进口贸易视域下国际粮价对国内粮价的传导路径--来自玉米和大豆的证据

基于玉米和大豆等粮食国际、国内价格历史数据,运用协整及误差修正模型、脉冲响应函数等方法,揭示国际粮价对国内粮价的传导作用及影响路径。理论分析表明,国际粮价通过进口直接路径、进口产品成本路径和进口替代路径等三个子路径传导至国内粮价。实证检验发现,国际大豆价格进口直接路径和进口产品成本路径传导均比较充分,即国际大豆价格会影响大豆进口价格,进而影响国内大豆价格,最终影响国内豆油价格;国际玉米价格进口直接路径和进口产品成本路径传导则不充分,国际玉米价格会影响玉米进口价格,但是对国内玉米价格和国内玉米淀粉价格影响程度较低;而在进口替代路径中,玉米和大豆的国际价格传导均不充分。我国应继续推进以提质增效为目标的农业供给侧结构性改革,加大粮食生产科技投入力度,调动农民种粮积极性,集中力量提高粮食供给质量和效率;坚持粮食进口仅为调剂国内供求余缺的方针,把握粮食安全的主动权;建立健全粮食市场的价格调控体系及风险防控体系,规避国际粮食市场剧烈波动对国内粮食市场的影响。     

Analysis of Bitcoin prices using market and sentiment variables

This paper proposes an empirical model for analysing the dynamics of Bitcoin prices. To do this, we consider a vector error correction model over two overlapping periods: 2010–17 and 2010–19. Price discovery is achieved through the Gonzalo–Granger permanent‐transitory decomposition. The pricing factors are endogenous linear combinations of the S&P 500 index, gold price, a Google search variable associated to Bitcoin and a fear index proxied by the FED Financial Stress Index. Our empirical analysis shows that during the first period, a linear combination of four pricing factors describes the efficient Bitcoin price. The S&P 500 index and Google searches have a positive effect whereas gold prices and the fear index have a negative effect. In contrast, during the second period, the efficient price behaves idiosyncratically and can be only rationalised by individuals' search for information on the cryptocurrency. These findings provide empirical evidence on the presence of a correction in Bitcoin prices during the period 2018–19 uncorrelated to market fundamentals. We also show that standard empirical asset pricing models perform poorly for explaining Bitcoin prices.     

干旱区内陆河流域未利用地开发生态风险评价及预测*——以开都河流域为例

[目的]探析开都河流域在未利用地开发过程中生态风险指数的变化特征,为西北干旱区内陆河流域土地利用结构调整与生态保护修复协调发展提供建议。[方法]文章采用PSR模型构建基于14个指标框架的流域未利用地开发生态风险评价指标体系;通过测度综合生态风险指数法进行时空视角的特征变化与格局划分评价;并运用灰色预测模型前瞻性模糊预测该区域未来4年的生态风险变化态势。[结果]2009—2016年开都河流域未利用地开发生态风险整体呈波动上升趋势,生态风险程度由较低下降至低生态风险水平,随后上升至一般程度。这是因为土地开发利用对生态环境造成压力,但在政府相应生态保护政策的出台落实下又逐步缓解,生态系统结构和功能好转明显,抵御风险能力得以提升。预测结果显示2017—2020年开都河流域生态风险将由一般生态风险程度上升至较高程度,因此需要采取适当的管理措施来消减生态风险发生的可能性。[结论]开都河流域作为沙漠中典型的绿洲生态系统,生态环境较为脆弱,通过未利用地的差别化开发、鼓励零星分散的开发模式以及细分不同地类开发的生态补偿设置等方式路径,以期缓解降低干旱区内陆河流域未利用地开发带来的生态风险。     

Which Products are More Responsive to In-Store Displays: Utilitarian or Hedonic?

In-store displays aim to boost sales of both utilitarian and hedonic products. Noting typical differences in the information processing and purchase behavior evoked by these product types, and building on congruency theory principles, the authors propose that different types of in-store displays (i.e., island, end-of-aisle, or shelf signage) are more appropriate for utilitarian versus hedonic products, and the use of price or product promotions might reinforce these effects. With a database that combines three data sources (scanner, observational, and survey), this article presents an analysis of a market share model at the SKU level. The results confirm that in-store displays have differential effects on sales, depending on their characteristics; congruency between the decision-making process of utilitarian versus hedonic products and the characteristics of in-store display types moderates their effectiveness in terms of SKU sales. Shelf signage strongly increases the sales of utilitarian products, whereas island and end-of-aisle displays increase sales of hedonic product categories more effectively. The use of congruent promotions creates synergistic influences that reinforce these effects. In particular, price promotions improve the impact of shelf signage on utilitarian products, and product promotions strengthen the impacts of island and end-of-aisle displays on hedonic products. These results extend prior research on in-store marketing actions and the nature of utilitarian versus hedonic products, as well as providing recommendations for retailers and manufacturers seeking to optimize their retail space and commercial budgets.     

基于模糊多属性群决策的维护方式选择研究

维护方式选择是维护管理中的重要工作之一,合理的维护方式既能达到保障设备的稳定运行,又能同时兼顾其它各个方面的要求。由于对维护方式的评价涉及多个部门、人员和属性,有些指标只是一个模糊的概念,因而采用模糊多属性群决策的方法对维护方式进行优先抉择。本文结合A公司的设备维护方式选择问题,尝试使用模糊多属性群决策折衷算法求解最佳的维护方式。     

Data vs. information: Using clustering techniques to enhance stock returns forecasting

This paper explores the use of clustering models of stocks to improve both (a) the prediction of stock prices and (b) the returns of trading algorithms.We cluster stocks using k-means and several alternative distance metrics, using as features quarterly financial ratios, prices and daily returns. Then, for each cluster, we train ARIMA and LSTM forecasting models to predict the daily price of each stock in the cluster. Finally, we employ the clustering-empowered forecasting models to analyze the returns of different trading algorithms.We obtain three key results: (i) LSTM models outperform ARIMA and benchmark models, obtaining positive investment returns in several scenarios; (ii) forecasting is improved by using the additional information provided by the clustering methods, therefore selecting relevant data is an important preprocessing task in the forecasting process; (iii) using information from the whole sample of stocks deteriorates the forecasting ability of LSTM models.These results have been validated using data of 240 companies of the Russell 3000 index spanning 2017 to 2022, training and testing with different subperiods.     

What determines the long-term correlation between oil prices and exchange rates?

In this study, we obtain the long-term correlation between oil prices and exchange rates by employing the dynamic conditional correlation-mixed data sampling (DCC-MIDAS) model. We then identify the factors that influence the long-term correlation using panel data analysis. We find that the long-run correlations between oil prices and exchange rates are negative for all oil-exchange rate markets except Japan. We also find that both inflation and term spread have negative effects, while the risk-free interest rate has a positive effect on the long-term correlation between oil prices and exchange rates. Importantly, the empirical results show that an increase in inflation will significantly damage the real value of the currency itself.     

Testing vertical price transmission for Vietnam's Robusta coffee

Vietnam has undergone market reforms over the last three decades; and as a consequence, the coffee sector has become increasingly market‐driven. The success of the government's liberalisation policies in terms of market efficiency is investigated by examining the transmission of both positive and negative price changes for Robusta coffee between export and farmgate prices. We used a threshold vector error correction model and high‐frequency daily data. The primary result here is that of a symmetric price transmission between export and farm‐level prices. This result holds when tested with weekly price data, derived from the daily data. Farmgate prices respond faster to decreases than increases in export prices when the long‐run deviation exceeds a certain threshold. These price changes are transmitted within several days. This research also confirms the importance of transaction costs, and other price frictions mostly ignored in prior analyses for coffee.     

On the rebound: estimating direct rebound effects for Australian households

Reducing dependence on fossil fuels by decreasing energy consumption is a common environmental policy. One mechanism used to achieve this is to encourage increased energy efficiency. However, improving efficiency may have an opposing effect and cause an increase in energy consumption if the intensity of use changes. This phenomenon is known as the rebound effect. We estimate direct rebound effects for energy use in Australia based on both aggregate residential energy use data and on household energy expenditure data. Our approach implements a new methodology developed by Hunt and Ryan (2014, Catching on the rebound: Why price elasticities are generally inappropriate measures of rebound effects. Surrey Energy Economics Discussion Paper Series SEEDS 148; 2015, Energy Economics 50, 273) that explicitly relates energy service use with energy source demand and directly incorporates measures of efficiency changes. The results indicate that the rebound effect is relatively high for energy use by Australian households. Due to the unique nature of our household data set, we can examine the influence of demographic and housing characteristics. We find that low‐income households and households with vulnerable members have the largest rebound effects. The relatively large rebound effects found here suggest that consumers gain from efficiency by improved energy services, and thus, policy targeting energy efficiency is not likely to be successful at reducing energy consumption.