Search

Projections of commodity prices have long been a significant source of dependence for investors and the government. This study investigates the challenging topic of forecasting the daily regional steel price index in the northeast Chinese market from January 1, 2010, to April 15, 2021. The projection of this significant commodity price indication has not received enough attention in the literature. The forecasting model that is used is Gaussian process regressions, which are trained using a mix of cross-validation and Bayesian optimizations. The models that were built precisely predicted the price indices between January 8, 2019, and April 15, 2021, with an out-of-sample relative root mean square error of 0.5432%. Investors and government officials can use the established models to study pricing and make judgments. Forecasting results can help create comparable commodity price indices when reference data on the price trends suggested by these models are used. [ABSTRACT FROM AUTHOR]

Enhanced recovery after surgery (ERAS) pathways utilize multimodal analgesia. In pathways already utilizing incisional injection of liposomal bupivacaine (ILB), we assessed the benefit of adding intrathecal opioid analgesia (ITA). In this randomized controlled non-inferiority trial in patients undergoing laparotomy for gynecologic malignancy, we allocated patients 1:1 to ILB alone versus ITA + ILB with 150 μg intrathecal hydromorphone. The primary endpoint was the Overall Benefit of Analgesia Score (OBAS) at 24 h following surgery. Secondary endpoints included pain scores, intravenous opioid use, and cost of care. Demographic and surgical factors were balanced for 105 patients. For the primary endpoint, ILB alone was non-inferior to ITA + ILB (median OBAS at 24 h of 4 vs 4; p = 0.70). We observed a significant reduction in the need for intravenous opioids (26% vs 71%; p < 0.001) and total opioid requirements (median 7.5 vs 39.3 mg morphine equivalents, p < 0.001) in the first 24 h. Clinically relevant improvements in pain scores were identified in the first 16 h after surgery favoring ITA + ILB. Total cost of the index episode, pharmacy costs, and costs at 30 days were not statistically different. Using OBAS as the primary endpoint, ILB alone was non-inferior to ITA + ILB. However, important cost-neutral benefits for ITA + ILB in the first 24 h post-operatively included lower pain scores and reduced need for intravenous opioids. These early, incremental benefits of adding ITA to ERAS bundles already utilizing ILB should be considered to optimize immediate post-operative pain. • The addition of intrathecal analgesia was found to be non-inferior to incisional liposomal bupivacaine (ILB) alone. • Adding intrathecal analgesia to ILB resulted in lower pain scores on the day of surgery and less breakthrough opioid use. • The addition of intrathecal analgesia was safe and cost neutral compared to incisional liposomal bupivacaine alone. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions For men with prostate cancer, there is substantial variation in the use of conservative management, such as active surveillance. Commercial prices, which vary across urology practices, may afford incentives that foster physician behaviors associated with utilization. Such behaviors may “spillover” to the Medicare population and affect quality. This study evaluated the effects of practice‐level commercial prices on health care utilization and quality in men with prostate cancer insured by traditional Medicare.From a 20% Medicare sample, the authors identified men with newly diagnosed prostate cancer between 2014–2019 (n = 44,653). Using commercial payments from the MarketScan database, they developed a practice‐level commercial price index (ratio of commercial prices to Medicare prices). They examined the association of the price index with price standardized spending, overtreatment (treatment among those with >50% noncancer mortality within 10 years), and underuse of diagnostic testing in active surveillance (at least one prostate‐specific antigen test and one confirmatory test—MRI, prostate biopsy, genomic test—within 12 months of diagnosis).Practice‐level commercial price indices varied from 1.34 (134% of Medicare prices), for practices in the bottom decile, to 3.00, for practices in the top decile. Increasing price index was associated with lower odds of overtreatment (odds ratio, 0.86; 95% confidence interval, 0.76–0.97; p = .01), but not price standardized spending or underuse of diagnostic testing in active surveillance.Commercial prices vary markedly across urology practices. Among newly diagnosed men with traditional Medicare, those managed by practices with higher commercial price indices had lower odds of overtreatment, suggesting improved prostate cancer care quality. [ABSTRACT FROM AUTHOR]

This study aims to investigate a financial system consisting of four ordinary differential equations associated with the rate of interest, investment demand, price index, and the density of financial information gained by the population. The equilibrium and local stability of the system are investigated numerically. The impact of saving amounts and the rate of investment demand increases after getting financial information on the system are discussed. The findings of the study are verified graphically. It is found that the system becomes stable if the rate of investment demand increases after getting financial information kept at a certain level, such that the savings amount is maintained at a higher level. Also, the bifurcation diagrams of the system for various significant parameters that affect the system's stability have been depicted. [ABSTRACT FROM AUTHOR]

Purpose: This study attempts to comprehensively analyze the cost Malmquist productivity index of conventional and Islamic banks in Saudi Arabia, the largest dual banking sector in the world, during the COVID-19 pandemic. Design/methodology/approach: This study employs the novel approach of cost Malmquist productivity index, which focuses on production costs, to measure the change in cost productivity so that the actual impact of the COVID-19 pandemic could be captured. Findings: The Saudi Central Bank has successfully mitigated the impact of the COVID-19 epidemic on the Saudi banking sector by implementing several policies and services. This success is reflected in the large positive shift in the production frontier of Saudi banks. Moreover, it was found that Islamic Saudi banks were by far more productive than conventional Saudi banks during the COVID-19 pandemic. However, the total cost productivity index (CMPCH) of Islamic Saudi banks starts to decline sharply in the last quarter of 2022 compared to conventional Saudi banks, indicating that Islamic banks in Saudi Arabia are suffering the most from the tighter monetary policy recently implemented by the Saudi Central Bank. Practical implications: The results provide insights for policymakers and investors on how different types of banks respond differently to economic crises and monetary policy changes. Targeted support measures may be needed to ensure all banks remain productive and efficient. Originality/value: To the author's knowledge, this is the first study to use this innovative methodology to assess the impact of COVID-19 on bank performance in a dual banking sector. [ABSTRACT FROM AUTHOR]

We trace the long‐term performance of the UK art market across a broad set of crises: world wars, economic recessions, financial crises, inflationary periods, and changes in monetary policy. By means of digitalized historical auction archives, we construct art price indices from the early twentieth century onwards and disclose that annual art auction value grew, in real terms, more than seven‐fold over this period. The arithmetic annual real return and risk amount to 3.6 per cent and 20.1 per cent, respectively. Art returns plummeted at the onset of wars, but turned positive in the second half of wars when they outperformed stocks, suggesting that art was seen as a safe haven in times of political turmoil. During wars, smaller – and thus more transportable – paintings obtained higher returns. Art returns are sensitive to economic and financial crises, with the largest slumps occurring during the Great Depression, oil crisis, recessions of the early 1980s and early 1990s, and the Great Recession. We also document changes in art preferences for paintings' sizes, schools, liquid art, and artists' nationalities across crises. Art enters a broad optimal asset portfolio both in non‐crisis periods and during war times. [ABSTRACT FROM AUTHOR]

In this study, we use monthly G7 industrial production data, commodity price index data, and commodity currency exchange rate data in a dynamic factor model to examine the global economic factors useful for commodity price prediction. We differentiate between the dynamic factors by specifying a persistent factor and a non‐persistent factor, both as a single global factor using all data and as factors for each category of data. The in‐sample predictive performances of the three persistent factors together are better than the non‐persistent factors and the single global factors. Out‐of‐sample outcomes based on forecast combinations also support the presence of predictive information in the persistent factors for overall commodity prices and for most sub‐categories of commodity price indexes relative to their means. The gains in forecast accuracy are heterogeneous, ranging from 5% to 7% in the 1‐ to 6‐month horizon for overall commodity prices to a high of around 20% for fertilizers in the 12‐month horizon in the recent sample. We further show that the information in the persistent factors, especially in the commodity currency exchange rate‐based persistent factor, can be integrated with other global measures to further improve the predictive performances of the global measures. [ABSTRACT FROM AUTHOR]

Copyright of Taiwan Economic Forecast & Policy is the property of Institute of Economics, Academia Sinica, Taiwan Economic Forecast & Policy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study investigates the regional demand for shrimp in Norway by employing the linear approximated Almost Ideal Demand System (LA/AIDS) framework. Shrimp consumption has grown significantly globally, with Norway being no exception. The LA/AIDS model allows us to examine the demand of shrimp across different regions in Norway. We utilize comprehensive data on regional shrimp consumption, prices, and household expenditures to estimate and analyze demand patterns. Moreover, we can distinguish between farmed (freshwater) and wild (coldwater) shrimp. Our findings reveal consistent patterns across regions, with fresh coldwater shrimp emerging as a dominant product form, often substituting for other shrimp species. Furthermore, regional variations in substitution relationships highlight the nuanced nature of the markets, with differences in the intensity of competition and the extent of substitution effects. We also provide the theoretical relationship between regional demand and the aggregate demand at the national level. [ABSTRACT FROM AUTHOR]

Ensuring the secure and cost-effective operation of smart power microgrids has become a significant concern for managers and operators due to the escalating damage caused by natural phenomena and cyber-attacks. This paper presents a novel framework focused on the dynamic reconfiguration of multi-microgrids to enhance system's security index, including stability, reliability, and operation costs. The framework incorporates distributed generation (DG) to address cyber-attacks that can lead to line outages or generation failures within the network. Additionally, this work considers the uncertainties and accessibility factors of power networks through a modified point prediction method, which was previously overlooked. To achieve the secure and cost-effective operation of smart power multi-microgrids, an optimization framework is developed as a multi-objective problem, where the states of switches and DG serve as independent parameters, while the dependent parameters consist of the operation cost and techno-security indexes. The multi-objective problem employs deep learning (DL) techniques, specifically based on long short-term memory (LSTM) and prediction intervals, to effectively detect false data injection attacks (FDIAs) on advanced metering infrastructures (AMIs). By incorporating a modified point prediction method, LSTM-based deep learning, and consideration of technical indexes and FDIA cyber-attacks, this framework aims to advance the security and reliability of smart power multi-microgrids. The effectiveness of this method was validated on a network of 118 buses. The results of the proposed approach demonstrate remarkable improvements over PSO, MOGA, ICA, and HHO algorithms in both technical and economic indicators. [ABSTRACT FROM AUTHOR]

In this paper, we explore the connection between a single index model under the real-world probability measure and martingale pricing via minimal relative entropy or Esscher transform, within the context of a one-period market model, possibly incomplete, with multiple risky assets and a single risk-free asset. The minimal relative entropy martingale measure and the Esscher martingale measure coincide in such a market, provided they both exist. From their Radon–Nikodym derivative, we derive a portfolio of risky assets in a natural way, termed portfolio G. Our analysis shows that pricing using the Esscher or minimal relative entropy martingale measure is equivalent to a single index model (SIM) incorporating portfolio G. In the special case of elliptical returns, portfolio G coincides with the classical tangency portfolio. Furthermore, in the case of jointly normal returns, Esscher or minimal relative entropy martingale measure pricing is equivalent to CAPM pricing. [ABSTRACT FROM AUTHOR]

A substitution of the traditional data sources allows National Statistical Institutes to collect large samples of product items with corresponding prices recorded over more frequent time periods. For some product categories, a frequent replacement of product items requires a dynamic approach to define a product sample that can be achieved by a monthly update of the sample. In a month-on-month comparison of price changes, bilateral indices must be chained that can lead to a chain drift bias. Multilateral methods are designed to reduce the chain drift, albeit not completely eliminate it. A disadvantage of multilateral methods is its ambiguous interpretability and a revision problem. The main objective of this paper is to show an implementation of multilateral methods for official price statistics in practice. The multilateral GEKS method is decomposed into elementary components that identifies the effect of successive price relatives used in its compilation. An importance of the decomposition is shown when interpreting the GEKS index in the context of official price statistics. We demonstrate that it can lead to an inaccurate public dissemination of the consumer price index. Moreover, we propose a new method for the splice extension that is required to avoid the revision of previously published indices. The method uses a Stein-type shrinkage estimator for the splice extension that is applied additively to the last published index. In the empirical study, we use web scraped data, which are obtained from the Slovak market, for comparison of bilateral and multilateral indices. [ABSTRACT FROM AUTHOR]

The Construction Cost Index (CCI) is an important tool that is widely used in construction cost management to monitor cost fluctuations over time. Numerous studies have been conducted on CCI development and forecasting models, including time series, artificial intelligence, machine learning, and hybrid models. Therefore, this study seeks to reveal the complexity of CCI forecasting and identify the leading indicators, trends, and techniques for CCI prediction. A bibliometric analysis was conducted to explore the landscape in the CCI literature, focusing on co-occurrence, co-authorship, and citation analysis. These analyses revealed the frequent keywords, the most cited authors and documents, and the most productive countries. The research topics and clusters in the CCI forecasting process were presented, and directions for future research were suggested to enhance the prediction models. A case study was conducted to demonstrate the practical application of a forecasting model to validate its prediction reliability. Furthermore, this study emphasizes the need to integrate advanced technologies and sustainable practices into future CCI forecasting models. The findings are useful in enhancing the knowledge of CCI prediction techniques and serve as a base for future research in construction cost estimation. [ABSTRACT FROM AUTHOR]

Copyright of Gazi Journal of Economics & Business is the property of Gazi Journal of Economics & Business and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The present study investigates the price behaviour, structural breaks, price cointegration and price transmission in major onion markets of India in light of the COVID-19 pandemic. The findings of this study revealed that seasonal price indices were highest in November for southern region markets. A major structural break in the selected markets was noted in August or September 2019, showing that the structural break did not align with the COVID-19 pandemic lockdown period as anticipated. Further, the study found 11 cointegration equations at 1% significance level by trace test, indicating long-term cointegration in onion markets despite their geographical segmentation. Some markets exhibited bi-directional or uni-directional influences of prices on other selected markets, with Delhi showing the highest number of bi-directional influences. Additionally, the impulse response function revealed that the Delhi price shock had a positive effect up to seven periods, which later became a follower. [ABSTRACT FROM AUTHOR]

Factors such as supply chain difficulties, rising energy and oil prices, economic recession and production loss due to the pandemic have increased costs and inflation. All these factors have also seriously affected the construction sector. This study aims to create a deep learning and machine learning focused forecasting system based on Istanbul and Ankara monthly housing price index data for the period of January 2010 to June 2023. The system was created using approximately 13 years of housing interest rates, Consumer Price Index, XGMYO, Monthly Average Dollar and XAU data as the basis of the Istanbul and Ankara Housing Price Index forecasting process. During the research process, different RNN structures (Long and Short Term Memory, Gated Recurrent Unit) and machine learning (Random Forest) structures were tested and the effectiveness of these structures in housing price index forecasting was compared. The performances of the models were evaluated using RMSE, MSE, MAE, MAPE and R2 statistics. According to the results obtained, the method that gave the best performance for both provinces is the RF model. This is followed by LSTM and GRU models, respectively. [ABSTRACT FROM AUTHOR]

Construction project management prioritizes the achievement of three main objectives: cost, quality, and time. This study evaluates a road widening project in Mojokerto Regency with a target completion time of 150 days. To prevent delays and cost overruns, researchers used the Earned Value method to identify early warnings regarding project performance. This method integrates time and cost values through measurements of Cost Variance (CV), Schedule Variance (SV), Cost Performance Index (CPI), Schedule Performance Index (SPI), Estimated at Completion (EAC), and Estimated Schedule at Completion (EAC). The research findings show that additional costs required (ETC) reached Rp 3,276,704,110.14, with additional time (ETS) up to week 12 being 87 days. The change in project costs (EAC) reached Rp 3,961,767,811.01 from the contract cost, while the project completion time (EAS) was 171 days. The analysis indicates that the construction service provider has implemented an efficient implementation method with consistent supervision, ensuring the smooth progress of the project. [ABSTRACT FROM AUTHOR]

Constructing price indices for commercial real estate (CPPIs) is challenging due to heterogeneous and limited observations. Common price index methods often result in volatile index series. Attempts to reduce volatility often lead to frequent revisions of the entire index series and a loss of methodological index properties. When it comes to CPPIs in official statistics, both volatility and frequent revisions are undesirable. Revisions could compromise the confidence of users if indicators are allowed to change indefinitely, while instable indices insufficiently reflect structural underlying developments. In this study, a combination of hedonic imputation, multilateral calculations, time series analysis, and window splicing is introduced. The result is a method that produces stable and limited-revisable indices with the ability to detect turning points in an early stage. Commercial real estate transactions in the Netherlands are used to empirically test the method. The resulting CPPIs appear suitable for monitoring financial stability and, therefore, seem appropriate for the use in official statistics. [ABSTRACT FROM AUTHOR]

Laboratory automation with robot-assisted processes enhances synthetic biology, but its economic impact on projects is uncertain. We have proposed an experiment price index (EPI) for a quantitative comparison of factors in time, cost, and sample numbers, helping measure the efficiency of laboratory automation in synthetic biology and biomolecular engineering. [ABSTRACT FROM AUTHOR]

Off-site steel construction (OSC) is an emerging alternative to traditional on-site methods that offers advantages such as reduced waste, improved quality, and faster delivery. However, OSC also requires robust monitoring and control mechanisms within fabrication shops to ensure the timely and cost-effective completion of projects. This study presents the development and application of a machine learning (ML) model to estimate the cost performance index (CPI), a pivotal indicator of project progress and performance, by considering influential factors that affect OSC projects. A comprehensive analysis of data from 56 OSC projects fabricated in a steel parts manufacturing facility between 2020 and 2022 was conducted. The investigation focused on four key features: project weight, project utilization type, project connection type, and material supply method, examining their impact on CPI. The data set was meticulously preprocessed, and three ML algorithms—support vector machine (SVM), gradient boosting (GB), and decision tree (DT)—were employed to model CPI. Model performance was evaluated and compared using metrics including root mean square error, accuracy, and R-squared. The findings demonstrated that GB excelled in CPI prediction, achieving an accuracy rate of 91%. This research underscores the utility of ML as a valuable tool for monitoring and controlling off-site steel construction projects. It also provides insights into the factors that influence CPI and suggests ways to optimize them for better project outcomes. Furthermore, the study contributes to the literature on OSC by exploring the relationship between project characteristics and performance indicators, which can help practitioners improve their decision-making and planning processes. The study also discusses the limitations and challenges of applying ML models to OSC data, such as data availability, quality, and consistency. [ABSTRACT FROM AUTHOR]

In this study, we apply a novel approach of portfolio diversification—the robust maximum diversified (RMD)—to a small and developing economy's stock market. Using monthly returns data from August 2019 to May 2024 of 18/19 stocks listed on Fiji's South Pacific Stock Exchange (SPX), we construct the RMD portfolio and simulate with additional constraints. To implement the RMD portfolio, we replace the covariance matrix with a matrix comprising unexplained variations. The RMD procedure diversifies weights, and not risks, hence we need to run a pairwise regression between two assets (stocks) and extract the R-square to create a P-matrix. We compute each asset's beta using the market-weighted price index, and the CAPM to calculate market-adjusted returns. Next, together with other benchmark portfolios (1/N, minimum variance, market portfolio, semi-variance, maximum skewness, and the most diversified portfolio), we examine the expected returns against the risk-free (RF) rate. From the simulations, in terms of expected return, we note that eight portfolios perform up to the RF rate. Specifically, for returns between 4 and 5%, we find that max. RMD with positive Sharpe and Sortino (as constraints) and the most diversified portfolio offer comparable returns, although the latter has slightly lower standard deviation and downside volatility and contains 94% of all the stocks. Portfolios with returns between 5% and the RF rate are the minimum-variance, the semi-variance, and the max. RMD with positive Sharpe; the latter coincides with the RF rate and contains the most (94%) stocks compared to the other two. An investor with a diversification objective, some risk tolerance and return preference up to the RF rate can consider the max. RMD with positive Sharpe. However, depending on the level of risk-averseness, the minimum-variance or the semi-variance portfolio can be considered, with the latter having lower downside volatility. Two portfolios offer returns above the RF rate—the market portfolio (max. Sharpe) and the maximum Sortino. Although the latter has the highest return, this portfolio is the least diversified and has the largest standard deviation and downside volatility. To achieve diversification and returns above the RF rate, the market portfolio should be considered. [ABSTRACT FROM AUTHOR]

Although modern price index theory is based on comparisons of ratios of prices, quantities and expenditures, we may be more interested in the magnitude of differences in these characteristics in many business applications. The benefit of using these differences is that there is no problem associated with the occurrence of zero prices and quantities, a problem that arises when we work with ratios. In practice, we most often care about decomposing value difference into indicators of contributions from price and quantity differences. The best-known price and quantity indicators are the Bennet indicators, which are not transitive. Although there have been papers in the literature that propose a transitive version of the Bennet indicators, they deal with comparisons across firms in cross-section or panel contexts. This paper revises the price and quantity Bennet indicators and their multilateral versions for the analysis of scanner data. Specifically, instead of considering comparisons across firms, countries or regions, the transitive versions of the Bennet indicators are adapted to work on scanner data sets observed over a fixed time w indow. Since the scanner data sets have a high turnover of products, which can make it difficult to interpret the difference in sales values over the compared time periods, the paper also considers a matched sample approach. One of the objectives of the study is to compare bilateral and multilateral Bennet indicator results across all available products or strictly matched products over time. It also examines the impact of data filters used and the level of data aggregation on the price and quantity Bennet indicators. According to the best author's knowledge, this study is a pioneer in the field of implementing the multilateral Bennet indicators in scanner data analysis. [ABSTRACT FROM AUTHOR]

In order to prepare proper budgets, practitioners endeavor to forecast future cost fluctuations. In recent years, the COVID-19 pandemic and the Russia-Ukraine war have caused cost overruns in most construction projects, and there has been no effort to analyze the effects of such situations on cost forecasting performances. This paper aims to investigate the impact of unexpected conditions, such as pandemics and wars, on the construction cost forecasting performances of widely used univariate time series models. For this purpose, statistical forecasting models and two widely used machine learning techniques are employed for the construction cost forecasting of nine European economies in two scenarios: a stable period (2017–2020) and a fluctuating period (2020–2023). The results showed that the success of the models varied by region and period. Unexpected global conditions cause significant errors, especially in the medium and long term. There are significant increases in forecast errors between stable and fluctuating periods. This study provides comprehensive experimental results to determine the cost-forecasting risks of construction projects under unexpected global conditions. The investigation is limited to the European economies, which are mostly developed countries with low inflation rates. Therefore, the results should also be validated in underdeveloped and developing countries. [ABSTRACT FROM AUTHOR]

This study aims to analyse housing prices in Taiwan in the post-epidemic era, identify the crucial factors influencing them, and develop a suitable method for analysing and forecasting them. This study collects relevant data such as Taiwan's housing price index data from 2002 to 2020 to identify the crucial factors affecting Taiwan's housing prices; this is achieved by constructing a regression model, forecasting Taiwan's housing prices through a constructed long short-term memory (LSTM) model that employs big data analytics, and verifying the efficiency of the proposed models through R-square and root mean square error values. The results indicate that the top 10 factors affecting Taiwan's housing prices are mostly related to mortgage interest rates, suggesting that in Taiwan, the effect on housing prices in the post-epidemic era may be non-significant. This study collects data on Taiwan's housing price for the period from the first quarter of 2002 to the fourth quarter of 2020 to construct an LSTM for forecasting Taiwan's housing prices. The results indicate that the proposed LSTM exhibits good fitness, indicating that the model is suitable for analysing and forecasting housing prices. Given that analysing and forecasting quantity is also crucial in housing market analyses and that this study focuses only on predicting housing prices, future research should explore the simultaneous prediction and analysis of both price and quantity. [ABSTRACT FROM AUTHOR]

A useful tool for non-linear multivariable modelling is the Artificial Neural Network (ANN). Cost-effectiveness has been demonstrated for the use of ANN. Typically, the neural network is trained using the back propagation (BP) algorithm. Even while this technique is particularly successful at training a wide variety of networks, it suffers from slow convergence and easy trapping in local minima. Regression tree models (M5P) were used to suggest the adjustment of neural networks utilizing particle swarm optimization (PSO) as a technique for predicting building costs due to the potential of multiscale modelling and prediction using the incorporated relevant parameters affecting the building cost. The three models were assessed using a variety of metrics, including Mean Square Error (MSE), absolute error, and Pearson's correlation as an accuracy criterion. The MSE was also employed as a parameter to determine the ideal number of neurons for the hidden layer. The results indicate that the PSO model outperforms the other two techniques. The study also concluded that the PSO model can accurately predict the costs of construction projects when considering specified project activities and price indices. A combination of project features and an automated modelling mechanism has the potential to provide a reliable prediction result. [ABSTRACT FROM AUTHOR]

In this paper, we use quarterly stock price index data to test the weak form of the efficient market hypothesis for 13 art market indices. The fact that hypotheses can be efficiently tested by performing unit root tests is well known. The conventional unit root test results show that most art indices have unit roots, which means that these art markets are efficient. However, the results of the non-linear quantile unit root tests we conduct over a range of quantiles lead us to reject the null of the unit root test for several art markets. To acquire more information, we perform the test over sub-quantile intervals, finding asymmetric market efficiency between the low and high quantile intervals: we cannot reject the unit root null in upper quantile intervals for most art price indices. [ABSTRACT FROM AUTHOR]

Estimating economic poverty indicators at the local level is essential for well-targeted data-driven welfare policies. However, Italy is a country characterized by strong geographical heterogeneity represented by unequal price levels among different areas, and computing poverty indicators with a national monetary poverty threshold can be misleading. This work proposes a novel approach to estimate monetary poverty incidence at the provincial level in Italy considering the different price levels within national boundaries. To account for local price variation, Spatial Price Indices (SPIs) are computed using scanner data on retail prices. The SPIs are estimated in two ways, referring to the mean local prices and using the 20th percentile of the prices. These two kinds of SPIs are used to adjust the national poverty line when computing the poverty incidence at the provincial level using Small Area Estimation (SAE) models. Our findings suggest that adjusting the national poverty line using the SPIs to compute a monetary poverty index can modify the poverty mapping results from the map produced with the traditional national poverty line that ignores price differences. [ABSTRACT FROM AUTHOR]

Using Bayesian Structural Time Series analysis, this study examines the causal impact of loan‐to‐value (LTV) restrictions imposed by the Reserve Bank of New Zealand in October 2013. By incorporating state‐space components, such as local linear trend, seasonality and regression, counterfactual values of house price indices are predicted. Surprisingly, the study reveals that the implementation of LTV restrictions had no significant effect on national house price indices, contradicting prior Central Bank studies that reported a nearly 3 percentage‐point decrease in housing cost inflation. This contradictory evidence challenges existing perceptions of the effectiveness of LTV restrictions in curbing house price inflation. [ABSTRACT FROM AUTHOR]

Copyright of WISTA Wirtschaft und Statistik is the property of Statistisches Bundesamt and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Most users of Producer Price Index (PPI) data know that PPIs measure the average changes over time in the selling prices received by domestic producers of goods and services. What [...]

Background: Price levels of medical services may vary across regions with different income levels, which would raise concerns about the equal access to medical services. This study aimed to estimate the spatial price index of medical services to measure price levels across 31 provincial regions in China. Methods: Price data were collected from medical service price schedule in each region. Two methods based on the Purchasing Power Parities were used to estimate the spatial price index and measure price differences across regions. The two-way fixed effects models were used to examine the association between medical service price levels and income levels, and further investigate the impacts of price differences on utilization of medical services and medical expenditure. Results: The consistent estimation results were given by two methods. Medical service price level in the highest-price region was found to be 74% higher than the lowest. There was a significant negative correlation between price levels and income levels, as well as price levels and the utilization of outpatient services. Moreover, we also found a 1% increase in medical service price level was significantly associated with a 0.34% and 0.24% increase in the medical service expense per outpatient visit and per inpatient respectively. Conclusions: Regions in China had significant gaps in medical service price levels. Policymakers should pay more attention to regional price differences and take great measures such as enhancing financial protection to ensure the equal access to medical services and better achieve the universal health coverage. [ABSTRACT FROM AUTHOR]

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we select the China Carbon Market Price Index, which reflects the overall price changes in China's carbon market (CCM), and employs the TVP- VAR-BK model to examine the risk spillover effects between the carbon market and high-carbon-emission industries in China from a frequency domain viewpoint. Employing the nonparametric quantile Granger causality test, it delves further into the effects of economic policy uncertainty (EPU) in China on the degree of risk spillovers between the carbon market and high-carbon- emission industries. There are significant risk spillover effects between the carbon market and high-carbon-emission industries. During the short term, the carbon market affects the cement industry more than the electric power and steel industries. However, the carbon market is affected by the volatility of the high-carbon-emission industries over the long term. In addition, the effect of EPU on the magnitude of risk spillovers between the carbon market and high- carbon-emission industries is nonsignificant at extreme quartiles and significant at the middle quartile level, which is typically asymmetric. [ABSTRACT FROM AUTHOR]

This paper presents a new way to estimate the constant elasticity of substitution $ (\sigma ). $ (σ). We show that the link between $ \sigma $ σ and Divisia index numbers gives rise to a revealing way to obtain preliminary estimates of $ \sigma $ σ. This approach, which is related to stochastic index numbers, is illustrated with time-series and cross-country data. Using consumption data for 12 commodities over time from 23 European countries, we obtain 400 + estimates of $ \sigma $ σ, which mostly lie between 0 and 1. The cross-country estimates are broadly similar. Extensions of the approach to deal with endogenous prices and non-homothetic demand are also considered. [ABSTRACT FROM AUTHOR]

Burn injuries pose a significant burden on both patients and healthcare systems. Yet, costs arising from the consumption of resources by these patients are rarely examined in Canada. The objective of this study was to assess real-world costs resulting from the initial hospitalization of patients admitted to a major burn unit in Quebec, Canada. A cost study based on a retrospective cohort was undertaken using in-hospital economic data matched to hospital chart data. Our cohort included all burn-injured patients admitted between April 1, 2017, and March 31, 2021, to the hospital's major burn unit during their initial hospitalization. Descriptive statistics were tabulated for sociodemographic and economic data. Costing data were analyzed unstratified and stratified according to burn severity (i.e., ≥ 20% of total body surface area [TBSA] vs. < 20%). Costs were presented in CAD 2021. Our cohort included 362 patients, including 65 (18%) with TBSA ≥ 20%. The average initial hospitalization cost was $32,360 ($22,783 for < 20% TBSA and $76,121 for ≥ 20% TBSA). Conclusion: Findings reveal that the total cost of the initial hospitalization, from a public hospital perspective, was $11,714,348. Our study underlines the substantial burden associated with burns and highlights the need for long-term cost evaluations. • Burn injuries are resource intensive but their exact cost is unknown in Canada. • Real-world cost of the index hospitalization was estimated at $32,360 (CAD 2021). • Heterogeneity of burn patients contributed to a high variability in costs. • Cost of long-term burn injury care remains unknown. [ABSTRACT FROM AUTHOR]

Determination of the optimal operational policy for an automotive supply chain is explored under a centralized management approach using dynamic programming. A deterministic optimal control model is proposed to meet multi-product demand over a period while minimizing a cost performance index for a five-echelon network. The production-inventory levels are the state variables and the raw material acquisition rates are the control variables to be decided in the problem. The novelties include parts mixing operations, assembly requirements, and a push–pull chain operation strategy. The continuous model is solved using Iterative Dynamic Programming, an algorithm with successful applications in chemical engineering problems. Its implementation here is the first in supply chain (SC) management models. The results demonstrate that the proposal is suitable to represent the dynamic behavior of the SC and provides useful information to outline a cooperative decision-making process. Managerial insights are derived to improve the resilience and efficiency of the chain. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Technology Economics is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Effectively reducing the energy consumed by buildings under the dual carbon targets in China was our focus in this study. We used experimental methods to test and analyze the indoor air and average radiation temperatures in a specific apartment building in Xi'an. We compared the impact of night ventilation on the indoor thermal environment using the EnergyPlus software V9.5.0. The results showed that night ventilation is suitable for the typical summer temperatures in Xi'an when the daily temperature range is larger than 6 °C. Night ventilation technology can be used for 76 days from June to August, accounting for approximately 82.6% of this period. The indoor air and average radiation temperatures both decrease with the adoption of night ventilation, with these temperatures decreasing with an increase in the daily temperature range. When the daily temperature range increases from 3 °C to 15 °C, night ventilation can reduce the indoor average and radiation temperatures by a maximum of 1.07 and 0.47 °C, respectively, on typical meteorological days. When the daily temperature range is 15 °C, the maximum energy savings is 4.85 kWh/d, and the cost saving index for air conditioning operation is 0.065 CNY/(m2·d). With a daily temperature range of 3 °C, the air conditioning operating costs are reduced by 63.7%. Our study provides a reference for building energy conservation and the creation of comfortable indoor thermal environments under the dual carbon target: a carbon peak before 2030 and carbon neutrality before 2060. [ABSTRACT FROM AUTHOR]

We rely on novel textual analysis of real estate listings and identify renovated dwellings in a dataset of Norwegian transactions to estimate the renovation premium in an urban housing market. The renovation premium is estimated in a hedonic framework by classical regression approaches and a random forest algorithm. The strength of the latter is that it allows for a more complex interplay between the renovation premium and explanatory variables. We estimate a significant positive renovation premium of 5–7 percent for renovated dwellings and a negative premium of 9–10 percent for unmaintained/neglected dwellings. These averages mask significant variations in these premiums over time, particularly, a counter-cyclical effect. Omitting renovation information also has implications for estimated short-term house price growth. Unmaintained dwellings tend to transact more in the fourth quarter, indicating that parts of the seasonal price variation reported in the literature are due to compositional variation with respect to renovation. This composition effect bias price movement estimates downward, if uncontrolled for, as unmaintained dwellings transact at significantly lower prices. [ABSTRACT FROM AUTHOR]

Copyright of Agricultural Outlook (1673-3908) is the property of Institute of Agricultural Information, Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)