Conditional Quantile Regression Research Articles

The U-shaped relationship between corruption and international tourism demand: A gravity model approach

The tourism industry significantly contributes to a country’s economic growth and creates a positive image of the destination. This study assesses the impact of corruption on international tourism demand. It employs two-dimensional analyses using the gravity model and a pooled ordinary least square estimator to provide a unique recognition of international tourism demand. The study utilized a conditional quantile regression technique to analyze a dataset of 200 destination countries from 1995 to 2022. The empirical results demonstrate a mixed effect of corruption on international tourism demand. The analysis reveals a non-linear relationship between corruption and international tourism demand, with the inverted-U relationship being statistically significant only at the 50th–75th quantiles and not holding at the upper and lower quantiles. The research confirms that income has a positive impact on tourism across quantiles, but the impact is disproportionate. The sub-period of 2006–2022 experienced a significant decline in gross domestic product (GDP) due to the global financial crisis and its aftershocks, which severely impacted the attractiveness of destination countries for tourists. These valuable insights can inform national tourism policies and businesses.

Is the Gender Pay Gap Largest at the Top?

Conditional quantile regressions reveal that, while the gender pay gap at the top percentiles is largest among the most educated, the gap at the bottom percentiles is largest among the least educated. Gender differences in work hours create more pay inequality among the least educated than they do among the most educated. The pay gap has declined throughout the distribution since 2006, but it has declined more for the most educated women. Current economics-of-gender research focuses heavily on the top end; equal emphasis should be placed on mechanisms driving gender inequality for non-college-educated workers.

Social Vulnerability and COVID-19 Pandemic Outcomes: Evidence from Spatial Quantile Regression

This paper evaluates the empirical relevance of a prominent social vulnerability measure to local economic and public health outcomes at two distinct stages of the COVID-19 pandemic. In contrast to traditional least-squares regression, conditional quantile regression offers stronger evidence in support of social vulnerability for predicting the pandemic’s uneven economic devastation and death tolls across the United States. An extension of the spatial autoregressive geographically weighted regression model to conditional quantile regression reveals the varying role of social vulnerability within and across broad regions that were exposed to different levels of pandemic impacts.

Asymmetric effects of macro policies on women’s and men’s incomes. An empirical investigation of the eurozone crisis in a gender perspective

AbstractWe study how macro policies affected women’s and men’s incomes during the financial crisis in Europe. We consider the monetary stance, proxied by benchmark interest rates, and the fiscal stance, measured by the variation in public expenditures and public revenues, and investigate how they are associated to women’s and men’s labor and capital incomes, using microdata for 27 European countries between 2008 and 2016. We individualize household-level data by considering four scenarios of intra-household sharing of resources. We also explore how and to what extent macro-policies affect the distribution of labour incomes for men and women by applying a conditional quantile regression approach. Results highlight that the ECB’s expansionary policies had a positive effect on both labor and capital incomes for both men and women, while austerity policies had a mixed impact. Reductions in public expenditure had the effect of reducing labor incomes for both men and women, particularly at the median of the wage and labor distributions. In contrast, increases in public revenues benefited capital incomes, for all income quantiles.

Unconditional Quantile Regression for Streaming Datasets

The Unconditional Quantile Regression (UQR) method, initially introduced by Firpo et al. has gained significant traction as a popular approach for modeling and analyzing data. However, much like Conditional Quantile Regression (CQR), UQR encounters computational challenges when it comes to obtaining parameter estimates for streaming datasets. This is attributed to the involvement of unknown parameters in the logistic regression loss function used in UQR, which presents obstacles in both computational execution and theoretical development. To address this, we present a novel approach involving smoothing logistic regression estimation. Subsequently, we propose a renewable estimator tailored for UQR with streaming data, relying exclusively on current data and summary statistics derived from historical data. Theoretically, our proposed estimators exhibit equivalent asymptotic properties to the standard version computed directly on the entire dataset, without any additional constraints. Both simulations and real data analysis are conducted to illustrate the finite sample performance of the proposed methods.

Scarred for Life? Early-Life Experience of the Post-Reunification Economic Crisis in East Germany and Physical and Mental Health Outcomes in Early Adulthood

Existing research suggests adverse short-term health effects of economic crises during early life, yet, the long-term health effects for children and adolescents exposed to economic crises are still understudied. We investigated the early-adult health implications of experiencing the post-reunification economic crisis in East Germany in the early 1990s during infancy, childhood and adolescence. Using data from the German Socio-Economic Panel (SOEP) and its linkage with German pension records (SOEP-RV), we applied entropy balancing and conditional quantile regression to assess the relationship between the experience of the economic crisis during early life (ages 0-17) and physical and mental health effects (SF-12 summary scores) in N = 2,337 young adults (aged 17-30) from East and West Germany. Our results indicate mainly no significant physical health effects, yet, significant adverse mental health effects for respondents exposed to the economic crisis in East Germany, especially in young women of average and better mental health. Parental unemployment was an additional risk factor for young women’s mental health. Thus, we suggest women who experienced economic crises during early life are at increased risk of adverse mental health effects already early in their adulthood. Support for families in times of economic crisis and early prevention for infants, children and adolescents exposed to economic adversities is warranted.

The Association between Education and Cognitive Performance Varies at Different Levels of Cognitive Performance: A Quantile Regression Approach

Introduction: Educational differences in cognitive performance among older adults are well documented. Studies that explore this association typically estimate a single average effect of education on cognitive performance. We argue that the processes that contribute to the association between education and cognitive performance are unlikely to have equal effects at all levels of cognitive performance. In this study, we employ an analytical approach that enables us to go beyond averages to examine the association between education and five measures of global and domain-specific cognitive performance across the outcome distributions. Methods: This cross-sectional study included 1,780 older adults aged 58–68 years from the Longitudinal Aging Study Amsterdam. Conditional quantile regression was used to examine variation across the outcome distribution. Cognitive outcomes included Mini-Mental State Examination (MMSE) score, crystallized intelligence, information processing speed, episodic memory, and a composite score of global cognitive performance. Results: The results showed that the associations between education and different cognitive measures varied across the outcome distributions. Specifically, we found that education had a stronger association with crystallized intelligence, MMSE, and a composite cognitive performance measure in the lower tail of performance distributions. The associations between education and information processing speed and episodic memory were uniform across the outcome distributions. Conclusion: Larger associations between education and some domains of cognitive performance in the lower tail of the performance distributions imply that inequalities are primarily generated among individuals with lower performance rather than among average and high performers. Additionally, the varying associations across some of the outcome distributions indicate that estimating a single average effect through standard regression methods may overlook variations in cognitive performance between educational groups. Future studies should consider heterogeneity across the outcome distribution.

Connectedness of Vietnamese bank stock returns under the impact of the COVID-19 pandemic

The COVID-19 pandemic highlighted the sensitivity of connectedness among bank stock returns in Vietnam. The aim of this study is to examine the strength of this connectedness along with the effect of government lockdown policy and COVID-19 cases on the total connectedness index (TCI) of 16 listed banks on Vietnamese stock exchanges. They are assessed using the database of FiinPro on the banking sector between January 2020 and July 2022, Vietnam Center for Disease Control and Prevention (CDC), and The World Health Organization (WHO) on the COVID-19 pandemic, employing a time-varying-parameter vector autoregressive (TVP-VAR) connectedness framework and the conditional quantile regression model. The results show that at the firm level, there is strong interdependence among bank stock returns with the average TCI being as high as 90.66%. It is also revealed that medium and large-sized banks are receivers of shock, while smaller banks are transmitters. As far as the impact on TCI is concerned, the widespread of the pandemic with the increasing number of COVID-19 cases is significantly negative, whereas the tightening of lockdown is significantly positive. Besides, the degree of the impact varies according to the 95th, 75th, 50th and 25th levels of conditional quantile regression. Based on the study’s findings, individual investors are recommended to thoroughly analyze the connectedness of banks before making investment decisions, while bank regulators should strengthen controls on credit relationships with small banks. Regarding policy makers, it is proposed to apply flexible restrictions and short-term lockdown depending on the actual outbreak of the pandemic. AcknowledgmentThe paper was conducted within the scope of Project QG21.48 of Vietnam National University.

Plant-wide byproduct gas distribution under uncertainty in iron and steel industry via quantile forecasting and robust optimization

In the modern iron and steel industry, the efficient distribution of byproduct gases faces significant challenges due to quantity- and quality-related uncertainties of gases. This study presents an optimal approach to gas distribution that addresses this issue by incorporating the energy flow network and the uncertain surplus gases from the manufacturing system. The uncertain optimization problem is formulated as a two-stage robust optimization (TSRO) model, including “here-and-now” decisions aimed at minimizing the start-stop cost of energy conversion units, as well as “wait-and-see” decisions aimed at minimizing the operating cost of gasholders and the penalties resulting from energy excess or shortage. To facilitate practical implementation, we propose a “first quantify, then optimize” approach: (1) quantifying the uncertainty of surplus gases via a conditional quantile regression (CDQ)-based T-step time series model, and (2) finding the optimal solution through a column-and-constraint generation algorithm. Furthermore, a case study is conducted on an industrial energy system to validate the proposed methodology. Computational results, using evaluation indicators, such as MAPE, RMSE, PICP, and PINAW, confirm the effectiveness of the data-driven time series model in accurately quantifying uncertainties in each period. Sensitivity analysis demonstrates that the proposed TSRO model achieves a favorable balance between robustness and flexibility by selecting the combination of “budget and quantile” and the parameters of storage and conversion units. Comparative results reveal: (1) the optimal objective of the TSRO closely aligns with that of stochastic programming (SP) and is 2.717 times higher than that of deterministic optimization (DO); and (2) the computation time of TSRO is 2.388 times longer than that of DO, yet significantly smaller than that of SP, being only 0.07 times longer. Consequently, TSRO can efficiently find a robust gas distribution solution with the desired level of conservativeness for integrated iron and steel plants.

Joint modelling of quantile regression for longitudinal data with information observation times and a terminal event

AbstractLongitudinal data arise frequently in biomedical follow‐up observation studies. Conditional mean regression and conditional quantile regression are two popular approaches to model longitudinal data. Many results are derived under the case where the response variables are independent of the observation times. In this article, we propose a quantile regression model for the analysis of longitudinal data, where the longitudinal responses are allowed to not only depend on the past observation history but also associate with a terminal event (e.g., death). Non‐smoothing estimating equation approaches are developed to estimate parameters, and the consistency and asymptotic normality of the proposed estimators are established. The asymptotic variance is estimated by a resampling method. A majorize‐minimize algorithm is proposed to compute the proposed estimators. Simulation studies show that the proposed estimators perform well, and an HIV‐RNA dataset is used to illustrate the proposed method.

The conditional impact of market conditions, volatility and liquidity shocks on the arbitrage opportunities duringpre‐COVIDandCOVIDperiods

AbstractThe study examines the effects of market conditions, volatility and liquidity shocks on the arbitrage profits during pre‐COVID and COVID periods. The study uses a conditional quantile regression and finds no significant difference in the impact of market conditions on the arbitrage profits during pre‐COVID and COVID crisis periods. The increase in volatility combined with low liquidity during the COVID period makes arbitrage non‐viable. However, the decline in volatility during the COVID period encourages investors to initiate arbitrage. The results are useful to fund managers and market analysts to develop suitable trading strategies and stock market regulators to take necessary steps to improve price discovery mechanisms and market efficiency.

Exploring Industry-Distress Effects on Loan Recovery: A Double Machine Learning Approach for Quantiles

In this study, we explore the effect of industry distress on recovery rates by using the unconditional quantile regression (UQR). The UQR provides better interpretative and thus policy-relevant information on the predictive effect of the target variable than the conditional quantile regression. To deal with a broad set of macroeconomic and industry variables, we use the lasso-based double selection to estimate the predictive effects of industry distress and select relevant variables. Our sample consists of 5334 debt and loan instruments in Moody’s Default and Recovery Database from 1990 to 2017. The results show that industry distress decreases recovery rates from 15.80% to 2.94% for the 15th to 55th percentile range and slightly increases the recovery rates in the lower and the upper tails. The UQR provide quantitative measurements to the loss given default during a downturn that the Basel Capital Accord requires.

Determinants of Health Management Practices’ Utilization and Its Effect on Poultry Farmers’ Income in Ondo State, Nigeria

Nigeria is the second largest poultry industry in Africa, with its poultry industry the most commercialized livestock sub-sector. Despite its significance, the farmers still experience economic losses due to disease outbreaks. Hence, the application of standard Health Management Practices (HMPs) is very important, as these practices improve the welfare of animals and increase animal production and farmers’ income. This paper examined the determinants of HMP’s utilization and its effect on poultry farmers’ income in Ondo State, Nigeria. We used multistage sampling procedures to select 120 respondents who provided data for the study. The study used Generalized Poisson Regression (GPR) and Conditional Quantile Regression (CQR) models to estimate the determinants of HMP’s utilization intensity, and its heterogeneous effects on farmers’ income, respectively. The results revealed that the HMP’s utilization intensity was significantly driven by variables such as education, training, experience, land ownership, stock size, mortality rate, and production system. In addition, HMP’s utilization had significant heterogeneous effects on farmers’ income. Therefore, the government and other developmental agencies should promote HMP’s utilization through the provision of poultry-based skills acquisition and vocational education.

Score‐based test in high‐dimensional quantile regression for longitudinal data with application to a glomerular filtration rate data

Motivated by a genome‐wide association study on the glomerular filtration rate, we develop a new robust test for longitudinal data to detect the effects of biomarkers in high‐dimensional quantile regression, in the presence of prespecified control variables. The test is based on the sum of score‐type statistics deduced from conditional quantile regression. The test statistic is constructed in a working‐independent manner, but the calibration reflects the intrinsic within‐subject correlation. Therefore, the test takes advantage of the feature of longitudinal data and provides more information than those based on only one measurement for each subject. Asymptotic properties of the proposed test statistic are established under both the null and local alternative hypotheses. Simulation studies show that the proposed test can control the family‐wise error rate well, while providing competitive power. The proposed method is applied to the motivating glomerular filtration rate data to test the overall significance of a large number of candidate single‐nucleotide polymorphisms that are possibly associated with the Type 1 diabetes, conditioning on the patients' demographics.

Is Contract Farming with Modern Distributors Partnership for Higher Returns? Analysis of Rice Farm Households in Taiwan

This study provides empirical evidence of the economic effect of contract farming for the agriculture sector dominated by smallholder farms. In light of the association between contract farming and modern food distribution channels, we categorize the adoption decisions of contract farming and modern marketing channels into four mutually exclusive choices and investigate their economic effects through the simulated maximum likelihood estimation of the multinomial treatment effects model. The results provide empirical evidence supporting higher returns from the dual partnerships as choosing modern distributors generates more revenues for the those participating in contract farming than for those with no contract farming, and contract farming is more likely to help generate more revenues for those who have taken modern distributors as their major marketing channel compared with those relying on traditional channels. Moreover, we examine whether any distributional pattern of marginal economic effects, of either contract farming or modern marketing channel, is present among farmers at various scales by using the conditional and unconditional quantile regression models. Our findings suggest that the marginal treatment effects are generally in an increasing trend as the quantile increases, implying that the economic effects of contract farming or partnership with modern distributors are more pronounced for higher returns among rice farmers in Taiwan. This finding has great policy implications for developing sustainable agriculture and food supply when facing greater uncertainties due to global warming in the future, especially in an agriculture sector with most smallholder farmers.

Risk hedging properties of infrastructure: a quantile regression approach

PurposeThis paper aims to examine the hedge, diversifier and safe haven properties of the global listed infrastructure sector and subsector indices against two traditional asset classes, stocks and bonds, and four alternative asset classes, including commodities, real estate, private equity and hedge funds during extreme negative stock market movements.Design/methodology/approachUsing dynamic conditional correlation and quantile regression, the authors analyze a data set of 12 indices comprising listed infrastructure and traditional asset classes from 2010 to 2019.FindingsOverall, the findings indicate that listed infrastructure acts as an effective diversifier but not as a strong safe haven or hedge when considered in a multiasset context. With minor exceptions, listed infrastructure cannot be concluded as a safe haven against other asset classes under investigation.Practical implicationsThe present study has implications for institutional investors looking to incorporate infrastructure in their multiasset portfolios for increased portfolio diversification benefits.Originality/valueDespite the increased influence of infrastructure as an asset class, to the best of the authors’ knowledge, this is the first study to investigate the hedge, safe haven and diversifying properties of infrastructure in a multi-asset context.

From Conditional Quantile Regression to Marginal Quantile Estimation with Applications to Missing Data and Causal Inference

It is well known that information on the conditional distribution of an outcome variable given covariates can be used to obtain an enhanced estimate of the marginal outcome distribution. This can be done easily by integrating out the marginal covariate distribution from the conditional outcome distribution. However, to date, no analogy has been established between marginal quantile and conditional quantile regression. This article provides a link between them. We propose two novel marginal quantile and marginal mean estimation approaches through conditional quantile regression when some of the outcomes are missing at random. The first of these approaches is free from the need to choose a propensity score. The second is double robust to model misspecification: it is consistent if either the conditional quantile regression model is correctly specified or the missing mechanism of outcome is correctly specified. Consistency and asymptotic normality of the two estimators are established, and the second double robust estimator achieves the semiparametric efficiency bound. Extensive simulation studies are performed to demonstrate the utility of the proposed approaches. An application to causal inference is introduced. For illustration, we apply the proposed methods to a job training program dataset.

Batch effects removal for microbiome data via conditional quantile regression

Batch effects in microbiome data arise from differential processing of specimens and can lead to spurious findings and obscure true signals. Strategies designed for genomic data to mitigate batch effects usually fail to address the zero-inflated and over-dispersed microbiome data. Most strategies tailored for microbiome data are restricted to association testing or specialized study designs, failing to allow other analytic goals or general designs. Here, we develop the Conditional Quantile Regression (ConQuR) approach to remove microbiome batch effects using a two-part quantile regression model. ConQuR is a comprehensive method that accommodates the complex distributions of microbial read counts by non-parametric modeling, and it generates batch-removed zero-inflated read counts that can be used in and benefit usual subsequent analyses. We apply ConQuR to simulated and real microbiome datasets and demonstrate its advantages in removing batch effects while preserving the signals of interest.

Response of soil moisture to vegetation and trade-off analysis in the hilly area of the Loess Plateau, China

It is said that in a synergistic relationship between vegetation and soil moisture (SM), the latter may be consumed excessively, while at the same time improving the ecological environment, such as in the large-scale artificial vegetation planting areas on the Loess Plateau. At present, the possibility of vegetation restoration and its impact on the water ecology of the Loess Plateau have received extensive attention. In this study, piecewise linear regression (PLR) and nonparametric conditional quantile regression (NCQR) models have been innovatively applied to vegetation survey data and soil moisture data from 50 field samples to explore the trade-off relationship between soil moisture and various vegetation parameters (VPs) and the response of soil moisture to the vegetation. The results showed that precipitation and topography are significantly linearly related to soil moisture in the sampling plots. Affected by irrational planting, the soil moisture benefit at 20–40 cm is significant weaker than that at 0–20 cm (plot 4, 5, 26 and 44). Some sampling plots excessively consumed soil water to promote vegetation planting. Although there was stronger soil water infiltration by shrubs, it was noted that the vegetation planting plan in some grass plots and shrub-grass plots were unreasonable and needed to be adjusted due to the clear risk of a soil water deficit determined by the trade-off values (represented by the root mean square deviation), plant species, a multiparameter evaluation. With the increase in soil moisture in the sampling plots, the negative correlation between the trade-off (soil moisture and vegetation) and soil moisture was transformed into a positive correlation (the inflection point was 8.5–10%). In addition, the benefit of soil moisture gradually changed from “shortage” to “appropriateness” and even “excess” (the inflection point was 15–20%). The fitting results of the two models are optimal and show clear applicability for evaluating trade-off relationships. Among the VPs involved in the calculation, the Shannon–Wiener index (H), Margarlef index (D1) and Fractional Vegetation Cover index (FVC) demonstrated better regional representation and stability.

Gender wage penalty in parenthood: A comparative study of South Korea and Japan

AbstractBoth South Korea and Japan attach significant importance to women′s marriage and their role in the family. Based on this fact, the present study analyzed gender wage penalty in parenthood in both the countries and compared the size and characteristics. We used conditional quantile regression models with counterfactual decomposition, and panel data from South Korea and Japan for 2007–2014. Results suggest that the gender wage penalty in parenthood of Japan was greater than in South Korea, indicating that discrimination against mother‐workers in Japan is more serious than in South Korea. Analysis by wage distribution showed that the gender wage penalty in parenthood was high in the low‐wage group in South Korea, whereas it was high for the high‐wage group in Japan. This finding indicates that South Korea needs to give fair treatment to the low‐wage group and mother‐workers of small and medium enterprises, while Japan needs to alleviate discrimination against high‐wage mother‐workers.