Countering medical nihilism by reconnecting facts and values

A pessimistic strain of thought is fomenting in the health studies literature regarding the status of medicine. Ioannidis’s (2005) now famous finding that “most published research findings are false” and Stegenga’s (2018) book-length argument for medical nihilism are examples of this. In this paper, we argue that these positions are incorrect insofar as they rest on an untenable account of the nature of facts. Proper attention to fallibilism and the social organization of knowledge, as well as Bayesian probabilities in medical reasoning, prompt us to ask why the cynics expect the results of quantitative studies to be incontrovertibly true in the first place. While we agree with Ioannidis and others’ identified flaws in the medical research enterprise, and encourage rectification, we conclude that medical nihilism is not the natural outcome of the current state of research.     

Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors

In this paper, we assess the predictive content of latent economic policy uncertainty and data surprise factors for forecasting and nowcasting gross domestic product (GDP) using factor-type econometric models. Our analysis focuses on five emerging market economies: Brazil, Indonesia, Mexico, South Africa, and Turkey; and we carry out a forecasting horse race in which predictions from various different models are compared. These models may (or may not) contain latent uncertainty and surprise factors constructed using both local and global economic datasets. The set of models that we examine in our experiments includes both simple benchmark linear econometric models as well as dynamic factor models that are estimated using a variety of frequentist and Bayesian data shrinkage methods based on the least absolute shrinkage operator (LASSO). We find that the inclusion of our new uncertainty and surprise factors leads to superior predictions of GDP growth, particularly when these latent factors are constructed using Bayesian variants of the LASSO. Overall, our findings point to the importance of spillover effects from global uncertainty and data surprises, when predicting GDP growth in emerging market economies.     

Predictive ability and economic gains from volatility forecast combinations

The availability of numerous modeling approaches for volatility forecasting leads to model uncertainty for both researchers and practitioners. A large number of studies provide evidence in favor of combination methods for forecasting a variety of financial variables, but most of them are implemented on returns forecasting and evaluate their performance based solely on statistical evaluation criteria. In this paper, we combine various volatility forecasts based on different combination schemes and evaluate their performance in forecasting the volatility of the S&P 500 index. We use an exhaustive variety of combination methods to forecast volatility, ranging from simple techniques to time-varying techniques based on the past performance of the single models and regression techniques. We then evaluate the forecasting performance of single and combination volatility forecasts based on both statistical and economic loss functions. The empirical analysis in this paper yields an important conclusion. Although combination forecasts based on more complex methods perform better than the simple combinations and single models, there is no dominant combination technique that outperforms the rest in both statistical and economic terms.     

Spatial dependence model with feature difference

This paper presents a new spatial dependence model with an adjustment of feature difference. The model accounts for the spatial autocorrelation in both the outcome variables and residuals. The feature difference adjustment in the model helps to emphasize feature changes across neighboring units, while suppressing unobserved covariates that are present in the same neighborhood. The prediction at a given unit incorporates components that depend on the differences between the values of its main features and those of its neighboring units. In contrast to conventional spatial regression models, our model does not require a comprehensive list of global covariates necessary to estimate the outcome variable at the unit, as common macro-level covariates are differenced away in the regression analysis. Using the real estate market data in Hong Kong, we applied Gibbs sampling to determine the posterior distribution of each model parameter. The result of our empirical analysis confirms that the adjustment of feature difference with an inclusion of the spatial error autocorrelation produces better out-of-sample prediction performance than other conventional spatial dependence models. In addition, our empirical analysis can identify components with more significant contributions.     

Forecasting of dependence,market, and investment risks of a global index portfolio

This paper undertakes an in-sample and rolling-window comparative analysis of dependence, market, and portfolio investment risks on a 10-year global index portfolio of developed, emerging, and commodity markets. We draw our empirical results by fitting vine copulas (e.g., r-vines, c-vines, d-vines), IGARCH(1,1) RiskMetrics value-at-risk (VaR), and portfolio optimization methods based on risk measures such as the variance, conditional value-at-risk, conditional drawdown-at-risk, minimizing regret (Minimax), and mean absolute deviation. The empirical results indicate that all international indices tend to correlate strongly in the negative tail of the return distribution; however, emerging markets, relative to developed and commodity markets, exhibit greater dependence, market, and portfolio investment risks. The portfolio optimization shows a clear preference towards the gold commodity for investment, while Japan and Canada are found to have the highest and lowest market risk, respectively. The vine copula analysis identifies symmetry in the dependence dynamics of the global index portfolio modeled. Large VaR diversification benefits are produced at the 95% and 99% confidence levels by the modeled international index portfolio. The empirical results may appeal to international portfolio investors and risk managers for advanced portfolio management, hedging, and risk forecasting.     

Can online search data improve the forecast accuracy of pork price in China?

Online search data provide us with a new perspective for quantifying public concern about animal diseases, which can be regarded as a major external shock to price fluctuations. We propose a modeling framework for pork price forecasting that incorporates online search data with support vector regression model. This novel framework involves three main steps: that is, formulation of the animal diseases composite indexes (ADCIs) based on online search data; forecast with the original ADCIs; and forecast improvement with the decomposed ADCIs. Considering that there are some noises within the online search data, four decomposition techniques are introduced: that is, wavelet decomposition, empirical mode decomposition, ensemble empirical mode decomposition, and singular spectrum analysis. The experimental study confirms the superiority of the proposed framework, which improves both the level and directional prediction accuracy. With the SSA method, the noise within the online search data can be removed and the performance of the optimal model is further enhanced. Owing to the long-term effect of diseases outbreak on price volatility, these improvements are more prominent in the mid- and long-term forecast horizons.     

白洋淀轮虫群落结构及富营养化评价

于2018年5月(春)、8月(夏)和10月(秋)对白洋淀26个采样点轮虫物种组成和群落结构进行了系统调查,测定了水温(WT)、溶解氧(DO)、pH值、水深(WD)、透明度(Tr)、总氮(TN)、总磷(TP)、叶绿素a(Chla)等环境因子,分析了轮虫群落参数与环境因子的关系,综合评价了白洋淀水体富营养化程度.共鉴定出轮虫21属61种,以耐污性种类为主,角突臂尾轮虫、萼花臂尾轮虫和小多肢轮虫为优势种.各采样点轮虫种数差异较大,最多为20种(聚龙淀),最少仅为6种(大麦淀).轮虫平均丰度和生物量呈现明显的季节变化,秋季最高(3 372 ind./L,0.90 mg/L),夏季其次(1 240 ind./L,0.88 mg/L),春季最低(210 ind./L,0.22 mg/L);轮虫最高丰度为6 930 ind./L(三角淀,秋季),最低仅为2 ind./L(平阳淀,夏季).Shannon-Wiener多样性指数(H')和Pielou均匀度指数(J)夏季最高(分别为2.30和0.86).经相关性分析发现,轮虫丰度与DO、WD、Chla、TP呈显著正相关,而与Tr显著负相关;轮虫种数与WD显著正相关,与WT显著负相关.与历史资料相比,白洋淀水质在一定程度有所好转,但仍处于富营养化状态.     

C3N4/ZnO复合材料可见光下的甲醛光电气敏性能

以水热法合成的ZnO纳米棒花为载体,将C_3N_4定量负载在其表面,制备C_3N_4/ZnO复合材料.由于C_3N_4与ZnO的能级匹配,可有效促进其光生电荷的分离,降低光生电荷复合率,显著提高载流子浓度,使其光电流明显增加.C_3N_4与ZnO质量比为2%的样品(2%C_3N_4/ZnO)性能最佳,其光电流在370 nm处可达120μA,为纯ZnO的2.4倍.在室温可见光下,2%C_3N_4/ZnO样品对HCHO的光电气敏响应值可达122%,比纯ZnO高4倍左右.结果表明,适量C_3N_4的负载有效延长了光生电荷的寿命,使ZnO材料在可见光区对HCHO气体表现出高效的光电气敏性能.     

水栖性的棘胸蛙身体大小不随环境水热梯度及净初级生产力梯度而分化

之前的研究发现不同地理种群棘胸蛙(Quasipaa spinosa)的身体大小随着环境生产力以及年最低温的降低而增大,因为更大的身体利于在生产力水平较低的环境中耐受饥饿并利于在寒冷的环境条件下保存热量.然而在种间水平上进行的相关研究发现:由于大部分时间生活在水体这种相对稳定的环境中,水栖性无尾类的身体大小并不受宏观水热波动的影响.为了更好地探究这个问题,我们以棘胸蛙为研究对象,在之前的基础上选择更多的研究地点及更多的环境因子,研究其身体大小的地理分化格局并探究潜在机制.与之前的发现不同,作为一种水栖性无尾类,棘胸蛙的身体大小并不受环境水热梯度以及净初级生产力梯度的影响.导致这种现象的原因可能是水栖性的生境偏好缓和了棘胸蛙受到的来自宏观环境水热波动的影响,所以它们并不通过改变身体大小来适应宏观环境梯度.此外由于棘胸蛙的肉食性,环境初级生产力并不能有效地衡量其食物供给,其身体大小是否受到食物供给量的影响仍有待深入探究.     

On the Difficulty of Measuring Forecasting Skill in Financial Markets

The use of correlation between forecasts and actual returns is commonplace in the literature, often used as a measurement of investors' skill. A prominent application of this is the concept of the information coefficient (IC). Not only can the IC be used as a tool to rate analysts and fund managers but it also represents an important parameter in the asset allocation and portfolio construction process. Nevertheless, a theoretical understanding of it has typically been limited to the partial equilibrium context where the investing activities of each agent have no effect on other market participants. In this paper we show that this can be an undesirable oversimplification and we demonstrate plausible circumstances in which conventional empirical measurements of IC can be highly misleading. We suggest that improved understanding of IC in a general equilibrium setting can lead to refined portfolio decision making ex ante and more informative analysis of performance ex post. Copyright © 2015 John Wiley & Sons, Ltd.