prior and likelihood. We use SMC's flexibility to demonstrate that model selection among MS‐VARs can be highly sensitive to the choice of prior.     

Comparing automated text classification methods

Online social media drive the growth of unstructured text data. Many marketing applications require structuring this data at scales non-accessible to human coding, e.g., to detect communication shifts in sentiment or other researcher-defined content categories. Several methods have been proposed to automatically classify unstructured text. This paper compares the performance of ten such approaches (five lexicon-based, five machine learning algorithms) across 41 social media datasets covering major social media platforms, various sample sizes, and languages. So far, marketing research relies predominantly on support vector machines (SVM) and Linguistic Inquiry and Word Count (LIWC). Across all tasks we study, either random forest (RF) or naive Bayes (NB) performs best in terms of correctly uncovering human intuition. In particular, RF exhibits consistently high performance for three-class sentiment, NB for small samples sizes. SVM never outperform the remaining methods. All lexicon-based approaches, LIWC in particular, perform poorly compared with machine learning. In some applications, accuracies only slightly exceed chance. Since additional considerations of text classification choice are also in favor of NB and RF, our results suggest that marketing research can benefit from considering these alternatives.     

Volatility forecasting: long memory,regime switching and heteroscedasticity

In this article, we account for the first time for long memory, regime switching and the conditional time-varying volatility of volatility (heteroscedasticity) to model and forecast market volatility using the heterogeneous autoregressive model of realized volatility (HAR-RV) and its extensions. We present several interesting and notable findings. First, existing models exhibit significant nonlinearity and clustering, which provide empirical evidence on the benefit of introducing regime switching and heteroscedasticity. Second, out-of-sample results indicate that combining regime switching and heteroscedasticity can substantially improve predictive power from a statistical viewpoint. More specifically, our proposed models generally exhibit higher forecasting accuracy. Third, these results are widely consistent across a variety of robustness tests such as different forecasting windows, forecasting models, realized measures, and stock markets. Consequently, this study sheds new light on forecasting future volatility.     

Oil shocks and volatility jumps

Review of Quantitative Finance and Accounting - In this paper, we analyse the role of oil price shocks, derived from expectations of consumers, economists, financial market, and policymakers, in...     

苹果种植适度规模经营研究*——以陕西省白水县为例

［目的］分析苹果种植户经济效益的影响因素，研究适合苹果种植的农地规模，提高白水县果农苹果种植的经济效益，探索苹果产业可持续发展经营模式，丰富以果蔬为主的集约型经济作物适度规模经营研究范围。［方法］采用实地调研法，对陕西省白水县230户苹果种植户进行问卷调查，从收益最大化角度，运用柯布道格拉斯生产函数对苹果生产的投入产出进行经济分析，构建收益最大化模型，测算白水县苹果种植的最适规模，分析苹果产量的影响因素，探讨劳动力投入、资本投入、有效面积和果农参加技术培训对苹果产出的影响和贡献程度，并用钱克明等对农地的适度经营规模定义进行结果验证，最后提出合理的政策建议。［结果］经过分析，在同等技术条件下，劳动力投入、资本投入、有效种植面积、果农参加技术培训的次数对苹果的产出具有显著影响。以农户收益最大化模型测算的劳均经营规模面积在0363~0447hm2（544~670亩）的可视为适度规模，根据钱克明等的适度经营规模目标值的计算，其函数解为550，在适度规模范围之内。即户均农地适度经营规模0751~0966hm2（1127~1449亩）。目前白水县苹果生产处于规模报酬递减阶段，农资市场环境和劳动力投入方面仍然存在短板。［结论］建议扩大白水县户均苹果种植面积，提升土地经营效益，从而增加果农收入，支持农业新型经营主体发展，创新发展模式； 建立新型职业农民培育体系，促进苹果种植户剩余劳动力转移，提高农民非农收入； 从法律上规范农村地区农资市场环境； 规范农户绿色生产经营行为，提高化肥的使用效率和开发新的高效肥料； 政府引导农地的适度规模集中，整合资源进行规模化、现代化的经营。     

The Brexit effect: the case of UK ADR performance one year later

In an unexpected outcome, UK voters decided that it was time to exit the European Union based on the results of a vote held on 23 June 2016. Studies of the affects and implication of Brexit include a study showing that the vote was met with a negative short-term wealth effect for UK American depository receipts (ADRs). This study examines the one-year anniversary holding period returns of these ADRs along with the British Pound and the FTSE 100 to discover any lingering effects from the historical vote. Results indicate that the one-year holding period returns for the ADRs averaged 5.8% for the year while the FTSE gained 4.8%, the S&P 500 gained 15.4% and the Pound lost 13.2% of its value.     

Discretionary technology bootlegging tensions in institutional healthcare work

We explore individuals who take some of their technology use ‘underground’, described as ‘bootlegging’, to enhance healthcare work. We find that healthcare professionals’ informal use of mobile applications in healthcare work sometimes ‘sticks out’ and this produces professional identity tensions: (1) conflict with perceptions of professional behaviour, and (2) defilement of expert judgment. Our analysis, moreover, reveals that identity work (i.e. ‘accepting’ and ‘sensemaking’) provides a coping mechanism to deal with these unresolved professional identity tensions. This paper contributes to a better understanding of the constitutive entanglements and two‐way interactions of discretionary technology bootlegging, professional identity and autonomy in institutional healthcare work.     

Agency costs and tax planning when the government is a major Shareholder

In state owned enterprises (SOEs), taxes are a dividend to the controlling shareholder, the state, but a cost to other shareholders. We examine publicly traded firms in China and find significantly lower tax avoidance by SOEs relative to non-SOEs. The differences are pronounced for locally versus centrally-owned SOEs and during the year of SOE term performance evaluations. We link our results to managerial incentives through promotion tests, finding that higher SOE tax rates are associated with higher promotion frequencies of SOE managers. Our results suggest managerial incentives and tax reporting are conditional on the ownership structure of the firm.