How Fast are Semiconductor Prices Falling?

The Producer Price Index (PPI) for the U.S. suggests that semiconductor prices have barely been falling in recent years, a dramatic contrast to the rapid declines reported from the mid‐1980s to the early 2000s. This slowdown in the rate of decline is puzzling in light of evidence that the performance of microprocessor units (MPUs) has continued to improve at a rapid pace. Over the course of the 2000s, the MPU prices posted by Intel, the dominant producer of MPUs, became much stickier over the chips' life cycle. As a result of this change, we argue that the matched‐model methodology used in the PPI for MPUs likely started to be biased after the early 2000s and that hedonic indexes can provide a more accurate measure of price change since then. MPU prices fell rapidly through 2004 on every price measure we present, with the PPI declining at an even quicker pace than the hedonic indexes. However, from 2004 to 2009, our preferred hedonic index fell faster than the PPI, and from 2009 to 2013 the gap widened further, with our preferred index falling at an average annual rate of 42 percent, while the PPI declined at only a 6 percent rate. Given that MPUs currently represent about half of U.S. shipments of semiconductors, this difference has important implications for gauging the rate of innovation in the semiconductor sector.     

An information-theoretic approach for forecasting interval-valued SP500 daily returns

We develop an iterative and efficient information-theoretic estimator for forecasting interval-valued data, and use our estimator to forecast the SP500 returns up to five days ahead using moving windows. Our forecasts are based on 13 years of data. We show that our estimator is superior to its competitors under all of the common criteria that are used to evaluate forecasts of interval data. Our approach differs from other methods that are used to forecast interval data in two major ways. First, rather than applying the more traditional methods that use only certain moments of the intervals in the estimation process, our estimator uses the complete sample information. Second, our method simultaneously selects the model (or models) and infers the model’s parameters. It is an iterative approach that imposes minimal structure and statistical assumptions.     

Forecasting using heterogeneous panels with cross-sectional dependence

In this paper, we focus on forecasting methods that use heterogeneous panels in the presence of cross-sectional dependence in terms of both spatial error dependence and common factors. We propose two main approaches to estimating the factor structure: a residuals-based approach, and an approach that uses a panel of auxiliary variables to extract the factors. Small sample properties of the proposed methods are investigated through Monte Carlo simulations and applied to predict house price inflation in OECD countries.     

The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning

Whether investor sentiment affects stock prices is an issue of long-standing interest for economists. We conduct a comprehensive study of the predictability of investor sentiment, which is measured directly by extracting expectations from online user-generated content (UGC) on the stock message board of Eastmoney.com in the Chinese stock market. We consider the influential factors in prediction, including the selections of different text classification algorithms, price forecasting models, time horizons, and information update schemes. Using comparisons of the long short-term memory (LSTM) model, logistic regression, support vector machine, and Naïve Bayes model, the results show that daily investor sentiment contains predictive information only for open prices, while the hourly sentiment has two hours of leading predictability for closing prices. Investors do update their expectations during trading hours. Moreover, our results reveal that advanced models, such as LSTM, can provide more predictive power with investor sentiment only if the inputs of a model contain predictive information.     

Fiscal and monetary interdependence

Where classical economics integrates the quantity theory of money with the concept of Ricardian equivalence, the tendency of recent macroeconomic presentations is to focus either upon money and inflation or upon taxation and debt. That neglect of classical monetary–fiscal integration is surprising, given an initiative by the International Monetary Fund that set credit, money, and fiscal policy within a single structure. This article places those ‘credit counterparts of broad money’ in the context of the Great Depression and the recent global financial crisis. The upshot is a set of conclusions: that, to counter the prospect of deflation, quantitative easing is a weak policy response; that fiscal deficits are better; and that cuts in taxation are preferable to increased government spending.     

Credit risk pricing and the rationality of lending decision-making within dual banking systems: A parametric approach

The reduction of non-performing loans, and making correct provisions for them, plays a primary role in the management and minimization of banking credit risk. However, these actions depend primarily upon the cost at which banks may dispose of these bad loans. Hence, this study aims to perceive the price of banks’ credit risk via estimating the shadow price of non-performing loans. We assess and compare the perceived price of the credit risk of Islamic and conventional banks operating in 9 countries from the Middle East and Asia, using a quadratic directional distance function. Following this, we evaluate the impact of different settings of directional vectors on shadow prices by conducting a risk-sensitivity analysis. Applying bootstrap regression, the factors affecting NPLs’ prices are further investigated. The paper concludes that the estimation of the shadow prices of bad loans can provide important elements in favor of credit risk management and, therefore, credit risk mitigation.     

Environmental policies,national culture,and stock price crash risk: Evidence from renewable energy firms

This study investigates the impact of country‐level environmental performance and national culture on the stock price crash risk of renewable energy firms. Employing a large sample of 626 renewable energy firms across 31 countries, we find a significant nonlinear relationship between country‐level environmental performance and crash risk. National culture dimensions are found to strongly predict the crash risk of renewable energy firms, particularly after the global financial crisis. On the contrary, national culture dimensions and environmental policies are observed to not exert any significance in explaining the crash risk of fossil fuel firms. Our results are robust with respect to alternative measures of stock price crash risk and the endogeneity of national culture dimensions. Overall, the findings of this paper contribute to the environmental economics literature by providing new evidence regarding the role of societal and environmental factors in explaining the stock price crash risk of energy firms.     

化肥价格变化对粮食价格的影响研究--基于省际动态面板数据模型的系统GMM检验

基于我国粮食价格与化肥价格变动趋势的分析,运用2006年~2017年省际动态面板数据模型和系统GMM方法,实证检验了化肥价格变动对粮食价格的影响。结果表明:化肥价格变动对粮食价格存在显著的正向影响,在考虑化肥价格影响下,农业生产服务、种子等农资价格对粮食价格也存在显著的正向影响,机械化农具和农药价格对粮食价格不显著,"价补分离"政策对粮食价格存在显著负向影响。因此,在化肥减量行动背景下,适度调控化肥价格,避免化肥价格上升挤占粮农利润,大力发展农业生产性服务业和种子业,继续实行"价补分离"政策,提高补贴精准性,减少农民投入成本。     

新能源汽车价格补贴政策的绩效分析和改善建议--以J新能源汽车公司为例

新能源汽车是我国汽车行业重要的发展方向,笔者通过研究我国新能源汽车的发展历程、价格补贴政策存在的问题,提出完善新能源汽车价格补贴政策的对策,以期为我国新能源产业的科学发展和我国汽车产业的转型升级提供参考。     

中国生猪价格周期性波动分析、展望及对策

2006年以来,中国生猪价格波动经历了三个半周期,呈现出价格波动周期日益延长、价格波动幅度前窄后宽、传统消费旺季作用逐渐消退等特征。其中既有生猪养殖自身规律、规模化养殖比例提升等内因的影响,也有上游行业价格变动、生猪疫情等外因的影响。综合来看,2019年非洲猪瘟疫情发展、规模化生猪养殖场补栏、气温条件变化及冬季的猪肉旺季消费等情况会对后续的生猪价格波动走势产生较大影响,至2020年春节前生猪价格持续走高。应通过加快生猪养殖产业结构升级、加强生猪市场信息服务体系建设、推进生猪产业链市场化发展等措施,有效减缓中国生猪价格周期波动的影响。