中国船舶工业绿色全要素生产率及影响因素分析——基于ML生产率视角对“波特假说”的再检验

选用船舶工业宏观数据和资源环境约束等作为基础数据,运用方向性距离函数和ML指数测算了中国船舶工业绿色全要素生产率(Total Factor Productivity),通过面板模型系统考察了环境规制、技术进步对中国船舶工业绿色全要素生产率的作用。发现从长远来看,绿色全要素生产率呈增长趋势且具有明显的收敛特征,技术进步和技术效率共同推动船舶工业绿色全要素生产率的增长,但整体上绿色全要素生产率低于传统全要素生产率。运用动态面板模型分析中国船舶工业绿色全要素生产率的影响因素,结论表明:较高的技术水平能显著提高绿色全要素生产率,而对国际市场的过度依赖阻碍了船舶工业绿色全要素生产率的增长。环境规制对船舶工业绿色全要素的增长已经带来"补偿效应"从而提高了中国船舶工业的国际竞争力,为考察船舶工业绿色竞争力提供了新的视角。     

The M4 Competition: Results,findings, conclusion and way forward

The M4 competition is the continuation of three previous competitions started more than 45 years ago whose purpose was to learn how to improve forecasting accuracy, and how such learning can be applied to advance the theory and practice of forecasting. The purpose of M4 was to replicate the results of the previous ones and extend them into three directions: First significantly increase the number of series, second include Machine Learning (ML) forecasting methods, and third evaluate both point forecasts and prediction intervals. The five major findings of the M4 Competitions are: 1. Out Of the 17 most accurate methods, 12 were “combinations” of mostly statistical approaches. 2. The biggest surprise was a “hybrid” approach that utilized both statistical and ML features. This method’s average sMAPE was close to 10% more accurate than the combination benchmark used to compare the submitted methods. 3. The second most accurate method was a combination of seven statistical methods and one ML one, with the weights for the averaging being calculated by a ML algorithm that was trained to minimize the forecasting. 4. The two most accurate methods also achieved an amazing success in specifying the 95% prediction intervals correctly. 5. The six pure ML methods performed poorly, with none of them being more accurate than the combination benchmark and only one being more accurate than Naïve2. This paper presents some initial results of M4, its major findings and a logical conclusion. Finally, it outlines what the authors consider to be the way forward for the field of forecasting.     

广义空间调制系统的正则化OMP检测算法

在广义空间调制（GSM）系统中,最大似然（ML）检测可以取得最优的检测性能,然而其计算复杂度随激活天线数的增加急剧增长。针对这一问题,提出了一种基于稀疏重构理论的低复杂度检测算法——正则化正交匹配追踪（ROMP）算法。该算法首先根据信道矩阵和当前残差的内积选取多个候选激活天线索引,接着对候选天线索引按正则化标准进行可靠性验证,剔除错误索引,缩小信号的搜索空间,最后通过求解最小二乘问题估计信号。仿真结果表明,与经典的正交匹配追踪（OMP）算法相比,所提算法以少许复杂度的增加为代价极大提升了检测性能,能够在检测性能与复杂度之间取得更好的折中。     

中国农业绿色水资源利用效率及时空分异研究

为全面客观了解我国农业绿色水资源利用效率的时空分异特征,推动水生态文明建设和农业绿色用水,运用包含非期望产出的SE-SBM模型,以农业面源污染作为非期望产出,测算了2002—2020年中国31个省级行政区的农业绿色水资源利用效率,并采用ML指数、泰尔指数、“效率-生产率”方阵分析了水资源效率的时空分异特征。结果表明:26个省级行政区的农业绿色水资源利用效率呈平稳波动状态,且大小顺序为东部地区、东北地区、中部地区、西部地区,跨期动态发展各具优劣势;ML指数先减小后增大,73.68%的研究期内EC与TC呈“此消彼长”状态;水资源效率总体差距的62.11%来自主要由西部地区构成的区域内差距,但区域间差距也不可忽视。     

中国省际经济增长的空间关联网络结构--基于非线性Granger因果检验方法的再考察

文章基于经济增长空间传导的非线性趋势,利用最新发展起来的非线性Granger因果检验技术,对中国省际经济增长的空间溢出关系进行了识别,并在社会网络分析框架下揭示了其网络结构特征。研究发现：（1）线性 Granger因果检验方法严重低估了中国省际经济增长的空间关联性,而采用非线性Granger因果检验方法不仅可以揭示出更多的空间关联关系,而且能够识别出更多可能的空间溢出渠道,这为提升中国经济增长的空间协同效应提供了更多的实证支持。（2）中国省际经济增长从整体上呈现出更紧密的、更稳定的网络结构形态,在中国省际经济增长的最大可能性网络（ML-Network）中没有任何一个省份能够“孤立”于整体网络。“不谋全局者,不能谋一域”,必须从全局的、整体的视角重新审视中国区域经济的协调发展,探寻并创造出更多的空间溢出渠道,发挥经济增长不同板块的不同功能,实现区域经济空间的协同发展。     

MIMO系统中的数据检测

BLAST提议在MIMO信道中获取更高的频谱利用率,大部分BLAST算法的研究结果认为,在接收端存在时空的白躁声和干扰。现研究在空间和时间都存在干扰的情况下一个MIMO系统的信道估计和数据检测。得到最大似然信道估计和空间干扰相关矩阵。通过利用已知的时间干扰相关特性,按照最小均方误差准则扩展一个时槽（无效的检测）到对多时槽版本。在独立的瑞利衰落信道下估计MIMO系统中未编码的QPSK的误码率。     

我国蛋鸡养殖环境全要素生产率测度及省域比较分析

［目的］蛋鸡养殖场规模化养殖与生态环境保护协调发展问题是蛋鸡养殖业可持续发展过程中面临的关键性问题,测度我国大、中、小3种规模下不同省份蛋鸡养殖场的环境全要素生产率,并从时间、地区两个维度对蛋鸡养殖场的环境全要素生产率进行比较。［方法］利用2004—2016年不同养殖规模蛋鸡养殖场的投入和产出数据,文章基于SBM函数的Malmquist Luengerber（ML）指数方法,对蛋鸡养殖场环境全要素生产率进行分解,将其分解为技术进步指数、效率改进指数。［结果］（1）蛋鸡养殖场环境全要素生产率整体呈现下降趋势,同时与蛋鸡养殖规模呈反比,蛋鸡养殖场规模越大,蛋鸡养殖环境生产率越小。（2）蛋鸡养殖场效率存在效率改善,但养殖技术进步率相对较低,尤其是对小规模养殖场而言,改善空间更明显; （3）蛋鸡养殖环境全要素生产率区域差异较为明显,整体来看,蛋鸡养殖主产区环境全要素生产率高于非主产区。［结论］加强蛋鸡产业科技创新水平以及公共服务设施的投入,提高养殖户的废弃物无害化处理能力,推行蛋鸡养殖的规模化和标准化发展,进一步改善养殖效率,以促进蛋鸡养殖规模化和生态环境保护协调发展。     

Economic viability of foreign investment in railways: a case study of the China-Pakistan Economic Corridor (CPEC)

Abstract

Pakistan Railways has faced a severe financial crisis in recent years. Pakistan has recently become a partner with China in a mega-investment project under an agreement called the China-Pakistan Economic Corridor (CPEC). Among other things, CPEC also includes a range of investments in Pakistan Railways. This particular study focuses on the analysis of US$8.2 billion investment in the upgrade and expansion of the Karachi-Peshawar railways link, which is also known as the ML-1 (Main Line 1). The study found ML-1 as economically viable with a payback period of 10?years. Furthermore, ML-1 project investment is expected to result in uplifting Pakistan Railways, mainly through an increase in freight and passenger transportation. Some risk factors may hinder the expected economic return from the CPEC investment in Pakistan Railways. These factors include consistency in the government policies, the status of the Pakistani economy in upcoming years, and law and order situations in the country. The study has a utility for the governments of both countries and larger business communities have stakes in the trade between the two countries. It is equally beneficial for the international community, businesses (both in China and Pakistan) and locals of the region associated with the CPEC infrastructure.     

FATF全球ML&TF威胁评估及对我国的启示

2010年下半年,金融行动特别工作组(FATF)发布了第1份全球ML&TF(洗钱和恐怖融资)威胁评估报告(GTA),介绍了全球视野下ML&TF威胁及最终危害的系统性框架,并提供了用来判断和应对这些威胁的指导性方法。本文对该报告进行了翻译和研究,并提出了我国借鉴该评估框架的建议。     

基于最大似然频率精细估计的载波捕获算法

深空通信中,星上对载波的主动捕获主要利用频率估计的方法。在实际载波捕获电路 中,精确的频率估计值导入锁相环,使得锁相环捕获带余量充足。在锁相环带宽一定的情况 下,估计精度的提高可以减少FFT实现点数。在FFT频率粗估计的基础上,通过频率精细估计 算法可提高估计精度。为获得估计精度更高的频率精细估计算法,利用最大似然泛函不变性 推导了频率精细估计的最大似然算法。载噪比在5 dB时,估计精度可以提高到FFT分辨率的1 0 -4。仿真结果表明,该算法估计性能优于其他频率精细估计算法。