This paper analyses a model of private value auctions with symmetric risk-neutral bidders, where bidders' private values of an indivisible good are fuzzy. The auction is studied as a game with incomplete information. Fuzzy random variables, their quantile functions, and expressions for expectations through quantile functions are used. An explicit expression for the solution is found. Also, expected bidders' payments are studied.

Vector Error Correction Models (VECM) have become a standard tool in empirical economics for analyzing nonstationary time series data because they integrate two key concepts in economics: equilibrium and dynamic adjustment in a single model. The current standard VECM procedure is limited to time series data with the same degree of integration, i.e., all I(1) variables. However, empirical studies often involve time series data with different de‐grees of integration, necessitating the simultaneous handling of I(1) and I(0) time series. This paper extends the standard VECM to accommodate mixed I(1) and I(0) variables. The conditions for the mixed VECM are derived, and consequently, we present a test and estimation for the mixed VECM.

Credit scoring remains one of the most important subjects in financial risk management. Although the methods in this field have grown in sophistication, further improvements are necessary. These advances could translate in major gains for financial institutions and other companies that extend credit by diminishing the potential for losses in this process. This research seeks to compare statistical and artificial intelligence (AI) predictors in a credit risk analysis setting, namely the discriminant analysis, the logistic regression (LR), the artificial neural networks (ANNs), and the random forests. In order to perform this comparison, these methods are used to predict the default risk for a sample of companies that engage in trade credit. Pre-processing procedures are established, namely in the form of a proper sampling technique to assure the balance of the sample. Additionally, multicollinearity in the dataset is assessed via an analysis of the variance inflation factors (VIFs), and the presence of multivariate outliers is investigated with an algorithm based on robust Mahalanobis distances (MDs). After seeking the most beneficial architectures and/or settings for each predictor category, the final models are then compared in terms of several relevant key performance indicators (KPIs). The benchmarking analysis revealed that the artificial intelligence methods outperformed the statistical approaches.

The precise prediction of carbon emissions trading prices is the foundation for the stable and sustainable development of the carbon financial market. In recent years, influenced by a combination of factors such as the pandemic, trading regulations, and policies, carbon prices have exhibited strong random volatility and clear non-stationary characteristics. Traditional single-perspective prediction methods based on conventional statistical models are increasingly inadequate due to the homogenization of features and are struggling to adapt to China's regional carbon emissions trading market. Therefore, this paper proposes a multi-perspective fusion-based prediction method tailored to the Chinese market. It leverages carbon emissions trading information from key cities as relevant features to predict the price changes in individual cities. Inspired by the development of artificial intelligence, this paper implements various time series models based on deep neural networks. The effectiveness of the multi-perspective approach is validated through multiple metrics. It provides scientific decision-making tools for domestic carbon emissions trading investors, making a significant contribution to strengthening carbon market risk management and promoting the establishment and rational development of a unified carbon market in China.

This study focuses on the impact of trade in environmental goods (green trade) on the environment. We found that green trade can decrease pollution levels by exploiting a panel of 277 Chinese cities from 2004 to 2013 and using the instrumental variable (IV) strategy. However, total trade openness is far less favorable to the environment. We also found that both green imports and exports are conducive to the Chinese environment, while ordinary green trade performs better than green processing trade. Nevertheless, the effects of green trade are restricted by a city's purchasing power and absorptive capacity, as well as the classifications of environmental goods. Furthermore, green trade mainly promotes local green technological progress to benefit the environment.

Hundreds of articles have been written that include empirical estimates of the dis-employment effects of minimum wages; however, many of these articles find statistically insignificant effects, some find significant negative effects, and a few find significant positive effects. Most of these studies use multivariate analyses which can be criticized for omitting key variables. The omitted variables problem ruins all statistics and estimates. This paper uses reiterative truncated projected least squares (RTPLS), a solution to the omitted variables problem, to estimate the percentage increase in unemployment due to a one percent increase in the real minimum wage using monthly data for the 50 states of the USA from 1987 to 2021. RTPLS produces a separate elasticity for every observation where differences in these estimates are due to omitted variables. We argue that RTPLS solves most of the econometric problems that David Neumark identified in his keynote address at a minimum wage conference in Berlin in 2018. We find that the percentage change in the unemployment rate due to a one percent change in the minimum wage ranges between 1.156 and 3.389, that the elasticities for different states tend to move together over time, and that all these elasticities are statistically significant at a 95 percent confidence level.

The goal of this paper is to work out a poststratification method for estimating in-time indicators for international end-to-end delivery processes when

• collected data cannot cover all the strata making up the logistics universe to be surveyed,
• the true weights of the strata - needed to correct biases in representativeness caused by disproportionate sampling and incomplete coverage - are unknown but can be inferred from marginal subtotals related to stratification criteria considered separately, rather than jointly, and conditional on each end of the delivery journey: outbound- versus inbound-specific. Within this perspective, poststratification is used here to mean a statistical correction of measurements derived from incomplete stratified samples, an ex-post calibration aimed at yielding more accurate estimates based on an analysis of the data. Thus, we tackle instances where ex-ante assignment to strata is not a problem, but when surveying all strata is out of the question.

For that purpose, an econometric model is designed

• to link the discrete transport lead times, counted in days, of tested items to the specifics of their material characteristics (e.g. size/weight), as well as those of the routes they take through the distribution network (e.g. origin and destination zones),
• and provide performance predictions for each of the strata, covered as well as non-covered.

Benchmarking the multinomial cumulative logit regression against the negative binomial one reveals that delivery time had better be treated as an ordinal categorical system’s response, rather than as a ratio-scaled count.

The model-based fitted and extrapolated estimates are then used as inputs to the ex-post weighting stage, which produces robust point- and interval-estimates of aggregate key performance indicators (KPIs) through bootstrapping. Simple linear programs provide two extreme weighting sets, one per country-to-country path: the first minimizes the KPIs’ values, while the second maximizes them. 

Probabilities of delivery within deadlines summarize distributions of delivery times better than their means and standard deviations, because logistical efforts to cut transit by one day must be enhanced more and more as it gets shortened. Three types of graphs are proposed to help visualize this exponential increase in the service quality required. The applicability of the methodology developed is demonstrated on the 2023 database of the International Post Corporation. In this case, the imprecision of the KPI estimates depends much more on the uncertainty caused by disturbances occurring during the first- and last-miles, than on the imperfection of the information about the real weights of the strata. 

Non-labor income is a crucial factor influencing time allocation, and prior studies have primarily concentrated on the linear association between non-labor income and labor hours. Utilizing micro-survey data from the CFPS in 2018 and 2020 and employing the panel threshold model, this paper empirically identifies a double threshold with the wage rate as the threshold variable. This finding reveals a non-linear relationship between non-labor income and labor hours. The two thresholds categorize the relationship into three intervals. In the first interval, non-labor income significantly promotes labor hours, while in the second and third intervals, non-labor income significantly decreases labor hours, exhibiting slightly varying degrees of influence. In general, the relationship between non-labor income and labor hours demonstrates an irregular inverted U-shaped pattern. Upon dividing the workers in the sample into three groups based on the two thresholds, it is observed that wage rates exhibit a positive correlation with non-labor income.

This research evaluates the efficacy of survival models in forecasting startup failures and investigates their economic implications. Several machine learning survival models, including Kernel SVM, DeepSurv, Survival Random Forest, and MTLR, are assessed using the concordance index (C-index) as a measure of prediction accuracy. The findings reveal that more sophisticated models, such as Multi-Task Logical Regression (MTLR) and Random Forest, outperform the standard Cox and Kaplan Meier (K-M) models in terms of predicted accuracy.

This research delves into the temporal dynamics of a nation's pursuit of a targeted GDP per capita level, employing five different survival machine learning models, remarkably Deep Learning algorithm (DeepSurv) and Survival Random Forest. This nuanced perspective moves beyond static evaluations, providing a comprehensive understanding of the developmental processes shaping economic trajectories over time. The economic implications underscore the intricate balance required between calculated risk-taking and strategic vulnerability mitigation. These findings guide policymakers in formulating resilient economic strategies for sustained development and growth amid the complexities inherent in contemporary economic landscapes.

This study investigates the impact of COVID-19 on bank borrowers across 22 Arab economies from 2017 to 2022, employing the two-step system GMM estimator. The findings reveal a significant negative effect of COVID-19 on the number of bank borrowers, highlighting a sharp decline in borrowing activity due to the pandemic’s economic disruptions. Notably, the decline was more pronounced in non-GCC countries, where economic structures and pandemic responses differed. Robustness tests, including subsample analysis and alternative econometric models (pooled OLS, fixed effects, random effects, and 2SLS), consistently confirm these results. The study emphasizes the need for targeted policy interventions to stabilize financial systems, reduce borrowing costs, and control exchange rate fluctuations to maintain credit access during crises. The findings provide crucial insights for policymakers aiming to support economic recovery and strengthen the resilience of the banking sector in the face of future external shocks.

This study aims to investigate the factors influencing consumers’ cognitive attitude towards the brands of products during their consumption willingness, behaviors and choices. Taking pork brands as an example, this study conducts a questionnaire by using a random sampling method in 165 farmers’ meat markets and 54 supermarkets in Tianhe District, Yuexiu District, Liwan District, the representative districts in Guangzhou city in China, and obtains 1,050 valid observations of the pork consumers. The empirical results via a logit regression show that the factors significantly enhance consumers’ brand cognitive attitude include younger age; lower perception of product safety; less cognition of green food, frailer awareness of fresh, cold, and frozen meats, as well as weaker ability to identify a specific product. Also, stronger consumers’ brand attitude results from larger amount of money spent in a single purchase, stronger cognition of nuisance-free food, and higher identification of different types of pork meats.

The Weibull distribution is extensively useful in the field of finance, insurance and natural disasters. Recently, It has been considered as one of the most frequently used statistical distributions in modelling and analyzing stock pricing movement and uncertain prediction in financial and investment data sets, such as insurance claims distribution. It is well known that the Bayes estimators of the two-parameter Weibull distribution do not have a compact form and the closed-form expression of the Bayes estimators cannot be obtained. In this paper and the Bayesian setting, it is assumed that the scale parameter of the Weibull model has a gamma prior under the assumption that its shape parameter is known. A simulation study is performed using random claims amount to compare the performance of the Bayesian approach with traditional maximum likelihood estimators in terms of Root Mean Square Errors (RMSE) and Mean Absolute Error (MAE) for different sample sizes, with specific values of the scale parameter and shape parameters. The results have been compared with the estimated result via the maximum likelihood method. The result revealed that the Bayesian approach behaves similarly to the maximum likelihood method when the sample size is small. Nevertheless, in all cases for both methods, the RMSE and MAE decrease as the sample size increases. Finally, applications of the proposed model to the insurance claim data set have been presented.

This study documents the relationship between economic freedom and unauthorized immigration to the U.S.A. and tries to answer a still-in-debate question, what motivates people to unauthorizedly migrate? Using the generated instrument variables methodology, and a data panel of 15 countries and 16 years, the endogenous variable is the gap in economic freedom between the country of origin and the U.S., and the dependent variable is the unauthorized immigrant’s growth rate. The results show that for each 10% improvement in economic freedom in the country-of-origin unauthorized immigration decreases around 7%. Estimations are robust even after using other covariates and external instruments. The main conclusion is that economic and social factors play a significant role in shaping migration patterns and that the difference in economic freedom between the country of origin and the U.S. plays a fundamental role in deciding to unauthorizedly migrate

This study examines the response of real stock returns to expected inflation and uncertainty as measured by state variable correlated with equity market volatility (EMV). Evidence reveals a significantly negative relationship between real stock returns and expected inflation for each country except some cases in India and Japan. Evidence indicates a negative relationship between real stock returns and uncertainty, which is measured not only by the impact of the Fed’s monetary policy uncertainty but also from various state variables that covary with EMV. These elements have not been explicitly incorporated into test equations in previous studies of the inflation-stock return relationship. The model is robust in its ability to test data for both advanced and emerging markets, level or the first difference of explanatory variables, and various categorical EMVs. Evidence shows that the Fed’s rate hikes respond to the inflation data, displaying a nonlinear impact on real stock returns.

The Oswald hypothesis is that home ownership reduces mobility and through that channel results in poorer labor market outcomes. There has been only limited previous evidence on the Australian case. Here we use data from the first four Australian censuses of the twenty-first century, aggregated at the smallest geographical areas for which statistics are released. We propose testing the Oswald hypothesis by estimating the effect of home ownership on the full-time employment rate. Modelling the unemployment rate, as is often done in testing the hypothesis, produces implausible results with respect to the control variables, most likely due to the limitations of the official definition of unemployment. We find that, in modelling the full-time employment rate, the control variables for educational and demographic factors have the theoretically plausible effects, while variables capturing rates for both outright home ownership and with mortgage home ownership are positively related to full-time employment rates, with the effect of the former stronger than the latter. Our findings strongly contradict the Oswald hypothesis.

This research paper investigates the policy impact of tariff, interest rate, and political stability on balance of payments among three income groups classified by the World Bank. The global panel data is taken from the World Bank from 2002 to 2022 and divided into three groups based on the classification on per capita income into High Income, Upper middle income, and Lower middle-income countries. This study explores how these variables impact balance of payments in different economic conditions and if their impact is different. The study employed in this paper is the Panel Least Squares, Fixed Effects Regression, GMM and Dumitrescu panel granger causality tests. The findings do confirm that these variables indeed have unlike impacts in the selected groups and the importance of tariff and interest rate as a policy tool cannot be sidelined. This research contributes to a deeper understanding of the complex relationship between macroeconomic variables within each income group also highlighting the importance of political stability for economic outlook.

High levels of trust are positively correlated with increased collaboration, prosocial actions, and heightened adherence to preventive behaviors during the COVID-19 pandemic. Previous studies on trust during the pandemic have primarily focused on either cross-sectional data or its impact in conjunction with other related variables, such as political party affiliations or vaccine availability. In this study, we employed a national survey panel comprising data from 760 individuals interviewed at three intervals between July 2020 and January 2021. We used pooled datasets, panel datasets, and dependent variable lags to control for time-invariant unobservable variables and endogeneity. Our findings reveal that trust in government influences individuals’ behavior when they are requested to follow public interventions. Notably, trust in local government is associated with increased adherence to COVID-19 preventive behaviors, similar to the effect observed with an annual income exceeding $100,000.

In the past, investing in housing has served as an engine of growth for many economies as it is widely recognized that poor housing conditions can have significant negative impacts on human health, education, and economic opportunities. To assess the housing-related quality of life, indicators such as housing quality, housing environment, and cost burdens can be applied. However, recent studies indicate that materials used for construction are critical. Permanent materials, as opposed to temporary materials, typically offer a range of benefits in terms of durability, low maintenance, improved energy efficiency, increased property value, and better safety. The ultimate aim is to identify the key drivers of housing conditions in Cambodia, with a particular focus on materials and water quality, and the legal status of housing. To find the empirical relationship between economic, socio-economic, and demographic variables on the one hand, and variables measuring housing and living conditions in Cambodia on the other, the analysis employs Ordinary Least Squares and Methods-of-Moments regression modeling. Results indicate that high employment rates and entrepreneurship increase home ownership, and improve the quality of drinking water available. Furthermore, in addition to employment and entrepreneurship, the higher-performing construction materials can also be empirically explained by a bigger labor force and variables capturing the wider macroeconomic environment.

The vigorous development of the digital economy has brought new opportunities and challenges to the construction of local fiscal revenue efficiency. Based on the panel data from 2011 to 2020, this paper uses the fixed effect model, instrumental variable method and other empirical studies to investigate the impact of the development of the digital economy on the efficiency of local fiscal revenue. The research results show that the development of the digital economy has directly improved the efficiency of local fiscal revenue, and the regression results of instrumental variables are still significant. The mechanism analysis shows that the development of digital economy mainly promotes local fiscal revenue such as efficiency improvement by cultivating tax sources.