The Prediction for the Consumer Price Index of Residents in Perspective of Time Series Method in Case of Chongqing

The consumer price index (CPI) measures the relative number of changes in the price level of consumer goods and services over time, reflecting the trend and degree of changes in the price level of goods and services purchased by residents. This article uses the ARMA model to analyze the fluctuation trend of the CPI (taking Chongqing as an example) and make short-term predictions. To test the predictive performance of the model, the observation values from January to December 2023 were retained as the reference object for evaluating the predictive accuracy of the model. Finally, through trial predictions of the data from May to August 2023, it was found that the constructed model had good fitting performance.

Production Chain Length and PPI-CPI Divergence:Analysis Based on the Global Input-output Price Model

Productivity and international energy price shocks are reflected in PPI and CPI via industrial chains.China’s in-depth participation into the global value chains has increasingly lengthened its industrial production chains.The question is how the changing length of production chains will affect CPI and PPI,as well as CPI-PPI correlation?By constructing a global input-output price model,this paper offers a theoretical discussion on the impact of production chain length on the CPI-PPI divergence.Our findings suggest that the price shock of international bulk commodities has a greater impact on China’s PPI than that on CPI.The effects on both China’s PPI and CPI estimated by using the single-country input-output model are higher than the results estimated with the global input-output model.However,the difference between CPI and PPI variations estimated with the global input-output model is greater than the result estimated with the single-country input-output model,which supports the view that the lengthening of production chains,especially international production chains,leads to a divergence between CPI and PPI.Empirical results based on cross-national panel data also suggest that the lengthening of production chains has reduced the CPI-PPI correlation for countries,i.e.the lengthening of production chains has increased the PPI-CPI divergence.That is to say,policymakers should target not just CPI in maintaining price stability,but instead focus on the stability of both PPI and CPI.Efforts can be made to proactively adjust the price index system,and formulate the industrial chain price index.

Joint Detection of Serum Vitamin D,Body Mass Index,and Tumor Necrosis Factor Alpha for the Diagnosis of Crohn’s Disease

Objective Vitamin D(VD)deficiency was reported to contribute to the progression of Crohn’s disease(CD)and affect the prognosis of CD patients.This study investigated the role of serum VD,body mass index(BMI),and tumor necrosis factor alpha(TNF-α)in the diagnosis of Crohn’s disease.Methods CD patients(n=76)and healthy subjects(n=76)were enrolled between May 2019 and December 2020.The serum 25-hydroxyvitamin D[25(OH)D]levels,BMI,and TNF-αlevels,together with other biochemical parameters,were assessed before treatment.The diagnostic efficacy of the single and joint detection of serum 25(OH)D,BMI,and TNF-αwas determined using receiver operating characteristic(ROC)curves.Results The levels of 25(OH)D,BMI,and nutritional indicators,including hemoglobin,total protein,albumin,and high-density lipoprotein cholesterol,were much lower,and the TNF-αlevels were much higher in the CD patients than in the healthy subjects(P<0.05 for all).The areas under the ROC curve for the single detection of 25(OH)D,BMI,and TNF-αwere 0.887,0.896,and 0.838,respectively,with the optimal cutoff values being 20.64 ng/mL,19.77 kg/m^(2),and 6.85 fmol/mL,respectively.The diagnostic efficacy of the joint detection of 25(OH)D,BMI,and TNF-αwas the highest,with an area under the ROC curve of 0.988(95%CI:0.968–1.000).Conclusion The joint detection of 25(OH)D,TNF-α,and BMI showed high sensitivity,specificity,and accuracy in CD diagnosis;thus,it would be effective for the diagnosis of CD in clinical practice.

The Virtual Repeat Sale Model for the House Price Index for New Building in China

By using the characteristics of the new building in China, this article constructs the virtual repeat sale method to produce virtual repeat data which is similar to the repeat sale model on the house price index. Case-Shiller procedure and OFHEO method are used to calculate the house price index for new building in China. A discussion is given and furthering models are needed to take advantage of the virtual repeat sale data.

Women’s empowerment and food consumption:Evidence from female-headed households in Tanzania

Despite the growing recognition of women’s increasing role in the household and corresponding empowerment programs in sub-Saharan Africa,intensive research on the relationship between women’s influence and household food consumption is minimal.Using the most recent(2017-2018)national household survey data from Tanzania,this study examined the influence of women’s empowerment on household food consumption.First,we compared the monthly consumption of eight food categories between female-headed households(FHHs)and male-headed households(MHHs)using both descriptive statistics and the propensity score matching(PSM)method.Furthermore,we adopted the two-stage Linear Expenditure System and Almost Ideal Demand System model(LES-AIDS)to estimate income and price elasticities for the two household types.The results show that FHHs consume bread and cereals,fish,oils and fats,vegetables,and confectionery(sugar,jam,honey,chocolate,etc.)more than MHHs.Moreover,FHHs have a significantly higher income elasticity of demand for all food groups than MHHs.They are also more price elastic than MHHs in meat,fish,oils,fats,sugar,jam,honey,chocolate,etc.

Is pass‑through of the exchange rate to restaurant and hotel prices asymmetric in the US?Role of monetary policy uncertainty

This study examines the exchange rate pass-through to the United States(US)restaurant and hotel prices by incorporating the effect of monetary policy uncertainty over the period 2001:M12 to 2019:M01.Using the nonlinear autoregressive distributed lag(NARDL)model,empirical evidence indicates asymmetric pass-through of exchange rate and monetary policy uncertainty.Moreover,a stronger pass-through effect is observed during depreciation and a negative shock in monetary policy uncertainty,corroborating asymmetric pass-through predictions.Our results further show that a positive shock in energy prices leads to an increase in restaurant and hotel prices.Furthermore,asymmetric causality indicates that a positive shock in the exchange rate causes a positive shock to restaurant and hotel prices.We found feedback causal effects between positive and negative shocks in monetary policy uncertainty and positive and negative shocks in the exchange rate.Additionally,we detected a one-way asymmetric causality,flowing from a positive(negative)shock to a positive(negative)shock in energy prices.Therefore,these findings provide insights for policymakers to achieve low and stable prices in the US restaurant and hotel industry through sound monetary policy formulations.Highlights.The drivers of restaurant and hotel business in tourism destinations are examined.There is asymmetric pass-through of exchange rate and monetary policy uncertainty.A stronger pass-through is observed during appreciation and a negative shock to monetary policy uncertainty.There is asymmetric causality from positive shock in exchange rate to postive shock in restaurant and hotel prices.

Do U.S.economic conditions at the state level predict the realized volatility of oil‑price returns?A quantile machine‑learning approach

Because the U.S.is a major player in the international oil market,it is interesting to study whether aggregate and state-level economic conditions can predict the subse-quent realized volatility of oil price returns.To address this research question,we frame our analysis in terms of variants of the popular heterogeneous autoregressive realized volatility(HAR-RV)model.To estimate the models,we use quantile-regression and quantile machine learning(Lasso)estimators.Our estimation results highlights the dif-ferential effects of economic conditions on the quantiles of the conditional distribution of realized volatility.Using weekly data for the period April 1987 to December 2021,we document evidence of predictability at a biweekly and monthly horizon.

The predictive power of Bitcoin prices for the realized volatility of US stock sector returns

This paper is motivated by Bitcoin’s rapid ascension into mainstream finance and recent evidence of a strong relationship between Bitcoin and US stock markets.It is also motivated by a lack of empirical studies on whether Bitcoin prices contain useful information for the volatility of US stock returns,particularly at the sectoral level of data.We specifically assess Bitcoin prices’ability to predict the volatility of US composite and sectoral stock indices using both in-sample and out-of-sample analyses over multiple forecast horizons,based on daily data from November 22,2017,to December,30,2021.The findings show that Bitcoin prices have significant predictive power for US stock volatility,with an inverse relationship between Bitcoin prices and stock sector volatility.Regardless of the stock sectors or number of forecast horizons,the model that includes Bitcoin prices consistently outperforms the benchmark historical average model.These findings are independent of the volatility measure used.Using Bitcoin prices as a predictor yields higher economic gains.These findings emphasize the importance and utility of tracking Bitcoin prices when forecasting the volatility of US stock sectors,which is important for practitioners and policymakers.

Association between childhood obesity and gut microbiota:16S rRNA gene sequencing-based cohort study

BACKGROUND This study aimed to identify characteristic gut genera in obese and normal-weight children(8-12 years old)using 16S rDNA sequencing.The research aimed to provide insights for mechanistic studies and prevention strategies for childhood obesity.Thirty normal-weight and thirty age-and sex-matched obese children were included.Questionnaires and body measurements were collected,and fecal samples underwent 16S rDNA sequencing.Significant differences in body mass index(BMI)and body-fat percentage were observed between the groups.Analysis of gut microbiota diversity revealed lowerα-diversity in obese children.Differences in gut microbiota composition were found between the two groups.Prevotella and Firmicutes were more abundant in the obese group,while Bacteroides and Sanguibacteroides were more prevalent in the control group.AIM To identify the characteristic gut genera in obese and normal-weight children(8-12-year-old)using 16S rDNA sequencing,and provide a basis for subsequent mechanistic studies and prevention strategies for childhood obesity.METHODS Thirty each normal-weight,1:1 matched for age and sex,and obese children,with an obese status from 2020 to 2022,were included in the control and obese groups,respectively.Basic information was collected through questionnaires and body measurements were obtained from both obese and normal-weight children.Fecal samples were collected from both groups and subjected to 16S rDNA sequencing using an Illumina MiSeq sequencing platform for gut microbiota diversity analysis.RESULTS Significant differences in BMI and body-fat percentage were observed between the two groups.The Ace and Chao1 indices were significantly lower in the obese group than those in the control group,whereas differences were not significant in the Shannon and Simpson indices.Kruskal-Wallis tests indicated significant differences in unweighted and weighted UniFrac distances between the gut microbiota of normal-weight and obese children(P<0.01),suggesting substantial disparities in both the species and quantity of gut microbiota between the two groups.Prevotella,Firmicutes,Bacteroides,and Sanguibacteroides were more abundant in the obese and control groups,respectively.Heatmap results demonstrated significant differences in the gut microbiota composition between obese and normal-weight children.CONCLUSION Obese children exhibited lowerα-diversity in their gut microbiota than did the normal-weight children.Significant differences were observed in the composition of gut microbiota between obese and normal-weight children.

Did weekly economic index and volatility index impact US food sales during the first year of the pandemic?

We explore the impacts of economic and financial dislocations caused by COVID-19 pandemic shocks on food sales in the United States from January 2020 to January 2021.We use the US weekly economic index(WEI)to measure economic dislocations and the Chicago Board Options Exchange volatility index(VIX)to capture the broader stock market dislocations.We validate the NARDL model by testing a battery of models using the autoregressive distributed lags(ARDL)methodology(ARDL,NARDL,and QARDL specifications).Our study postulates that an increase in WEI has a significant negative long-term effect on food sales,whereas a decrease in WEI has no statistically significant(long-run)effect.Thus,policy responses that ignore asymmetric effects and hidden cointegration may fail to promote food security during pandemics.

The diagnostic value of liver stiffness measurement combined with S index in the degree of hepatitis B liver fibrosis

Objective:To investigate the value of liver stiffness measurement(LSM)combined with S index in predicting the degree of liver fibrosis in hepatitis B patients.Methods:A total of 187 chronic hepatitis B patients who were admitted to the Department of Infection,the First Affiliated Hospital of Hainan Medical College from January 2019 to December 2021 were selected.General data were collected,blood routine,liver function,liver fibrosis and liver stiffness measurement were tested,and S index,APRI and FIB-4 index were calculated,and liver biopsy was performed.Results:According to the pathological results of liver puncture,The patients were divided into no significant fibrosis group(n=86),significant fibrosis group(n=71)and cirrhosis group(n=30).There were significant differences in age,PLT,GGT,ALB,S index,HA,LN and LSM levels among the three groups(P<0.05).There was a good correlation between S index and the degree of hepatic fibrosis(rs=0.738,P<0.001).The AUC of S index and LSM for the diagnosis of significant fibrosis in hepatitis B were 0.873 and 0.792,respectively.And the AUC of S index and LSM for the diagnosis of liver cirrhosis were 0.966 and 0.879,respectively.The AUC for the combined diagnosis of significant fibrosis and cirrhosis were 0.908 and 0.988,respectively.The AUROC of combined detection in the diagnosis of cirrhosis was higher than that of LSM,APRI and FIB-4(P<0.05).Conclusion:LSM combined with S index has certain application value in the diagnosis of liver fibrosis/cirrhosis in hepatitis B patients.

Forecasting Model Based on Information-Granulated GA-SVR and ARIMA for Producer Price Index

The accuracy of predicting the Producer Price Index(PPI)plays an indispensable role in government economic work.However,it is difficult to forecast the PPI.In our research,we first propose an unprecedented hybrid model based on fuzzy information granulation that integrates the GA-SVR and ARIMA(Autoregressive Integrated Moving Average Model)models.The fuzzy-information-granulation-based GA-SVR-ARIMA hybrid model is intended to deal with the problem of imprecision in PPI estimation.The proposed model adopts the fuzzy information-granulation algorithm to pre-classification-process monthly training samples of the PPI,and produced three different sequences of fuzzy information granules,whose Support Vector Regression(SVR)machine forecast models were separately established for their Genetic Algorithm(GA)optimization parameters.Finally,the residual errors of the GA-SVR model were rectified through ARIMA modeling,and the PPI estimate was reached.Research shows that the PPI value predicted by this hybrid model is more accurate than that predicted by other models,including ARIMA,GRNN,and GA-SVR,following several comparative experiments.Research also indicates the precision and validation of the PPI prediction of the hybrid model and demonstrates that the model has consistent ability to leverage the forecasting advantage of GA-SVR in non-linear space and of ARIMA in linear space.

Bitcoin:Exploring Price Predictability and the Impact of Investor Sentiment

This article addresses the predictability of Bitcoin’s price by examining relationships between Bitcoin and financial and emotional variables such as the Fear and Greed Index(FGI),the American Interest Rate(FED),and the Stock Market Index(NASDAQ).Through the use of statistical techniques such as the Johansen Cointegration Test and Granger Causality,as well as forecasting models,the study reveals that,despite the notorious volatility of the cryptocurrency market,it is possible to identify consistent behavioral patterns that can be successfully used to predict Bitcoin returns.The approach that combines VAR models and neural networks stands out as an effective tool to assist investors and analysts in making informed decisions in an ever-changing market environment.

The Empirical Analysis of the Relationship between our Country Steel Price Index and the International Iron ore Freight Rate

The use of econometric methods to analyze the relationship between our country steel price index and the international iron ore freight rate,time series stationarity test,cointegration test,Granger test of causality and model parameter estimation tools use,find that there is Granger causality between our country steel price index and the international iron ore freight rate,China' s steel price fluctuations to some extent affect the international iron ore freight.

基于BiLSTM-Attention的F_(10.7)指数预测模型与中国自主数据集的应用

F_(10.7)指数是太阳活动的重要指标,准确预测F_(10.7)指数有助于预防和缓解太阳活动对无线电通信、导航和卫星通信等领域的影响.基于F_(10.7)射电流量的特性,在双向长短时记忆网络(Bidirectional Long Short-Term Memory Network,BiLSTM)基础上融入注意力机制(Attention),提出了一种基于BiLSTM-Attention的F_(10.7)预报模型.在加拿大DRAO数据集上其平均绝对误差(MAE)为5.38,平均绝对百分比误差(MAPE)控制在5%以内,相关系数(R)高达0.987,与其他RNN模型相比拥有优越的预测性能.针对中国廊坊L&S望远镜观测的F_(10.7)数据集,提出了一种转换平均校准(Conversion Average Calibration,CAC)方法进行数据预处理,处理后的数据与DRAO数据集具有较高的相关性.基于该数据集对比分析了RNN系列模型的预报效果,实验结果表明,BiLSTM-Attention和BiLSTM两种模型在预测F_(10.7)指数方面具有较好的优势,表现出较好的预测性能和稳定性.

New indices to balanceα-diversity against tree size inequality

The number and composition of species in a community can be quantified withα-diversity indices,including species richness(R),Simpson’s index(D),and the Shannon-Wiener index(H΄).In forest communities,there are large variations in tree size among species and individu-als of the same species,which result in differences in eco-logical processes and ecosystem functions.However,tree size inequality(TSI)has been largely neglected in studies using the available diversity indices.The TSI in the diameter at breast height(DBH)data for each of 99920 m×20 m forest census quadrats was quantified using the Gini index(GI),a measure of the inequality of size distribution.The generalized performance equation was used to describe the rotated and right-shifted Lorenz curve of the cumulative proportion of DBH and the cumulative proportion of number of trees per quadrat.We also examined the relationships ofα-diversity indices with the GI using correlation tests.The generalized performance equation effectively described the rotated and right-shifted Lorenz curve of DBH distributions,with most root-mean-square errors(990 out of 999 quadrats)being<0.0030.There were significant positive correlations between each of threeα-diversity indices(i.e.,R,D,and H’)and the GI.Nevertheless,the total abundance of trees in each quadrat did not significantly influence the GI.This means that the TSI increased with increasing spe-cies diversity.Thus,two new indices are proposed that can balanceα-diversity against the extent of TSI in the com-munity:(1−GI)×D,and(1−GI)×H’.These new indices were significantly correlated with the original D and H΄,and did not increase the extent of variation within each group of indices.This study presents a useful tool for quantifying both species diversity and the variation in tree sizes in forest communities,especially in the face of cumulative species loss under global climate change.

融合情感分析和GAN-TrellisNet的股价预测方法

将时序深度神经网络应用于股票价格预测,已成为量化金融领域的重要研究方向。时序神经网络具有很好的序列数据捕捉能力和学习记忆能力,在股票预测上有一定适用性。但是现有的模型大多存在预测准确度不高、模型结构复杂导致训练时间较长等问题.为了解决以上问题,提出了一种基于情感分析和GAN-TrellisNet的股价预测方法。提出了一个基于LSTM-CNN的情感分析模型,用于分析爬虫获取的主流金融论坛股票评论,并获得股票情感指数。为了提高预测准确度,将情感指数和百度搜索指数加入股票交易数据中作为训练集,提出了一个基于TrellisNet和CNN的改进型GAN股价预测模型,利用TrellisNet生成器的卷积特性来捕捉数据的局部特征,选取特征提取能力较强的CNN作为判别器来区别预测结果和真实股价。通过选取10只代表性股票和三种大盘指数的不同时段数据进行算法验证,结果表明,与ConvLSTM和GAN-LSTM预测模型相比,GAN-TrellisNet模型能有效缩短训练时间,提高预测准确率。

基于CSLE的泛第三极地区土壤侵蚀强度空间分布特征

[目的]定量评估泛第三极地区的土壤侵蚀状况,明确土壤侵蚀的分布特征,以期为土壤侵蚀的科学防治提供依据。[方法]基于分层不等概系统抽样方法,共布设31 406个调查单元。利用中国土壤流失方程CSLE计算泛第三极地区土壤侵蚀速率,并利用莫兰指数(I)探讨其分布特征。[结果](1)泛第三极地区平均土壤侵蚀速率为162.73 t/(km^(2)·a),水土流失面积比例为7.75%,土壤侵蚀速率超过500 t/(km^(2)·a)的地区主要位于西亚、东亚和南亚;(2)土壤侵蚀存在明显的空间聚集性(I=0.920 9),以高高型和低低型聚集为主,高高型聚集主要分布在西亚北部、南亚北部和西南部以及中国的东北、黄土高原和西藏南部;(3)草地、灌丛和耕地是土壤侵蚀较严重的土地利用类型,其中>1 000 m的草地、>500 m的灌丛和耕地的土壤侵蚀更严重,超过25%的土地都发生了水土流失,平均土壤侵蚀速率变化于426.85~683.72 t/(km^(2)·a)。[结论]海拔介于1 000~6 000 m的草地、500~4 000 m的灌丛以及1 000~4 000 m的耕地是泛第三极地区土壤侵蚀发生的重点区域,需特别关注。

基于麻雀搜索优化SVR模型的房地产价格研究

为解决传统经济指标作为房价影响因素的数据获取滞后性问题以及机器学习模型在预测房价时存在的参数选取不确定性问题,文中以网络搜索数据作为房价指数解释变量,采用麻雀搜索算法(Sparrow Search Algorithm,SSA)建立SSA-SVR(Support Vector Regression)模型对SVR的惩罚因子C和RBF(Radical Basic Function)核函数的参数g进行优化。将SSA-SVR模型与PSO(Particle Swarm Optimization)-SVR、GA(Genetic Algorithm)-SVR、WOA(Whale Optimization Algorithm)-SVR、GS(Grid Search)-SVR以及基准SVR进行对比,SSA-SVR的相关系数(0.99)、均方根误差(6.71)、平均绝对误差(5.24)、均方误差(45.13)以及平均绝对百分比误差(0.26%)均优于其他5种模型。结果表明,麻雀搜索算法优化的SVR模型在房价预测方面具有更好的全局寻优能力,可以提高模型的预测准确度和预测能力。

Elevation transition of aquatic insects closely matches a thermal feature in the Yungas of Northwestern Argentina

Temperature is a key factor that shapes the distribution of organisms.Having knowledge about how species respond to temperature is relevant to devise strategies for addressing the impacts of climate change.Aquatic insects are particularly vulnerable to climate change,yet there is still much to learn about their ecology and distribution.In the Yungas ecoregion of Northwestern Argentina,cold-and warm-adapted species of the orders Ephemeroptera,Plecoptera,and Trichoptera(EPT)are segregated by elevation.We modeled the ecological niche of South American EPT species in this region using available data and projected their potential distribution in geographic space.Species were grouped based on their ecogeographic similarity,and we analyzed their replacement pattern along elevation gradients,focusing on the ecotone where opposing thermal preferences converge.Along this interface,we identified critical points where the combined incidence of cold and warm assemblages maximizes,indicating a significant transition zone.We found that the Montane Cloud Forest holds the interface,with a particularly greater suitability at its lower boundary.The main axis of the interface runs in a N-S direction and falls between 14°C-16°C mean annual isotherms.The probability of a particular location within a basin being classified as part of the interface increases as Kira’s warmth index approaches a score around 150.Understanding the interface is critical for defining the thermal limits of species distribution and designing biomonitoring programs.Changes in the location of thermal constants related to mountainous ecotones may cause vertical displacement of aquatic insects and vegetation communities.We have recognized significant temperature thresholds that serve as indicators of suitability for the interface.As global warming is anticipated to shift these indicators,we suggest using them to monitor the imprints of climate change on mountain ecosystems.