加工劣质原料油增产丙烯专用催化剂设计

为了扩大催化裂化进料来源,中山天乙石化公司在该公司产量为40万t/a的催化裂化装置上加工M-100类劣质原料油,该类原料油性质较差、金属含量高、裂化难度大,因此,对所使用的催化剂提出了苛刻的要求。中石化石油化工研究院(RIPP)和中石化催化剂长岭分公司设计并生产出该装置所专用的催化剂CMO,经实验评价,CMO具有很好的活性和活性稳定性、优异的抗金属污染性能、优秀的重油转化能力,能有效地增产丙烯。     

荧光法测定水质中石油类干扰物质及消除方法的研究

通过对15种物质做了干扰实验,认为荧光分光光度法测定石油类主要为某些物质的负干扰作用,并分析了产生这些负干扰的原因,提出采用标准加入法可以有效地克服这些负干扰。     

Proposal for calculating regulatory capital requirements for reverse mortgages

In the current context in which many people worry about the sustainability of pension systems, reverse mortgages are gaining popularity because they are a way to supplement elderly people's incomes. However, it is necessary to provide banks with an adequate risk measurement and management procedure for reverse mortgages to increase the commercialization of these products, which will result in greater well-being for the retirement age population. In this paper, we propose a method to measure risk and estimate the regulatory capital requirements for a portfolio of reverse mortgages owned by a financial institution according to Basel II and III. The method considers house price risk, mortality risk and interest rate risk; consequently, regulatory capital requirements need to be computed using a Monte Carlo simulation procedure. The proposed method is general and can accommodate several scenarios for reverse mortgage specifications, including fixed or variable mortgage rates and different income stream schemes (with the lump sum as a particular case). The results for the U.K. show that reverse mortgage providers face higher risk when the lender initially advances a higher amount, with the lump-sum case indicating the highest risk, for relatively younger borrowers, the female population, higher interest rates and floating mortgage rates.     

Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx

We extend neural basis expansion analysis (NBEATS) to incorporate exogenous factors. The resulting method, called NBEATSx, improves on a well-performing deep learning model, extending its capabilities by including exogenous variables and allowing it to integrate multiple sources of useful information. To showcase the utility of the NBEATSx model, we conduct a comprehensive study of its application to electricity price forecasting tasks across a broad range of years and markets. We observe state-of-the-art performance, significantly improving the forecast accuracy by nearly 20% over the original NBEATS model, and by up to 5% over other well-established statistical and machine learning methods specialized for these tasks. Additionally, the proposed neural network has an interpretable configuration that can structurally decompose time series, visualizing the relative impact of trend and seasonal components and revealing the modeled processes’ interactions with exogenous factors. To assist related work, we made the code available in a dedicated repository.     

The shale oil revolution and the global oil supply curve

We focus on the implications of the shale oil boom for the global supply of oil. In order to derive testable implications, we introduce a simple stylized model with two producers, one facing low production costs and one higher production costs but potentially lower adjustment costs, competing à la Stackelberg. We find that the supply function is flatter for the high cost producer and that the supply function for shale oil producers becomes more responsive to demand shocks when adjustment costs decline. On the empirical side, we apply an instrumental variable approach using estimates of demand-driven oil price changes derived from a standard structural VAR of the oil market. A main finding is that global oil supply is rather vertical, with a short-term elasticity around 0.05. A rolling sample reveals that the shale oil boom does not appear to have fundamentally changed the contours of global oil production, but there is evidence for the oil supply curve to become more vertical in Saudi Arabia and more price responsive in the United States.     

Filtration enlargement-based time series forecast in view of insider trading

This survey reviews filtration enlargement models in view of insider trading. Although filtration enlargement aptly models insiders' informational advantage, the theoretical results have not attracted the attention of the empiricists, owing mainly to the lack of a bridge transforming the results to testable hypotheses, and/or the absence of econometrics method linking the hypotheses and the data. This survey provides a feasible avenue to estimate insider information and to detect trading from a relatively sophisticated theoretical model, where the dynamics of publicly available data (e.g., stock price) implies insider information before the information is completely digested. We complete the survey with an empirical illustration based on simulated data.     

基于广义虚拟经济视角下的中国居民消费与房价关系研究

根据居民消费理论,一般而言财富增加会促进消费,国外大量经验研究已证实了房价对消费存在正向的作用。但对于中国而言,房价上涨(财富增加)似乎并没有带动消费的增加?这是什么原因呢?我们认为房价上涨不仅有财富效应,也有与之相对应的替代效应(或称为挤出效应),这是因为短期内房价上涨使得人们购房支出增加而挤出日常消费。另一方面,基于广义虚拟经济视角的消费者心理分析也能更好地解释这一现象。本文利用1999-2010年我国的省际面板数据进行估计后发现,在我国,房价上涨会对消费的变动产生显著的负影响。对此,本文认为,这主要是由于替代效应造成的,同时中国以家庭为观念的社会结构也使得人们宁愿挤出日常消费也要承受"房奴"的生活。     

油价冲击、货币政策调整与产出波动——基于中国的经验证据

本文基于我国2001-2010年宏观经济月度数据,采用SVAR模型分析了国际油价波动时,央行货币政策在排除回应油价干扰与未排除干扰下的反应差异及油价波动对产出的影响。研究发现,在排除货币政策回应油价波动干扰后,通过脉冲响应函数反映的油价波动对产出的短期负面影响消失。方差分解结果显示,长期内产出波动由油价冲击和货币政策解释的比例分别为5716%和32480%,比排除干扰前分别下降了2569%和4560%。这说明我国油价冲击带来的经济衰退主要是因为货币政策及其回应油价冲击紧缩所致。此外,面对油价的短期冲击,CPI指数并未随着生产者购进价格指数上升而上升,产出也未发生明显的衰减;但在较长时间内,油价上升会因为相对价格的改变,而影响CPI水平和货币政策,从而对产出产生显著的负面影响。     

International Comparisons of Australia's Infrastructure Performance

International benchmarking has played a key role in the reform of Australia's infrastructure industries by promoting indirect or yardstick competition, in highlighting the need for further reform and building a consensus for implementing necessary changes. The international benchmarking of infrastructure performance by the Bureau of Industry Economics (BIE) between 1991 and 1996 did much to focus attention on the need for change. The BIE examined the performance of eight infrastructure industries relative to international best practice: electricity, rail freight, telecommunications, the waterfront, road freight, coastal shipping, aviation and gas supply. Performance comparisons concentrated on indicators of price, service quality, labour productivity and capital productivity. This paper reviews and summarises the performance comparisons the BIE undertook over the last three years of its infrastructure benchmarking program. The BIE international benchmarking program provides a useful role model for other countries wanting to seriously pursue reform of their infrastructure industries.     

Forecasting house prices using dynamic model averaging approach: Evidence from China

Forecasting house price has been of great interests for macroeconomists, policy makers and investors in recent years. To improve the forecasting accuracy, this paper introduces a dynamic model averaging (DMA) method to forecast the growth rate of house prices in 30 major Chinese cities. The advantage of DMA is that this method allows both the sets of predictors (forecasting models) as well as their coefficients to change over time. Both recursive and rolling forecasting modes are applied to compare the performance of DMA with other traditional forecasting models. Furthermore, a model confidence set (MCS) test is used to statistically evaluate the forecasting efficiency of different models. The empirical results reveal that DMA generally outperforms other models, such as Bayesian model averaging (BMA), information-theoretic model averaging (ITMA) and equal-weighted averaging (EW), in both recursive and rolling forecasting modes. In addition, in recent years it is found that the Google search index, instead of fundamental macroeconomic or monetary indicators, has developed greater predictive power for house price in China.