The Leontief Two-sector Model and Undiscounted Optimal Growth with Irreversible Investment: The Case of Labor-intensive Consumption Goods

This essay reports results on optimal growth in a Leontief two-sector model originally formulated by Bruno and analyzed in discrete time by Nishimura-Yano. Applying geometric methods pioneered by Khan-Mitra, we derive a complete characterization of optimal policy. Assuming that the consumption goods sector is more labor-intensive, our results depart from those in the capital-intensive case: (i) every optimal program is monotonic, (ii) every optimal program converges to the golden-rule stock in a finite number of periods, and (iii) every optimal program undergoes either unemployment or excess capacity of capital.     

氮肥减量及秸秆替代过量氮肥下冬小麦/夏玉米轮作体系氮素淋失风险研究

针对过量施用氮肥和地下水硝酸盐超标的现状,探索减量施氮、秸秆替代过量氮肥下土壤氮素的淋失风险,以期为降低氮素淋失风险提供科学依据。通过在河北省徐水县进行连续4年的冬小麦—夏玉米轮作体系田间定位试验,分析耕作(少耕和常规耕作)、施氮量(无机氮0、200和300 kg/hm~2)和秸秆(还田、不还田)等措施对作物收获后土壤无机氮累积、土壤—作物体系氮平衡状况以及冬小麦和夏玉米产量的影响。结果表明,小麦收获后,过量施氮处理0~100 cm土壤硝态氮累积量显著高于其它处理,高达221 kg/hm~2,过高的土壤残留硝态氮增加玉米高温多雨季氮素淋洗风险。3年氮平衡累积量比较显示,过量施氮、少耕/常规耕作减量施氮秸秆还田3处理的氮平衡值无显著性差异,以少耕减量施氮秸秆还田最低,为236 kg/hm~2,过量施氮处理最高,为281 kg/hm~2,三者均显著高于少耕/常规耕作减量施氮秸秆不还田处理。冬小麦—夏玉米轮作体系氮平衡值与0~100 cm土层的土壤无机氮、土壤硝态氮累积量呈显著正相关,说明氮素大量盈余会导致0~100 cm土壤剖面无机氮大量累积,尤其是硝态氮大量累积。少耕和常规耕作减量施氮秸秆还田处理的冬小麦、夏玉米产量与过量施氮无显著差异。综合考虑土壤无机氮累积量、氮平衡值和作物产量,少耕或者常规耕作下,可以利用秸秆氮替代过量无机氮,降低氮素淋洗风险。     

Asset valuation and accounting strategy within the Japanese shipping industry c.1876-c.1950

The Japanese shipping industry adopted European-style book-keeping in the 1870s. Before 1937, there were few regulations on accounting practices in Japan and we can observe their natural evolution at the Nippon Yusen Kaisha (NYK). NYK, which prospered to become a blue-chip company, developed its accounting techniques in asset valuation exploiting a policy that income and expense from selling securities or vessels should not go directly to the profit and loss account. Asset revaluations were undertaken not to reflect market value but to implement accounting strategy.     

CHANGE DETECTION OF ORDERS IN STOCK MARKETS USING A GAUSSIAN MIXTURE MODEL

We propose a method for detecting changes in the order balance in stock markets by applying a stochastic model to the feature vectors extracted from the order‐book data of stocks. First, the data are divided into training and test periods. Next, a Gaussian mixture model is estimated from the feature vectors extracted from the order‐book data in the training period. Finally, the goodness of fit of the feature vectors in the test period over this model is calculated. Using the proposed method, we found that the order balances of stocks for which insider trading was reported were unusual. Copyright © 2014 John Wiley & Sons, Ltd.