Confidence Index Research Articles

Do low-cost digital tools allow measuring the cladode area?

This study evaluated low-cost digital tools for estimating cladode area by mobile and fixed devices. We conducted the experiment at the Serra Talhada Academic Unit of the Federal Rural University of Pernambuco with pear cactus (Nopalea cochenillifera Salm-Dyck and Opuntia stricta (Haw.)). In this sense, we used four software to measure the cladode area: LAFore (fixed device), ImageJ (fixed device), Easy Leaf App (mobile device), and Petiole App (mobile device). We considered the LAFore software as a reference and used the following statistical parameters: linear regression, correlation coefficient (r), Willmott index (d), confidence index (c), and standard error of estimate (SEE). The software on fixed devices (ImageJ and LAFore) showed similar results, with performance considered excellent, and the Easy Leaf App showed a very good performance among mobile devices.

Investor sentiment and Euro area ETFs: an empirical analysis of consumer behaviour

The goal of this article is to study the relationship between investor sentiment and Euro area Exchange Traded Funds (ETFs), providing insights into the dynamics of market behaviour and the impact of consumer behaviour on investment patterns within the Eurozone. This paper has employed panel data analysis, correlation analysis, regression analysis, and the Granger causality test to investigate the relationship between ETFs and the consumer confidence index (CCI). Results suggest that CCI significantly negatively affects ETF pricing. While a weak positive correlation and regression relationship between iShares MSCI Eurozone ETF prices and CCI 19 Euro area markets have been found, the Granger causality test did not provide evidence of a causal relationship between the two variables. Other factors, such as financial stability, government regulations, and market-specific factors, may also affect ETF prices. The study concludes that, while CCI may be important, it should not be the only factor considered.

Global Transmission Shocks of U.S. Monetary Policy on China's Housing Prices

The paper demonstrates that the monetary policy of the United States has important spillover effects to China. Based on the monthly data from January 2016 to April 2024, this paper establishes a VAR model to analyze the influence of U.S. monetary policy on China's real estate prices and its transmission channels, including interest rate, exchange rate, consumer confidence, and asset price. The results show that the impact of the Federal Funds Effective Rate on housing prices is very quick, reaching a maximum in the first month and continuing to have a positive impact over the long term. The Federal Funds Effective Rate and China's Loan Prime Rate have a positive impact on housing prices in the early stage, but then there is a change in impact direction. The Exchange Rate, Consumer Confidence Index and CSI 300 index all had positive spillover effects on housing prices in the early days. The variance decomposition analysis shows that the exchange rate channel plays the most significant role in causing China’s real estate price fluctuations.

Translating mineral systems criteria into a prospectivity model for IOCG deposits in the Kolari region, Finland

The mineral system approach facilitates mapping the fingerprints of geological processes in ore formation using mineral prospectivity modeling (MPM). It aims to reduce the time and cost of exploration as a priority in the mineral exploration industry. Finland, which is covered with thick soil, dense vegetation, and snow in winter and thus has limited outcrops, serves as a suitable testing ground for this method. In this contribution, MPM was employed to predict favorable targets for Fe-Cu-Au (IOCG) deposits in the Kolari region, northwestern Finland. The mineral system components ore metal and sulfur sources, pathways, energy sources/drivers, traps, and the geological factors influencing the ore-forming processes were translated into mappable criteria and were considered for a regional-scale analysis. Knowledge-driven, fuzzy logic overlay, fuzzy inference system, and geometric average methods integrate evidential layers derived from geological (scale-free geology map), geochemical (till and bedrock sample geochemistry), and geophysical data (magnetic, radiometric, electromagnetic measurements, and gravity worms). Subsequently, the data were modeled using a combined conceptual/empirical approach, i.e. fuzzified evidential layers and logistic regression. In the prospectivity models, existing IOCG deposits are consistently placed within high-favorability areas. In addition, new exploration targets were identified. The models were validated against known IOCG deposits using Receiver Operating Characteristic (ROC) analysis and average Area Under the Curve (AUC) with values of final prospectivity models consistently reaching scores > 0.8. This indicates a favorable outcome of the application of MPM, making the approach applicable to other regions and ore deposit types. A majority voting ensemble technique was employed to combine the favorable areas of each prospectivity model. Then a confidence index was adopted reducing uncertainty linked to the models generated. These outputs consistently facilitated the identification of exploration targets more reliably.

Election and Wallet: The Effect of Election Periods and Consumer Expectations on Consumer Confidence with ARDL and QARDL Model Approach

The growing significance of consumer confidence and the explanation of the factors influencing this confidence is an important issue worthy of study. This study aims to examine the effects of election periods and consumer expectations on the Consumer Confidence Index (CCI) in Turkey. To this end, the Autoregressive Distributed Lag (ARDL) and Quantile Autoregressive Distributed Lag (QARDL) models will be employed. Data on the variables in question were collected on a monthly basis from Turkish Statistical Institute (TUIK) over the period spanning 2012:1 to 2024:3. The ARDL model findings indicate that election periods exert a positive and significant influence on the TGE, both in the short run and in the long run. The intention to buy or have a house built and the intention to save have positive and significant effects on the TGE in both the short and long run. Conversely, the intention to buy a car and the cost of living index have a positive and significant effects on the TGE in the short run, but no significant effect was found in the long run. The QARDL model reveals that asymmetry is present, with all variables exhibiting disparate effects across different quartiles. The findings of this study indicate that in order to enhance consumer confidence and guarantee economic stability, it is essential to prioritise the housing market, savings patterns and reducing the cost of living. The development of policies that ensure the sustainability of the confidence and stability created during election periods is crucial. The research offers invaluable insights into ARDL and QARDL models that can inform Turkey's economic policies.

Does a correlation exist between delayed vaccination and a decreased vaccine confidence?

ABSTRACT Delay in vaccination schedule may result in increased risk of vaccine-preventable outbreaks. Lack of parental confidence in vaccines and sociodemographic factors could be related to those delays. Vaccine Confidence Index (VCI) is an indicator to estimate confidence in vaccines, composed of simple dimensions about it and evaluated using a 5-point Likert scale. Objectives of this study, conducted in the Region of Murcia, Spain, were to determine which sociodemographic factors were associated with delayed administration in measles, mumps and rubella (MMR) vaccine, both first and second doses, and to evaluate if there were differences in parental vaccine confidence of children who were delayed on MMR vaccine schedule compare to vaccinated on time. The 2019–2020 (first dose) and 2016–2017 (second dose) cohorts were divided into four categories: vaccinated on time, slightly delayed, moderately delayed and highly delayed. Online questionnaire was sent to their parents, followed by a telephone questionnaire for non-responders. Regarding MMR first dose, a significant association was found between delay and birth order and intellectual/motor delay of children. Parental educational status, the main source of vaccine information and the social networks more frequently used by parents were also associated with delay. Suffering motor/intellectual delay and the obstacles to vaccination process derived from caring for other siblings were associated with delay in second MMR dose. The VCI decreased significantly in first dose group as delay increases but did not change in the second one. Confidence in vaccines and sociodemographic factors, respectively, were associated with delaying MMR first and second dose.

Integrated Systematic Framework for Forecasting China’s Consumer Confidence: A Machine Learning Approach

This study aims to introduce a novel approach for predicting China’s consumer confidence index (CCI), a key economic indicator that reflects consumers’ confidence in current and future economic conditions. While traditional statistical models and economic indicators are the primary tools for forecasting CCI, their reliance on linear assumptions limits their ability to capture the complex, dynamic relationships inherent in economic systems. In response, this study proposes a two-step method that integrates social network analysis (SNA) and machine learning (ML) to enhance prediction accuracy by accounting for the nonlinear interactions and systemic interdependencies that drive consumer confidence. The use of SNA enables the identification of critical variables and their interconnected roles in shaping consumer sentiment, while ML models, specifically the gradient boosting decision tree (GBDT), leverage these relationships to provide more precise predictions. Utilizing monthly data from 1999 to 2023, the combined SNA and GBDT approach significantly improves the accuracy of CCI forecasts, particularly during periods of high volatility. The results of this study hold substantial value for policymakers, market analysts, and economists, as they offer a systems-oriented framework for economic forecasting. By demonstrating the effectiveness of combining SNA with ML technologies, this research not only advances the methodological toolkit for economic forecasting, but also provides a new lens through which the complex, adaptive nature of economic systems can be better understood and managed. This integrated approach paves the way for future developments in forecasting models that more accurately reflect the evolving dynamics of consumer confidence in a rapidly changing economic environment.

Dynamics of persistence in Brazilian economic uncertainty, expectation, and confidence indexes

This study examines the persistence dynamics in Brazilian economic uncertainty, confidence, and expectation indexes, in both business and consumer sides of the economy. Using rolling window estimations of the fractional integration parameter, we assess how economic turbulence influences the persistence of these indexes. We find that economic uncertainty in Brazil is non-stationary but mean-reverting, with its persistence only transitorily affected by economic and political crises. Interestingly, confidence and expectation indexes displayed hysteretic behavior, with multiple persistence regimes linked to crises. These results may be informative for policymakers about the perceptions of the public and businessmen to economic shocks and crises.

Integrating Surface-related Indicators of Coverage, Distance and Distribution for Quantifying Scan-to-BIM Confidence Level

Abstract. Scan-to-BIM is a widely-used approach to generate Building Information Modelling and by extension Digital Twin models in the architecture, engineering, and construction sector. The resulting models need to be as accurate as possible to ensure subsequent activities that make use of them can do so effectively. Quality assessment of point clouds and occlusion assessment of BIM outputted from Scan-to-BIM has been investigated. However, Scan-to-BIM systems currently do not provide proper metrics as to the confidence the user can have in the quality, in particular geometric quality of the outputted model. This paper addresses this gap by introducing a confidence index, for analysing the reliability of the generated 3D models and thereby quantifying the confidence the user can have in them. Index of confidence is itself derived from three more specific indices: index of coverage estimates the portion of the surface of the modelled element that is explained by the input point cloud. Index of distribution estimates how well the points explaining the modelled surfaces are distributed around the overall object’s surface. Index of distance defines the closeness of the generated element models to the input point cloud. The proposed indices are assessed using three real examples, demonstrating their adequacy.

The Relationship Between Sectoral Employment and Real Sector Economic Confidence: Fourier Causation Analysis

Purpose –In order to ensure economic growth in the short term and economic development in the long term, it is important to evaluate the events occurring in the political and economic order correctly and to meet the expectations. This study was created to evaluate the effectiveness of economic confidence on the relevant sectors on a sectoral basisDesign/Methodology/Approach –In this study, the relationship between the real sector economic confidence index and employment rates in industry, construction and services sectors will be analyzed. Turkey is selected as the sample and the data range subject to the analysis is determined as 2009:01-2023:08. In the study, the Fourier analysis method, which is among the new generation analysis methods, was applied, and Fractional Frequency Fourier Augmented Dickey Fuller Unit Root Test and Fourier Toda-Yamamoto Causality Test were applied.Findings –As a result of the application, a one-way causality relationship from the real sector economic confidence index to building and industry sector employment was determined. A recommendation should be made regarding the necessity of making political arrangements that will maintain the stability level of employment in the relevant sector.Discussion –In line with the data obtained in this study, the fact that real economic confidence has an effect on employment in the construction and industrial sectors, in addition to the fact that sectoral employment has no effect on the real economic confidence index, can be linked to the fact that sectoral leaders pursue a policy of contraction or expansion within the framework of expectations

Are the leading indicators really leading? Evidence from mixed-frequency spillover approach

Leading indicators, usually measured at low frequency (e.g., monthly or quarterly), are widely used as pioneering indices to foresee the future perspective of macro economy and financial markets. However, it is not clear whether these low-frequency leading indicators really lead the actual performance of financial markets, which are usually measured at high frequency (e.g., daily or weekly). This paper uses a novel mixed-frequency spillover method of to examine the spillover effects between monthly leading indicators and weekly financial market prices in China, aiming to assess the pioneering function of these leading indicators. The empirical results suggest that, first, some monthly leading indicators can lead the monthly performance of financial markets, but all of them fail to lead the weekly performance of financial markets. Second, the mixed-frequency approach yields much higher spillover measures than the common-frequency method of Diebold and Yilmaz (2012). Third, China's stock and commodity markets are spillover transmitters to other financial markets in both common and mixed frequency conditions. Finally, the China Monetary Index (CMI) and the Manufacturing Purchasing Managers' Index (CPMI) are more effective leading indicators than the China Consumer Confidence Index (CCCI). These findings help investors and policymakers to adjust risk management strategies in a timely manner and facilitate more accurate financial forecasting.

Comparison of Empirical Methods to Estimated Reference Evapotranspiration

Evapotranspiration plays an important role in agricultural water management and crop modelling. Estimating reference Evapotranspiration (ETo) using meteorological variables, both theoretical and empirical methods, is highly recommended considering the availability of weather data in several locations. The estimation method recommended as the standard method is FAO Penman Monteith (FAOPM), but due to the limited meteorological data in a region and the difficulty and complexity of FAOPM, it is recommended to use the empirical method which is easier and only requires a few simple meteorological variables. The aim of this research is to compare and evaluated empirical methods for estimating ETo against the FAOPM. The statistical analysis using in this research are RSME, MAE, coefficient Correlation, NSE, Average bias, index of agreement, and confidence index (c). Evaluation for the best models based on statistic analyzed shows that several empirical methods show terrible performance in estimating the monthly average ETo (mm/day), which are Thornthwaite-Mather, Hargraves-Samani, Makkink, Hamon, Romaneko, and Kharauffa. Modified Blaney-Criddle method showed a good performance method, while PMAWS showed very good performance The Turc and Hansen method showed excellent performance with RMSE, MAE, NSE, and C values ​​for the Turc method, are 0.12, 0.11, 0.78, 0.92 respectively, and for the Hansen method are 0.12, 0.1, 0.8, and 0.89 respectively.

Understanding the post-covid Chinese economy

ABSTRACT The underperformance of the post-COVID Chinese economy in 2023 has attracted worldwide attention. By examining various indicators, this study confirms the conclusions observed by other researchers. This study offers a declining confidence approach to explain this underperformance. Based on the Baidu Index during January 2015 – January 2024, this study creatively constructs a Chinese confidence index and places the post-COVID Chinese economy into context. Trend data indicate that the Chinese confidence level was rising during 2015–2017 but began to decline persistently since 2018. This study then reviews a series of China’s foreign and domestic policies during this period, including the China-US trade war, supply-side structural reform, policies towards privately-owned enterprises, zero COVID policy, and national security policy, and concludes that they may have driven the rise and fall of Chinese confidence levels.

Survival Benefit of Lenvatinib Plus PD-1 Inhibitor with or Without HAIC in Advanced Hepatocellular Carcinoma Beyond Oligometastasis: a Multicenter Cohort Study.

The outcome between Lenvatinib plus programmed cell death protein-1 (PD-1) inhibitor and Lenvatinib in HCC beyond oligometastasis was unclear. In this multicenter, we compared the prognosis of Lenvatinib plus PD-1 inhibitor with Lenvatinib in HCC beyond oligometastasis. A total of 296 patients from six institutions were included. The patients were divided into two groups: (a) concurrent Lenvatinib plus PD-1 inhibitor treatment (Len+PD-1 group) and (b) Lenvatinib monotherapy (Len group). The primary endpoint was overall survival (OS), the second endpoint was progression-free survival (PFS) and efficacy. The median OS was 20.1 ± 1.2 (17.7-22.5) months and 15.7 ± 1.5 (12.8-18.6) months in the Len+PD-1 and Len groups, respectively. The 12-, 24-, and 36-month OS rates were 79.1%, 39.4%, and 10.7% in the Len+PD-1 group, and 76.3%, 29.7%, and 0% in the Len group, respectively. The OS and PFS rates of the Len+PD-1 group were significantly longer compared with the Len group (hazard ratio [HR], 0.88; 95% confidence index [CI], 0.49-0.94; P = 0.021) and (HR, 0.66; 95% CI, 0.50-0.87; P = 0.003). A subgroup analysis revealed that OS (HR, 0.57; 95% CI, 0.36-0.90; P = 0.016) was improved between the Len+PD-1 and Len groups with hepatic artery infusion chemotherapy (HAIC) treatment, whereas OS (HR, 1.11; 95% CI, 0.68-1.80; P = 0.689) was similar between the Len and Len+PD-1 groups without HAIC. Lenvatinib combined with PD-1 inhibitor significantly improves the survival of HCC beyond oligometastasis. For patients with HAIC, there was obviously significance between Len and Len+PD-1 groups.

A pioneering approach to measure increased resilience to face climate change: insights from the Race to Resilience campaign

This paper illustrates a methodology to measure the impact of resilience-building actions on the increased resilience of people and natural systems to face climate change, developed and field-tested around the Race to Resilience Campaign. Despite increasing acknowledgment of the need for robust methodologies and indicators to monitor and evaluate efforts across adaptation planning and implementation, and provide credibility, accountability and transparency to such actions, there is still a lack of sufficiently standardized and agreed upon metrics able to capture the effect of resilience-building actions. The proposal illustrated in this manuscript offers a pioneering approach for high-level tracking, monitoring and evaluation of resilience-building efforts of non-state actors, based on two complementing sets of metrics: depth metrics measure the degree to which an action is generating a change to fundamental conditions which can demonstrably be related to increasing resilience; while magnitude metrics offer a quantification of the beneficiaries that are affected by these changes. Underlying both stand the Resilience Attributes: properties which can be soundly associated with triggering resilience across different systems, and which can then be used to assess increased resilience ‘by proxy’: that is, by seeing how an action sets forth changes in properties commonly associated with resilience. These Attributes were identified based on updated scientific literature and co-construction exercises with global experts. The integration of Depth and Magnitude indices, adjusted by a Confidence Index evaluating data reliability, allows to estimate the overall contribution of a set of actions on increasing resilience against climate challenges. Based on the above, a possible Monitoring & Evaluation cycle is proposed, and an illustration is offered on two case studies from the Race to Resilience campaign. Key strengths, lessons learned and insights are summarized to stimulate the global discussion, in the context of the Global Stocktake and Global Goal on Adaptation.

Performance comparison of alternative volatility models applied to economic indexes: the role of asymmetric effects

ABSTRACT This article examines the empirical relationship between economic indexes – namely the industrial production and the consumer confidence – and their volatility by using the United States data over the periods 1919–2023 and 1960–2023, respectively. To address this topic, several GARCH-family models including volatility feedback and the asymmetric effects are estimated. We find that (i) the industrial production index exhibits asymmetric effects, with significant fitting performance over the standard GARCH model; (ii) the inclusion of t -distributed innovations in the error term improves the fitting performance of the competing models against their counterparts with a normal distribution assumption; (iii) both models tested for the consumer confidence index exhibit similar performance to fit with the data; (iv) by excluding the months during and after the COVID-19 pandemics and the Russia–Ukraine conflict, it leads to the same results and conclusions for both economic indexes. Conditional heteroscedasticity, economic growth, GARCH models, asymmetric effects, volatility feedback

Consumer confidence and cryptocurrency excess returns: A three-factor model

This study examined the relation between consumer confidence and cryptocurrency excess returns using a three-factor model of market, size and momentum. We analysed a dataset comprising 3318 cryptocurrencies from 1 January 2014 to 31 December 2022 based on the CoinMarketCap website. Results indicate a significant negative relation between the United States Consumer Confidence Index and cryptocurrency excess returns. The findings were reinforced based on robustness tests. This study contributes to consumer behaviour research and financial management within the cryptocurrency market. It also provides valuable insights for investors to strengthen their investment portfolios and for relevant authorities seeking to formulate effective policies for monitoring the cryptocurrency market.

New robust multi-criteria decision-making framework for thermal insulation of buildings under conflicting stakeholder interests

The choice of thermal insulation technology for existing building retrofit can be a complex multi-criteria decision-making (MCDM) problem characterized by multiple stakeholders with conflicting interests. The conflicting interests of the building owners and the policymakers could negatively influence the adoption of low-carbon energy retrofit measures by building possessors, slowing down the progress toward sustainable development. The investigation of this issue is, therefore, crucial to support more informed decisions and offer policymakers useful insights to develop future economic incentives for energy refurbishment of buildings. Most of the existing MCDM studies on the subject do not emphasize the differences among different stakeholders nor consider the outcomes' robustness. To overcome this gap, this paper integrates multi-stakeholder analyses and robustness assessments to provide broad-spectrum and reliable results. An innovative robust MCDM framework for building thermal insulation under conflicting stakeholder interests is proposed and tested on a real case study building (located in Southern Italy) using building dynamic energy simulation. Several alternatives of thermal insulation are evaluated under environmental, energy, and economic key performance indicators (KPIs). The Analytic Hierarchy Process is used to define criteria weights for conflicting decision-makers representing collectivity (policymakers) and private interests. TOPSIS, VIKOR, WASPAS, and MULTIMOORA methods are integrated to perform initial rankings of the alternatives. A rank similarity analysis is performed to evaluate the consensus between MCDM methods, and an ensemble ranking is obtained for each decision-maker using an approach based on the half-quadratic theory. A Confidence Index and a Trust Level are used to evaluate the agreement among the MCDM approaches, verifying the reliability of the final ensemble ranking. The robustness of the framework is attained by complying with well-established literature guidance. The hybrid MCDM analysis showed that porous materials like expanded clay and expanded perlite lead to better results under the KPIs investigated. The worst performances are attained by vacuum insulation panels and aerogel, mainly due to high values of embodied CO2 emissions and long payback periods. Insights upon the performance of different thermal insulating alternatives are also highlighted by the study and a stakeholder comparative analysis demonstrates that global rankings obtained for the different decision-makers show some similarities, but also important deviations, and a full compromise solution is not achieved.

Research on China insurance demand forecasting: Based on mixed frequency data model.

In this paper, we introduce the mixed-frequency data model (MIDAS) to China's insurance demand forecasting. We select the monthly indicators Consumer Confidence Index (CCI), China Economic Policy Uncertainty Index (EPU), Consumer Price Index (PPI), and quarterly indicator Depth of Insurance (TID) to construct a Mixed Data Sampling (MIDAS) regression model, which is used to study the impact and forecasting effect of CCI, EPU, and PPI on China's insurance demand. To ensure forecasting accuracy, we investigate the forecasting effects of the MIDAS models with different weighting functions, forecasting windows, and a combination of forecasting methods, and use the selected optimal MIDAS models to forecast the short-term insurance demand in China. The experimental results show that the MIDAS model has good forecasting performance, especially in short-term forecasting. Rolling window and recursive identification prediction can improve the prediction accuracy, and the combination prediction makes the results more robust. Consumer confidence is the main factor influencing the demand for insurance during the COVID-19 period, and the demand for insurance is most sensitive to changes in consumer confidence. Shortly, China's insurance demand is expected to return to the pre-COVID-19 level by 2023Q2, showing positive development. The findings of the study provide new ideas for China's insurance policymaking.

Design of Consumer Confidence Prediction Index Model based on DEGWO Algorithm

Based on the CCI index released by China Economic Information Network, combined with the DEGWO difference algorithm and BP neural network regression; Constructed a DEGWO-BP synthesis algorithm under machine learning mode to predict and fit consumer confidence index; The empirical results show that the cumulative error of the consumer confidence index using the DEGWO-BP algorithm is the lowest, only 37.8273; The average absolute error of the model is the lowest, and the model has the minimum level of deviation; The model with the minimum extreme deviation value has the strongest stability.