From exposure to expansion: understanding the economic impact of online attention through media function theory

Ferritin is the best biomarker for assessing iron deficiency, but ferritin concentrations increase with inflammation. Several adjustment methods have been proposed to account for inflammation's effect on iron biomarker interpretation. The most recent and highly recommended method uses linear regression models, but more research is needed on other models that may better define iron status in children, particularly when distributions are heterogenous and in contexts where the effect of inflammation on ferritin is not linear. Assess the utility and relevance of quadratic regression models and quantile quadratic regression models in adjusting ferritin concentration in the presence of inflammation. We used data from children aged under five years, taken from Cuba's national anemia and iron deficiency survey, which was carried out from 2015-2018 by the National Hygiene, Epidemiology and Microbiology Institute. We included data from 1375 children aged 6 to 59 months and collected ferritin concentrations and two biomarkers for inflammation: C-reactive protein and α-1 acid glycoprotein. Quadratic regression and quantile regression models were used to adjust for changes in ferritin concentration in the presence of inflammation. Unadjusted iron deficiency prevalence was 23% (316/1375). Inflammation-adjusted ferritin values increased iron-deficiency prevalence by 2.6-4.5 percentage points when quadratic regression correction model was used, and by 2.8-6.2 when quantile regression was used. The increase when using the quantile regression correction model was more pronounced and statistically significant when both inflammation biomarkers were considered, but adjusted prevalence was similar between the two correction methods when only one biomarker was analyzed. The use of quadratic regression and quantile quadratic regression models is a complementary strategy in adjusting ferritin for inflammation, and is preferable to standard regression analysis when the linear model's basic assumptions are not met, or when it can be assumed that ferritin-inflammation relationships within a subpopulation may deviate from average trends.

This study aims to investigate the impact of productive service industry agglomeration on the economic performance of Chinese cities. The study analyzes data from 285 prefecture-level cities over a 16-year period (2003–2019) and develops a comprehensive urban economic index evaluation system based on three dimensions: social, economic, and resource and environmental benefits. The empirical analysis reveals that high-end productive service industry agglomeration has a significant positive relationship with urban economic performance (effect size of 0.012), whereas low-end productive service industry agglomeration does not. The study also finds that the impact of productive service industry agglomeration on economic performance varies across different regions and cities. Specifically, productive service industry agglomeration has a significant positive impact on the economic performance of cities in the eastern, central, northeastern, and western regions, with the impact being twice as much in the western region compared to the eastern region. Moreover, the effect of productive service industry agglomeration on the economic performance of non-resource-based cities is almost one-third more than that of resource-based cities. The study further reveals that human capital plays a mediating role in the relationship between productive service industry agglomeration and urban economic performance. Based on these findings, the study suggests that Chinese cities, particularly non-resource-based cities and those in the central and western regions, should focus on supporting high-end productive service industry agglomeration, improve the quality of productive service industry agglomeration, strengthen the matching between the accumulation of human capital and the development of productive service industry agglomeration, and tailor their strategies to promote the development of productive service industry agglomeration effectively to improve their economic performance.

Foreign direct investment is an important component of state economic development and state political economy, yet relatively little is known about the political factors that influence foreign firm location decisions. Previous econometric studies of firm location decisions have downplayed the role of political conditions (i.e., institutions, parties, and policy interventions) in influencing firm location decisions, whereas case studies have provided evidence of political effects that are limited to specific firms. This study combines important elements from each of these perspectives into a regional- and time-effects Poisson analysis of foreign manufacturing firm location decisions in the American states from 1985 to 1990. The authors depict firm location decisions as a function of several sets of variables, including labor market conditions, manufacturing density, infrastructure/transportation, access to markets, taxes, the national context, state institutional ideology, and state policy activism. Findings suggest that several of these variables have significant impacts on foreign firm location decisions.

What Underlies Localization and Urbanization Economies? Evidence from the Location of New Firms

This study examines the interplay between urban policy interventions, infrastructure investments, and economic and labor market outcomes in large metropolitan areas from 2017 to 2022. We have employed empirical research to unravel the phenomena among these domains by employing a comprehensive dataset comprising gross value added (GVA), compensation of employees, gross fixed capital formation, environmental taxes, trade, employment, and time series data for demographic cohort transitions. Through a linear regression analysis, we uncovered strong positive associations between gross fixed capital formation and GVA and employment per capita, highlighting the critical role of infrastructure in driving urban economic growth. We also scrutinize how population dynamics—natural population changes and net migration—affect economic performance, offering valuable insights for evidence-driven urban policy. This research informs sustainable urban economic development policies and contributes significantly to the urban economics discourse.

Assessing the impact of local meteorological variables on surface ozone in Hong Kong during 2000–2015 using quantile and multiple line regression models

This paper discusses the crucial but sometimes neglected differences between unconditional quantile regression (UQR) models and quantile treatment effects (QTE) models. We argue that there is a frequent mismatch between the aim of the quantile regression analysis and the quantitative toolkit used in much of the applied literature, including the motherhood wage penalty literature. This mismatch may result in wrong conclusions being drawn from the data, and in the end, misguided theories. In this paper, we clarify the crucial conceptual distinction between influences on quantiles of the overall distributions, which we term population-level influences, and individual-level QTEs. Further, we use data simulations to illustrate that various classes of quantile regression models may, in some instances, give entirely different conclusions (to different questions). Finally, we compare quantile regression estimates using real data examples, showing that UQR and QTE models differ sometimes but not always. Still, the conceptual and empirical distinctions between quantile regression models underline the need to match the correct model to the specific research questions. We conclude the paper with a few practical guidelines for researchers.

Background: This study involves forecasting electricity demand for long-term planning purposes. Long-term forecasts for hourly electricity demands from 2006 to 2023 are done with in-sample forecasts from 2006 to 2012 and out-of-sample forecasts from 2013 to 2023. Quantile regression (QR) is used to forecast hourly electricity demand at various percentiles. Three contributions of this study are (1) that QR is used to generate long-term forecasts of the full distribution per hour of electricity demand in South Africa; (2) variabilities in the forecasts are evaluated and uncertainties around the forecasts can be assessed as the full demand distribution is forecasted and (3) probabilities of exceedance can be calculated, such as the probability of future peak demand exceeding certain levels of demand. A case study, in which forecasted electricity demands over the long-term horizon were developed using South African electricity demand data, is discussed. Aim: The aim of the study was: (1) to apply a quantile regression (QR) model to forecast hourly distribution of electricity demand in South Africa; (2) to investigate variabilities in the forecasts and evaluate uncertainties around point forecasts and (3) to determine whether the future peak electricity demands are likely to increase or decrease. Setting: The study explored the probabilistic forecasting of electricity demand in South Africa. Methods: The future hourly electricity demands were forecasted at 0.01, 0.02, 0.03, … , 0.99 quantiles of the distribution using QR, hence each hour of the day would have 99 forecasted future hourly demands, instead of forecasting just a single overall hourly demand as in the case of OLS. Results: The findings are that the future distributions of hourly demands and peak daily demands would be more likely to shift towards lower demands over the years until 2023 and that QR gives accurate long-term point forecasts with the peak demands well forecasted. Conclusion: QR gives forecasts at all percentiles of the distribution, allowing the potential variabilities in the forecasts to be evaluated by comparing the 50th percentile forecasts with the forecasts at other percentiles. Additional planning information, such as expected pattern shifts and probable peak values, could also be obtained from the forecasts produced by the QR model, while such information would not easily be obtained from other forecasting approaches. The forecasted electricity demand distribution closely matched the actual demand distribution between 2012 and 2015. Therefore, the forecasted demand distribution is expected to continue representing the actual demand distribution until 2023. Using a QR approach to obtain long-term forecasts of hourly load profile patterns is, therefore, recommended.

ABSTRACTThe objective of this paper is to analyze why firms in some industries locate in specialized economic environments (localization economies) while those in other industries prefer large city locations (urbanization economies). To this end, we examine the location decisions of new manufacturing firms in Spain at the city level and for narrowly defined industries. First, we estimate firm location models to obtain estimates that reflect the importance of localization and urbanization economies in each industry. Then, we regress these estimates on industry characteristics related to the potential importance of labor market pooling, input sharing, and knowledge spillovers. Urbanization effects are high in knowledge‐intensive industries, suggesting that firms locate in large cities to benefit from knowledge spillovers. We also find that localization effects are high in industries that employ workers whose skills are more industry‐specific, suggesting that industries locate in specialized economic environments to share a common pool of specialized workers.

. To address issues of parameter estimation and variable selection of both random and fixed effects in linear mixed- effect model for longitudinal data, this article introduces a novel linear mixed-effects double penalized L p -quantile regression (LME-DL p QR) model, which combines L p -quantile regression with a double-penalty approach. An iterative Lasso-L p -quantile regression algorithm is proposed to implement LME-DL p QR to estimate parameters and select variables, with penalty parameters chosen using two common criteria. Under certain conditions, LME-DL p QR achieves asymptotic normality of the estimation of regression coefficients. Simulations are conducted to study the performance of LME-DL p QR in coefficient estimation and variable selection across different quantiles, considering variations in signal-to-noise ratios, random effects, and model sparsity. The proposed method exhibits robustness and sensitivity, reflecting the advantages of L p -quantile regression when p taking different values. The study also summarizes the efficiency of selecting important variables for various p values. Under diverse conditions, LME-DL p QR with p values between 1 and 2 outperforms the linear mixed effects double penalized quantile (p = 1) and expectile (p = 2) regression model. Finally, the practical utility of LME-DL p QR is demonstrated for future application research of longitudinal data through its analysis on two real datasets.

So far, compared with quantile regression models, there has been relatively little research work done on quantile residual life regression models. The main purpose of this paper is to make statistical inference for quantile residual life regression model with censored length-biased data. Based on the martingale theory, two estimating equations are developed, which can lead to avoid estimating the survival function of the censoring variable, and a two-stage procedure is proposed to calculate the estimates of regression parameters, too. The uniform consistency and weak convergence of proposed estimators are also provided. In addition, results of the simulations show that the proposed composite martingale-based estimating method performs slightly better than the proposed martingale-based one in terms of the empirical standard derivations and empirical mean square errors. Finally, the Channing House data is employed to illustrate their applications.

This paper empirically studied relocation factors for manufacturing and service firms in the Seoul Metropolitan Area. The purpose of this study is to identify the influence not only of traditional firm location factors, but also of quality of life factors. A firm's location decision is an assertive action (behaviour) to maximize profit that may have a large ripple effect on the regional economy and society. This study used a binary logit model to analyse location choice factors during firm relocations. As of December 2011, the number of firms registered with the Korea Chamber of Commerce and Industry was 117,157. Of these firms, 36,598 were selected. The firms’ locations could be tracked because their names and owners did not change from December 2006 to December 2012. Of the selected firms, 8328 (22.8%) relocated during this period. It can be considered that 4.6% of all firms relocate in a year. A geographic information system database was built using location addresses of the 8328 firms. This study exhibited a trend that manufacturing firms moved from cities to suburbs, where transport and industry conditions were more favourable. By contrast, firms in higher-order industries with high-added value moved to city centres, where living conditions were more favourable. This trend suggests that quality of life factors have become more important in firm location decisions. This study is expected to be used to understand changes in firm locations and relocations, and to help make industrial policies for drawing firms.

The importance of the height-diameter (H-D) relationship in forest productivity is well known. The general nonlinear regression model, based on the mean regression technical, is not able to give a complete description of the H-D relationship. This study aims to evaluate the H-D relationship among relative competition levels and develop a quantile regression (QR) model to fully describe the H-D relationship. The dominance index was applied to determine the relative competition levels of trees for the Chinese fir. Based on the basic Weibull growth model, the mean regression for five relative competition levels and 11 QR models was constructed with 10-fold cross-validation. We have demonstrated that the H-D relationship for the Chinese fir strongly correlated with relative competition states, but the five curves from mean regression models did not show a notable difference between the trends of H-D relationship under different competition levels. Similar regression results were found in QR models of the specific quantiles; the average tree height of five competition levels varied between 5.78% and 17.65% (i.e., about 0.06 and 0.18 quantiles). In addition, some special curves of the H-D relationship such as the QR models of the 0.01 and 0.99 quantile showed the H-D relationship under certain conditions. These findings indicate that the QR models not only evaluated the rates of change of the H-D relationships in various competition levels, but also described their characteristics with more information, like the upper and lower boundary of the conditional distribution of responses. Although the flexible QR curves followed the distribution of the data and showed more information about the H-D relationships, the H-D curves may not intersect with each other, even when the trees reached their maximum height. Hence, the QR model requires further practice in assessing the growth trajectory of the tree’s diameter or tree height to gain better results.

Consider sensitivity analysis for estimating average treatment effects under unmeasured confounding, assumed to satisfy a marginal sensitivity model. At the population level, we provide new representations for the sharp population bounds and doubly robust estimating functions. We also derive new, relaxed population bounds, depending on weighted linear outcome quantile regression. At the sample level, we develop new methods and theory for obtaining not only doubly robust point estimators for the relaxed population bounds with respect to misspecification of a propensity score model or an outcome mean regression model, but also model-assisted confidence intervals which are valid if the propensity score model is correctly specified, but the outcome quantile and mean regression models may be misspecified. The relaxed population bounds reduce to the sharp bounds if outcome quantile regression is correctly specified. For a linear outcome mean regression model, the confidence intervals are also doubly robust. Our methods involve regularized calibrated estimation, with Lasso penalties but carefully chosen loss functions, for fitting propensity score and outcome mean and quantile regression models. We present a simulation study and an empirical application to an observational study on the effects of right-heart catheterization. The proposed method is implemented in the R package RCALsa.

This study used a quantile regression model and a non-homogeneous regression model to calibrate probabilistic forecasts of wind speed. These techniques were applied to the forecasts of wind speed over Pyeongchang area using 51-member European Centre for Medium-Range Weather Forecast (ECMWF). Reliability analysis was carried out by using rank histogram to identify the statistical consistency of ensemble forecasts and corresponding observations. The performances were evaluated by rank histogram, mean absolute error, root mean square error and continuous ranked probability score. The results showed that the forecasts of quantile regression and non-homogeneous regression models performed better than the raw ensemble forecasts. However, the differences of prediction skills between quantile regression and nonhomogeneous regression models were insignificant.