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We examine the role of peer (e.g., Lipper manager indices) versus pure (e.g., S&P 500) benchmarks in fund manager compensation. We model their impact on manager incentives and then test those predictions using novel data. We find that 71% of managers are compensated based on peer benchmarks. Consistent with the model, peer-benchmarked fund managers exhibit higher effort generating higher gross performance and collect higher fee income. Analyzing advisors' choice between benchmark types, we show that peer-benchmarking advisors cater to more sophisticated and performance-sensitive investors, and are more likely to sell through direct channels, consistent with investor heterogeneity and market segmentation. [ABSTRACT FROM AUTHOR]

• We calibrate a network model and monetary shocks based on empirical data from input-output tables for the Russian economy. • We examine various aspects of the propagation of monetary shocks, such as the dispersion of relative prices and the local peak values of the aggregated price index achieved during the convergence to the new equilibrium. • We argue that monetary shocks with more 'concentrated' money inflows carry relatively higher inflationary risks in the short term. We calibrate a network model and monetary shocks based on empirical data from input-output tables for the Russian economy. We examine various aspects of the propagation of monetary shocks, such as the dispersion of relative prices and the local peak values of the aggregated price index achieved during the convergence to the new equilibrium. We show that these developments depend significantly on the way new money is injected into the economy. [ABSTRACT FROM AUTHOR]

This study examines the influence of global and country factors on inflation movements in Korea using a multi‐level factor model. Our results indicate that country factors showed a more pronounced alignment with inflation in Korea up to the mid‐2000s, having particularly strong impacts around the Asian financial crisis. Beyond the mid‐2000s, global factors played a more influential role in shaping inflation. This increased influence of global factors on inflation became particularly apparent during the notable inflationary surge following the COVID‐19 pandemic. We construct structural models to identify global and country shocks and find that the effect of global shocks persists for a longer period, whereas the immediate impact of country shocks is more pronounced. Global shocks have a greater influence on producer price index and import price index inflation compared to consumer price index (CPI) or core CPI inflation. We further find that energy prices closely correlate with global factors, while exchange rates are highly correlated with country factors in Korea. [ABSTRACT FROM AUTHOR]

Scanner data are electronic transaction data that specify turnover and the number of items sold by barcodes, e.g., the Global Trade Article Number. These data are of particular value and interest to theorists and practitioners who wish to measure the Cost of Living Index or the Consumer Price Index, since their complete content makes it possible to compute any price index formula, including superlative indices or CES (Constant Elasticity Substitution) indices. Since the CES index requires the estimation of the elasticity of substitution, this paper focuses on verifying various methods of estimating this parameter based on scanner data. The paper considers both algebraic methods and methods based on the panel regression approach. The main achievement of the paper is the separation of the main factors that affect the estimated value of the elasticity of substitution, i.e., the type of data filter used and the level of data aggregation. The paper also verifies how the elasticity of substitution estimates affect the differences between the values of the CES indices based on these estimates. [ABSTRACT FROM AUTHOR]

While quality‐adjusted indices tracing the evolutions of regional house prices are increasingly available, this is less the case for data on regional house price levels. Yet, such information is fundamental to assess housing affordability and barriers to labour mobility. This article puts forward a method to compile regional house price levels that are consistent with the evolutions given by house price indices, representative of the underlying stock of dwellings, and based on the information on house price levels that is available at all dates rather than in a single reference period. Results for Spain show that the decline in house prices following the 2008–2009 crisis initially reduced the dispersion in house prices across Spanish regions, but this dispersion has increased again afterwards. Moreover, the relative housing affordability of Madrid deteriorated compared to all other Spanish regions in the last decade. Monitoring whether COVID‐19 will reverse this trend will be key. [ABSTRACT FROM AUTHOR]

This study examines the relationship between changing house prices and assessed value in the Detroit Michigan region. Using parcel-level data from 2008 through 2016, we construct a Hedonic Housing Price Index and an Assessed Value Index for 120 Michigan assessing jurisdictions in the five-county region surrounding Detroit (Macomb, Monroe, Oakland, Washtenaw, and Wayne). Our findings are consistent with the literature, indicating at least a three-year lag between changes in house prices and changes in property assessments. Communities considered to be distressed tended to over-assess properties before the crisis, but these communities recalibrated assessments to better align with market conditions after the financial crisis. Our evaluation also shows that while assessed values decreased more slowly than housing prices fell during the crisis, assessed values tended to fall to even deeper levels than housing prices over a longer time horizon. JEL Codes: H71, H83, G01 [ABSTRACT FROM AUTHOR]

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]

Purpose Methods Results Conclusions Level of Evidence Using time‐driven activity‐based costing (TDABC), we sought to compare the total facility costs, comprising supply and personnel costs during the episode of care for the index procedure, in patients with isolated medial compartmental knee osteoarthritis (OA) undergoing unicompartmental knee arthroplasty (UKA) versus total knee arthroplasty (TKA).We conducted a retrospective analysis of 100 UKAs and 100 TKAs from 2019 to 2022. From a larger sample of 4899 TKAs and 137 UKAs, patients with isolated medial OA (Kellgren–Lawrence Grade <2 in other compartments) were radiographically identified. Demographic data included age, sex and body mass index, with comorbidities controlled using the Charlson comorbidity index. Facility costs were calculated using TDABC and standardized in cost units (CUs), a metric for comparing costs across procedures. Multiple regression analysis was used to assess the independent effect of TKA versus UKA on facility costs.TKA patients had similar operative times (TKA vs. UKA: 1.01×, p = 0.783) and supply costs (1.00×, p = 0.866), where ‘x’ denotes the relative cost ratio. However, TKA was associated with higher personnel costs (260.0 vs. 222.5 CUs [1.17×], p < 0.001) and total facility costs, both including (706.3 vs. 667.5 CUs [1.06×], p = 0.007) and excluding implants (353.8 vs. 312.5 CUs [1.13×], p < 0.001). Adjusted for demographics and comorbidities, TKA had increased facility costs, including (32.2 CUs, 95% confidence interval [CI]: 2.9–61.5, p = 0.031) and excluding implants (37.8 CUs, 95% CI: 15.2–60.3, p = 0.001).TDABC analysis revealed higher total facility costs for the index procedure in TKA, suggesting UKA may be a cost‐saving alternative for isolated medial compartment knee OA.Level III, retrospective comparative study. [ABSTRACT FROM AUTHOR]

Background and objectives: The DOSE index, which incorporates Dyspnea, Obstruction, Smoking, and Exacerbations, is a widely used tool for assessing the severity and prognosis of Chronic Obstructive Pulmonary Disease (COPD). In addition to risk assessment, it has potential clinical utility in predicting healthcare costs, which are primarily driven by exacerbations. While several indices, such as the BODE (Body-mass index, Obstruction, Dyspnea, Exercise) and ADO (Age, Dyspnea, Obstruction) indices, exist for risk prediction, there is a lack of dedicated tools for forecasting healthcare costs. This study explores the potential of the DOSE index compared to other indices, including BODE, ADO, and the Charlson Comorbidity Index (CCI), for this purpose. Materials and methods: This cross-sectional retrospective study analyzed data from 396 COPD cases. We examined associations between the DOSE index, BODE index, ADO index, CCI, and healthcare costs, including hospitalizations and emergency room treatments. Healthcare costs were categorized as direct medical expenses. Results: Significant associations were observed between the DOSE index and various healthcare parameters. DOSE quartiles showed strong correlations with outpatient visits (p = 0.013) and outpatient medical expenses (p = 0.011). In addition, hospitalization frequency, duration, and associated costs were significantly correlated with higher DOSE quartiles (p < 0.001). A significant difference was found when comparing DOSE quartiles between patients with high (CCI ≥ 3) and low (CCI < 3) comorbidity scores (p = 0.018). The DOSE index outperformed other indices, likely due to its inclusion of exacerbations, a key driver of healthcare costs. Conclusion: The DOSE index demonstrates potential in predicting healthcare costs, particularly due to its inclusion of exacerbation frequency. This study highlights the importance of considering exacerbations alongside traditional risk factors for more accurate cost forecasting in COPD management. Our findings suggest that the DOSE index may be a valuable tool in both clinical and economic assessments of COPD patients, though further research is warranted to validate these findings in larger datasets. [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]

To solve the problem of difficulty for robots to quickly explore unknown environments with narrow entrances, as well as the decrease in exploration efficiency caused by wandering between targets with close exploration benefits, this paper proposed a self-exploration algorithm USAGE (uniformly scanning and attentive guidance explorer), which included uniform scanning and attentive guidance strategy. USAGE used the uniform scanning method to detect boundary points, and clustered the boundary points to obtain the points to be explored, and finally determined the optimal exploration target through an attentive guidance strategy. This strategy introduced the steering cost evaluation index into the traditional evaluation function containing information gain and path cost, and constrained the execution of the exploration task according to the robot's state, and guided the robot to focus on exploration. Through building a simulation environment in the robot operating system for verification, the experimental results show that compared with the exploration algorithm based on rapidly-exploring random tree (RRT), USAGE occupies less system memory by more than 11.34%, reduces the exploration time and exploration path level by 26. 90% and 31.94% respectively, which improves autonomous exploration efficiency. [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]

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]

Purpose: The purpose of this study is to make property price forecasts for the Chinese housing market that has grown rapidly in the last 10 years, which is an important concern for both government and investors. Design/methodology/approach: This study examines Gaussian process regressions with different kernels and basis functions for monthly pre-owned housing price index estimates for ten major Chinese cities from March 2012 to May 2020. The authors do this by using Bayesian optimizations and cross-validation. Findings: The ten price indices from June 2019 to May 2020 are accurately predicted out-of-sample by the established models, which have relative root mean square errors ranging from 0.0458% to 0.3035% and correlation coefficients ranging from 93.9160% to 99.9653%. Originality/value: The results might be applied separately or in conjunction with other forecasts to develop hypotheses regarding the patterns in the pre-owned residential real estate price index and conduct further policy research. [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]

Complex microscopic simulation models of strategic Air Traffic Management (ATM) performance assessment and decision-making are hindered by several factors. One of the most important is the existence of hidden parameters—such as aircraft take-off weight (TOW) and the selected cost index (CI)—which, if known, would allow for more effective performance modeling methodologies for assessing Key Performance Indicators (KPIs) at various levels of abstraction/detail, e.g., system-wide, or at the level of individual flights. This research proposes a data-driven methodology for the estimation of flights' hidden parameters combining mechanistic and advanced Artificial Intelligence/Machine Learning (AI/ML) models. Aiming at microsimulation models, our goal is to study the effect of these estimations on the prediction of flights' KPIs. In so doing, we propose a novel methodology according to which data-driven methods are trained given optimal trajectories (produced by mechanistic models) corresponding to known hidden parameter values, with the aim of predicting hidden parameters' values of unseen trajectories. The results show that estimations of hidden parameters support the accurate prediction of KPIs regarding the efficiency of flights: fuel consumption, gate-to-gate time and distance flown. [ABSTRACT FROM AUTHOR]

A number of studies have shown that, in hedonic models, the structural attributes of real property have a greater influence on price than external attributes related to location and the immediate neighbourhood. This makes it necessary to include detailed information about structural attributes when predicting prices using regression models and machine learning algorithms and makes it difficult to study the influence of external attributes. In our study of asking prices on the primary residential market in Warsaw (Poland), we used a methodology we developed to determine price indices aggregated to micro-markets, which we further treated as a dependent variable. The analysed database consisted of 10,135 records relating to 2444 residential developments existing as offers on the market at the end of each quarter in the period 2017–2021. Based on these data, aggregated price level indices were determined for 503 micro-markets in which primary market offers were documented. Using the analysed example, we showed that it is possible to predict the value of aggregated price indices based only on aggregated external attributes—location and neighbourhood. Depending on the model, we obtained an R2 value of 75.8% to 82.9% for the prediction in the set of control observations excluded from building the model. [ABSTRACT FROM AUTHOR]

The hedge fund (HF) industry is known to be one of the most unequal professional fields when it comes to gender. This study quantifies and confirms this severe gender gap, which appears to be persistent or even widening in recent years. We assess whether performance and risk differences explain this gap by comparing samples of woman-managed HFs vs. man-managed HFs. Through analyzing their descriptive statistics first, examining their alphas using HF benchmark indices and a pricing model and, finally, comparing differences in wealth generation, we found no evidence of performance differences that could explain such an extreme gender gap. Furthermore, our results do not support the view that women are more risk-averse than men, or that this is translated into their investment decisions. Other sociocultural factors probably partly explain the existence and persistence of this gender gap in hedge funds. [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]

Purpose: This study aims to examine the asymmetric S-curve between the trade balances of Pakistan and China at the commodity level using disaggregated data. Design/methodology/approach: This study focuses on Pakistan and China bilateral trade based on commodity-level data. This study delves into the S-curve phenomena by examining time series data from 1980 to 2023 across 32 three-digit industries/commodities. Findings: The findings show significant evidence in favor of the "asymmetric S-curve" in 27 out of the 32 industries studied. This study confirms that the devaluation of home currency is not a viable solution always to improve trade balance. Research limitations/implications: This study considers 32 three-digit industries limiting the generalizability of findings. Due to data unavailability, the authors fail to consider other industries. In the absence of quarterly data on industry-level trade between Pakistan and China, annual data from 1980 to 2023 were used in generating the cross-correlation functions. Previous literature frequently resorted to the general consumer price index with its inherent aggregation issues, whereas this study has opted for commodity price indices to overcome the shortcomings in the estimation of S-curves at the commodity level. Practical implications: The findings have practical relevance in guiding policy decisions regarding commodity trade, whereas the industry-wise analysis enriches the understanding of the short-term effects of currency depreciation on trade balance dynamics. Originality/value: The S-curve hypothesis predicts a negative cross-correlation between a country's current exchange rate and its past trade balance and a positive cross-correlation between the current exchange rate and its future trade balance. Previous empirical S-curve studies had the limitation of assuming symmetry in cross-correlation with both current and future trade balance values. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to investigate markets' integration using the capital enhanced equilibrium exchange rate (CHEER) model for seven, highly competitive, East Asian countries. Design/methodology/approach: The sample consists of monthly observations, whereas unit root and cointegration techniques with structural shifts have been used. Findings: The evidence shows that the weak form of the CHEER approach holds for Malaysia and Thailand. For China, Japan, Korea, Singapore and Taipei, only the uncovered interest parity condition is validated, implying capital markets integration. In contrast, for these five countries, the results indicate absence of goods' markets integration. This outcome can be attributed to the impact of quite high non-tariff barriers and the Balassa–Samuelson effect. Originality/value: To the best of the author's knowledge, this is the first study that investigate markets' integration in several East Asian economies, using the CHEER approach and more accurate price 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]

Every construction project faces cost, schedule, and quality constraints that must be managed to achieve the desired objectives. This research aims to control time and cost in construction projects by applying the Earned Value Concept and Crash Program methods. The subject of the study is the Preservation of Roads and Bridges project in Bireuen City - BTS, Lhokseumawe/North Aceh, Krueng Mane Buket Rata, SP. Krueng Geukeueh - PEL. Krueng Geukeueh (PEL. Lhokseumawe). The data analyzed includes the Budget Plan (RAB), project schedule, and reports on planned and actual work. This data is processed into three leading indicators: BCWS, BCWP, and ACWP, which are then analyzed through Schedule Variance (SV), Cost Variance (CV), Schedule Performance Index (SPI), and Cost Performance Index (CPI). The analysis results show that during weeks 22 to 23, the project had a positive Schedule Variance (SV) (>0), which then turned negative during weeks 24 to 28, with an average Schedule Performance Index (SPI) of 1.0096, indicating that project execution was ahead of schedule. Meanwhile, the Cost Variance (CV) was upbeat throughout the project, and the average Cost Performance Index (CPI) was 1.2118, indicating that actual costs did not exceed the budget. The application of the Crash Program method resulted in changes in heavy equipment rental costs and worker wages, from IDR 1,440,469,946.99 over 50 days to IDR 1,853,072,988.06 over 25 days, with a cost increase of IDR 412,603,041.07 and an acceleration ratio of 1.29. This research demonstrates that the Earned Value Concept and Crash Program methods effectively control time and cost in construction projects, helping achieve better project management efficiency. [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]

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]

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.)

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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]

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]

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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 [...]

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]

Objectives: To assess the direct hospital costs for unplanned re‐admissions within 30 days of hospitalisations with heart failure in Australia; to estimate the proportion of these costs attributable to potentially preventable re‐admissions. Study design: Retrospective cohort study; analysis of linked admitted patient data collections data. Setting, participants: People admitted to hospital (all public and most private hospitals in Australia) with primary diagnoses of heart failure, 1 January 2013 – 31 December 2017, who were discharged alive and re‐admitted to hospital at least once (any cause) within 30 days of discharge. Main outcome measures: Estimated re‐admission costs based on National Hospital Cost Data Collection, by unplanned re‐admission category based on the primary re‐admission diagnosis: potentially hospital‐acquired condition; recurrence of heart failure; other diagnoses related to heart failure; all other diagnoses. The first two groups were deemed the most preventable. Results: The 165 612 eligible hospitalisations of people with heart failure during 2013–2017 (mean age, 79 years [standard deviation, 12 years]; 85 964 men [51.9%]) incurred direct hospital costs of $1881.4 million (95% confidence interval [CI], $1872.5–1890.2 million), or $376.3 million per year (95% CI, $374.5–378.1 million per year) and $11 360 per patient (95% CI, $11 312–11 408 per patient). A total of 41 125 people (24.8%) experienced a total of 58 977 unplanned re‐admissions within 30 days of discharge from index admissions; these re‐admissions incurred direct hospital costs of $604.4 million (95% CI, $598.2–610.5 million), or 32% of total index admission costs; that is, $120.9 million per year (95% CI, $119.6–122.1 million per year), and $14 695 per patient (95% CI, $14 535–14 856 per patient). Re‐admissions with potentially hospital‐acquired conditions (21 641 re‐admissions) accounted for 40.1% of unplanned re‐admission costs, recurrence of heart failure (18 666 re‐admissions) for 35.6% of re‐admission costs. Conclusion: Unplanned re‐admissions after hospitalisations with heart failure are expensive, incurring costs equivalent to 32% of those for the initial hospitalisations; a large proportion of these costs are associated with potentially preventable re‐admissions. Reducing the number of unplanned re‐admissions could improve outcomes for people with heart failure and reduce hospital costs. [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]

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]

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]

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]

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]

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