Quantile Regression Techniques Research Articles

Green Finance, Innovation, Agriculture Finance and Sustainable Economic Development: The Case of Indonesia’s Provincial Carbon Emissions

Indonesia is struggling with utmost pressure to restrict carbon emissions as it is included in those emerging economies who are recognized as highest carbon emitters. Nevertheless, the Indonesian government is showing remarkable effort to reduce the emissions and set a planned goal to achieve low carbon economy. However, the major reliance on fossil fuels to maintain economic growth is creating obstacles for country. To explore that what tools could be helpful in the scenario to support country’s goal. The study intends to measure the role green finance, innovation, agriculture finance and sustainable economic development in gauging carbon intensity. The paper used two indicators to measure sustainable economic development; GDP per capita and renewable energy consumption. The panel data is used in the study which is extracted from 30 provinces of Indonesia in the time span of 2006-2020. The study used OLS fixed effect model and quantile regression technique to assess the relationship between explained variable and explanatory variables. Findings echo that with the increase in economic growth, carbon intensity also starts increasing. However, green finance, innovation, agriculture finance and renewable energy are significant tools to reduce the emissions. Furthermore, confirmation of findings was done through quantile regression analysis. The paper presents several implications as well on the basis of empirical evidence which are of great help for country.

Investment in renewable energy and electricity output: Role of green finance, environmental tax, and geopolitical risk: Empirical evidence from China

The global financial downturn induced by COVID-19 has hampered the effectiveness of renewable energy developments, impeding the accomplishment of the United Nations' sustainable development targets. Green finance is a significant means for promoting renewable energy investment and achieving sustainability. Using data from 2012 to 2021 from fifty energy firms in China, this study highlights the starring part of geopolitical risk, green finance, and environmental tax in investment in renewable energy (IRE) sources. It also investigated how IRE impacts the studied firms' electricity output. The data were analyzed through quantile regression and dynamic analysis techniques. The results indicated that green financing and environmental tax significantly impact IRE sources with 0.137*** and 0.428*** beta values, respectively. However, geopolitical risk significantly impedes such projects. Similarly, IRE significantly increases the electricity output of Chinese energy firms. This research is unique in the sense of studying green financing, geopolitical risk, and environmental tax nexus in renewable energy investments leading to electricity generation, which shows a pivotal role in achieving environmental sustainability and provides valuable insights to environmentalists and policymakers to design and implement ecological strategies leading to achieving sustainable development goals.

The Evolution of Hedonic Pricing Models

This study extends the work by Sirmans et al. (2005) by providing a review of the hedonic pricing model literature published in real estate journals from 2005 through 2021. A total of 252 studies are reviewed with 150 of these being published in the top three traditional real estate journals: Journal of Real Estate Finance and Economics, Journal of Real Estate Research, and Real Estate Economics. An additional 42 papers are published in the housing-specific journal, Journal of Housing Research. A review shows that real estate research is global in both authorship and data usage, although most of the studies use U.S. data and are performed by U.S. researchers. In addition, real estate modeling is quite expansive, including spatial or geospatial autoregressive models, difference-in-difference models, quantile regression, and other advanced techniques.

Natural resources and sustainable financial development: Evidence from South Asian economies

In contemporary times, the existing literature provides a blurred image regarding the connection between natural resources and financial market development. Some studies validate the financial resource curse, while others reveal financial resource blessings. To reach a true conclusion, this study analyzes the impact of natural resources on financial market development in the presence of financial risk and technological innovation. Exploring the nexus in four South Asian economies, this study covers the period from 1990 to 2020. Due to the data's asymmetric distribution, this study uses the novel non-parametric (bootstrap quantile regression) technique. The empirical results revealed that natural resources are a curse in the development of financial markets. That is, natural resources are negatively associated with financial market development. On the other hand, financial risk and technological innovation are the substantial factors of financial market development, which positively affect financial markets throughout quantiles. The robustness of the model is also tested via parametric (robust regression) and non-parametric (panel quantile regression) approaches. Unidirectional causal impact is observed from natural resources, financial risk, technological innovation, and financial market development. Based on the empirical findings, this study directed the channelization of natural resources, reducing financial risk and enhancing investment in research, development, and human capital.

FORECASTING THE STATE OF TELECOMMUNICATION NETWORKS USING QUANTILE AND LOGISTIC REGRESSION METHODS

In today's modern world, the ubiquity of information technology has intertwined telecommunications systems with every facet of human life. It's challenging to fathom a world where you're disconnected from the "World Wide Web" or unable to exchange data instantly via the intricate web of modern mobile devices. The vitality of staying connected online cannot be overstated, and ensuring the smooth functioning of telecommunications systems is paramount. This paper delves into the pivotal task of predicting and managing the performance of these networks, employing quantile and logical regression techniques. Our study leverages real-world data from telecommunication network operations to construct a predictive model capable of anticipating network conditions in advance. This predictive capability serves as the linchpin for intelligent decision-making systems, facilitating real-time network management. By implementing machine learning methods, specifically quantile regression, we achieve a sophisticated understanding of how various factors influence network performance. Our research doesn't just stop at forecasting; it extends to the realms of intellectual decision-making systems, where the insights gained from regression analysis play a pivotal role. These intelligent systems are equipped to make data-driven decisions on network resource allocation, maintenance schedules, and preemptive problem resolution. In essence, they act as the custodians of network stability, ensuring that telecommunication systems remain robust and responsive to the ever-evolving demands of modern society. This article sheds light on the indispensable role of regression methods in proactively managing the state of telecommunications networks. By harnessing the power of machine learning and data-driven insights, we pave the way for a future where network disruptions are minimized, and the seamless connectivity we've come to rely on remains a constant presence in our lives.

Moderation analysis of exchange rate, tourism and economic growth in Asia.

This study brings novelty to the tourism literature by re-examining the role of exchange rate in the tourism-growth nexus. It differs from previous tourism-led growth narrative to probe whether tourism exerts a positive effect on economic growth when the exchange rate is accounted for. Using a moderation modelling framework, instrumental variables general method of moments (IV-GMM) and quantile regression techniques in addition to real per capita GDP, tourism receipts and exchange rate, the study engages data on 44 Asian countries from 2010 to 2019. Results from the IV-GMM show that: (1) tourism exerts a positive effect on growth; (2) exchange rate depreciation hampers growth; (3) the interaction effect is positive but statistically not significant; and (4) results from EAP and SA samples are mixed. For the most part, constructive evidence from the quantile regression techniques reveals that the impact of tourism and exchange is significant at lower quantiles of 0.25 and 0.50 while the interaction effect is negative and statistically significant only for the SA sample. These are new contributions to the literature and policy recommendations are discussed.

The Impact of Government Effectiveness on Trade and Financial Openness: The Generalized Quantile Panel Regression Approach

Purpose: This paper aims to investigate the impact of government effectiveness on trade and financial openness in 35 selected countries around the globe. Design/methodology/approach: A quantitative research approach was applied in the study using the generalized quantile panel regression approach to analyze the impact of identified variables in these selected countries. Panel quantile models with high estimation performance are preferred in the presence of excessive deviations and in cases where the normal distribution is invalid. Findings/results: The empirical findings indicate that selected countries with above-average governmental effectiveness, that is, with a well-established state bureaucracy and a historically strong state tradition, will further increase their activities toward international integration through financial and trade openness. Practical implications: This study aims to provide valuable information that governments and regulatory authorities can benefit from in their decision-making processes. Originality/value: In this study, it is preferred to use the trade openness of countries as the share of exports in total world exports and financial openness as the ratio of capital flows to world flows. In this way, these variables will provide new information to analyze the influence of government effectiveness. Implementing the generalized quantile panel regression technique can also be expressed as an innovation in this field of literature.

Moderation of Services' EKC through Transportation Competitiveness: PQR Model in Global Prospective.

The continuously increasing GHG emissions have created environmental pollution and several challenges to ecosystems and biodiversity. The challenges of climate change are multipronged, resulting in melting glaciers, flash floods, and severe heat waves. In this regard, the adaptive and mitigation strategies to manage the consequences of climate change are highly important. The transport sector creates a quarter of carbon emissions, and this share is continuously increasing. Accordingly, this research study uses transport competitiveness to determine carbon emissions of the transport sector for 121 countries covering the time period from 2008 to 2018. The Panel Quantile Regression (PQR) technique is engaged to analyze the study results. The findings highlight that transport competitiveness tends to increase carbon emissions of the transport sector across quantile groups 1 and 3, while it reduces carbon emissions in quantile group 2. The U-shaped services' EKC is validated in quantile groups 2 and 4. The moderation engaged, i.e., transportation competitiveness, changes the turning point of the services' EKC across quantile groups 2 and 4. However, in the high-CO2 quantile group, the moderation impact of transport competitiveness is strongest as it reduces the sensitivity by flattening the services' EKC. Furthermore, the planned expansion of the population and improved institutional quality tend to mitigate carbon emissions across different quantile groups. The policy relevance/implications that are based on the study results/findings are made part of the research paper.

Leading pedestrian intervals – Yay or Nay? A Before-After evaluation of multiple conflict types using an enhanced Non-Stationary framework integrating quantile regression into Bayesian hierarchical extreme value analysis

A pedestrian was estimated to be killed every 85 min and injured every 7 min on US roads in 2019. Targeted safety treatments are particularly required at urban intersections where pedestrians regularly conflict with turning vehicles. Leading Pedestrian Intervals (LPIs) are an innovative, low-cost treatment where the pedestrian and vehicle usage of the potential conflict area (a crosswalk) is staggered in time to give the pedestrians a head start of a few seconds and reduce the “element of surprise” for right-turning vehicles. The effectiveness of LPI treatment on pedestrian safety is mixed, and most importantly, its effect on vehicle-vehicle conflicts is unknown. This study investigates the before-after effects of LPI treatments on vehicle–pedestrian and vehicle-vehicle crash risk by applying traffic conflict techniques. In particular, this study has developed a quantile regression technique within the extreme value model to estimate and compare crash risks before and after the installation of the LPI treatment. The before-after traffic movement video data (504 h in total) were collected from three signalized intersections in the City of Bellevue, Washington. The recorded movements were analyzed using Microsoft’s proprietary computer vision platform, Edge Video Service, and Advanced Mobility Analytics Group’s cloud-based SMART SafetyTM platform to automatedly extract traffic conflicts by analyzing road user trajectories. The treatment effect was measured using a Bayesian hierarchical extreme value model with the peak-over threshold approach. For the extreme value model, a Bayesian quantile regression analysis was conducted to estimate the conflict thresholds corresponding to a high (95th) quantile. Odds ratios were estimated for both conflict types using untreated crossing as a control group. Results indicate that the LPI treatment reduces the crash risk of pedestrians as measured by the reduction in extreme vehicle–pedestrian conflicts by about 42%. The LPI treatment has also been found not to negatively affect rear-end conflicts along the approaches leading to the LPI-treated pedestrian crossing at the signalized intersections. The findings of this study further emphasize the effectiveness of video analytics in proactive safety evaluations of engineering treatments.

Role of sustainable finance, geopolitical risk and economic growth in renewable energy investment: Empirical evidence from China

The global economic recession caused by COVID‐19 has posed a severe threat to the feasibility of renewable energy projects, hampering the United Nation (UN) sustainable development goals. Sustainable financing (SF) is a crucial instrument for promoting investment in renewable energy (IRE) sources, as it is regarded as a crucial aspect in achieving long‐term sustainability. This study sheds insight on the impact of SF, geopolitical risk (GPR), economic growth (EG), and environmental regulation (ER) on IRE sources by evaluating 10 years of data from 35 Chinese energy businesses from 2012 to 2021. The data analysis is done by utilizing quantile regression and dynamic analytic techniques, demonstrating that SF, EG, and ER have a significant positive effect on IRE sources. However, GPR has a significant detrimental impact on IRE in China. This is one of the early studies to examine the crucial role of SF, GPR, EG, and ER in IRE, which is critical for environmental sustainability. In addition, it provides policymakers and environmentalists with crucial insights for developing and executing environmental strategies that can deliver long‐term benefits and meet SD goals.

Geopolitical risks and mineral-driven renewable energy generation in China: A decomposed analysis

Minerals are critical in manufacturing and handling renewable energy technologies, including solar photovoltaics (PV), wind turbines, electric vehicles (EVs), and power storage batteries. However, mineral mining and the supply-related risky situations stunt the mineral import flows of the renewable energy-producing countries. Given this, we scrutinize the role of the decomposed measures of geopolitical risks in the mineral import demand and this demand-driven renewable energy generation in China, covering monthly data from 1996 to 2020. We employ the quantile autoregressive distributed lag (QARDL) technique for measuring the co-integrating association among the variables across different quantiles. We find a significant effect of the decomposed measures of the geopolitical risks on the mineral import and mineral import-augmented renewable energy generation mainly at the extreme quantiles under long memory. In particular, geopolitical risks ‘threats’ and ‘narrow’ parameters negatively affect where the geopolitical risks ‘acts’ and ‘broad’ measures positively influence mineral import demand and this demand-driven renewable energy generation in China. Furthermore, we legitimize our findings across the quantile on quantile (QQ) regression technique. Moreover, our investigation outcome is beneficial for elevating mineral-driven renewable energy generation by preventing geopolitical risks in the context of China.

How G-7 countries are paving the way for net-zero emissions through energy efficient ecosystem?

Given the relevance of the most burning issues across the globe, it is time to take action regarding the recovery of environmental issues such as global warming and climate change. Concerning inconsistencies and gaps in the prevailing literature, this study aims to investigate the role energy efficiency played in the energy related greenhouse gas emissions. With the advancement in the technological innovation, the importance of digitalization and environmental related technological innovation. Based on the economic importance of the Group of Seven (G7) economies, this study examined the connection of these variables in the mentioned group economies throughout 1990–2020. Employing advanced panel econometric approaches, the study found that prevalence of slope heterogeneity, cross-section dependence, and the existence of long-run equilibrium relationship. Due to non-linear data properties, the study uses non-parametric (method of moment quantile regression) technique. The results suggest the progressive role of energy efficiency, digitalization, and environmental related innovations on the environmental sustainability. These are substantial factors of sustainability as they significantly reduce the energy-related greenhouse gas emissions. On the contrary, economic growth is a significant driver of energy related greenhouse gas emissions. The results are robust and their causal linkage is also explored. This study suggests the increased investment and encouragement of energy efficiency, digitalization, and environmental related technological innovation in the G7 economies to pave the way for net zero emissions.

Analyzing the Effects of Renewable and Nonrenewable Energy Usage and Technological Innovation on Environmental Sustainability: Evidence from QUAD Economies

The following study examined how energy use and technological advancement impacted environmental sustainability in QUAD (US, Japan, Australia, and India) economies between 1991 and 2021. The study considers the generation of renewable energy, fossil fuel use, and the effects of economic expansion on environmental sustainability. The research used the moment quantile regression technique based on the outcomes of slope heterogeneity, cross-sectional dependence, and the order of the unit-root by the using second-generation method of cross-sectional augmented Im, Pesaran, and Shin tests. The study discovered that renewable energy production and technological innovation enhances environmental sustainability, whereas the use of nonrenewable energy and economic growth worsen it. When implementing policies regarding the environment, energy, and the growth of QUAD economies based on concrete evidence, policy makers and environmentalists in QUAD countries should also take into account the asymmetrical performance of efficiency in energy production, technological innovation, and economic growth.

Comparison between Quantile Regression Technique and Generalised Additive Model for Regional Flood Frequency Analysis: A Case Study for Victoria, Australia

For design flood estimation in ungauged catchments, Regional Flood Frequency Analysis (RFFA) is commonly used. Most of the RFFA methods are primarily based on linear modelling approaches, which do not account for the inherent nonlinearity of rainfall-runoff processes. Using data from 114 catchments in Victoria, Australia, this study employs the Generalised Additive Model (GAM) in RFFA and compares the results with linear method known as Quantile Regression Technique (QRT). The GAM model performance is found to be better for smaller return periods (i.e., 2, 5 and 10 years) with a median relative error ranging 16–41%. For higher return periods (i.e., 20, 50 and 100 years), log-log linear regression model (QRT) outperforms the GAM model with a median relative error ranging 31–59%.

Carry Trade and Capital Market Returns in South Africa

This paper assesses the extent to which carry trade operations affect the performance of equity and bond markets in a target country, South Africa, by considering the US and the euro area as the funding countries. A two- and three-factor capital asset pricing model (CAPM) is employed to assess whether the pricing of equity and bond markets in South Africa depends on the US dollar/rand and euro/rand carry trade returns. Moreover, the paper uses the quantile regression technique to assess whether this pricing varies with the distribution of the equity and bond returns. The findings support that the US- and euro-funded carry trade are essential factors for the pricing of equity and bond markets in South Africa. Moreover, the results of the two-factor model show a negative relationship between the equity excess return and the US-carry trade returns at lower quantiles of the equity market returns. The positive relationship is observed in the upper quantiles of the equity market. The negative relationship means that carry trade activities reduce equity market returns during a bear market as investors close out their position when conditions in the equity market become unfavourable. The results of the three-factor model, controlling for the global volatility or uncertainty, show that carry trade investors exit the equity market to invest in the bond market when global uncertainty rises. This finding shows that carry trade investors choose less risky assets during rising global uncertainty.

The role of technology innovation, R&D, and quality governance in pollution mitigation for EU economies: fresh evidence from method of moment quantile regression

ABSTRACT Human progress has become detrimental to the environment. Moreover, the failure to mitigate pollution would eventually impact negatively on the economy. This study investigates the influence of technological innovation, research and development, and quality of governance on carbon emissions for the European Union countries while controlling for industrialization, renewable energy, trade openness and foreign direct investment from 1996 to 2018 by employing the method of moment quantile regression technique. The result revealed that renewable energy, trade openness, and quality governance boost environmental quality, while technological innovation and industrialization increase carbon emissions. Also, the interaction between technological innovation and quality governance, research and development and quality governance as well as industrialization and quality governance decreases carbon emission while that of foreign direct investment and quality governance increases carbon emission. From the causality analysis, there is a bidirectional relationship between research and development and carbon emission, technology innovation and carbon emission, renewable energy and carbon emission, trade openness and carbon emission as well as industrialization and carbon emission. Moreover, a unidirectional connection was obtained between foreign direct investment and carbon emission, as well as quality governance and carbon emission. Based on the outcomes, comprehensive policy directions are suggested for policymakers in these countries.

Eğirdir yöresi doğal karaçam meşcereleri için çap-boy modeli: Kantil regresyon yaklaşımı

Black pine (Pinus nigra JF Arnold.) is one of the most economically and ecologically important tree species in Turkey. In this context, reliable and accurate information about the current status, growth and yield characteristics of these forests is needed for the sustainable management of black pine forests. One of the most important components of growth and yield models is the height-diameter relationships. Diameter and height variables are also the most important measurement tools in forest inventory studies. Since the height of a standing tree cannot be measured as easily as the breast height diameter, tree height is often estimated as a function of diameter in forest inventory studies. For this reason, accurate and reliable estimation of tree height has a great importance for forestry activity. In this study, a height-diameter model was developed for natural black pine stands in Eğirdir region using Quantile Regression (QR) techniques. The measured data were randomly divided into two equal groups. Chapman-Richards height-diameter model was chosen as the base model for both methods since this model has been provided successful results in previous studies. QR models are developed based on three quantile (3, 5 and 9 quantiles) sets in this study. The most important advantage of QR approach over other estimation methods is that QR allows the calibration of the model with extra sample tree heights. For this purpose, different calibration alternatives were tested using a number of trees ranging from 1 to 10 in each sample plot. As a result of the study, it was seen that the 3QR approach performed better than both 5QR and 9QR approaches in terms of describing the height-diameter relationships. In addition, it has been determined that the calibration with five sample trees in each sample plot is appropriate in terms of both the estimation precision of the models and the sampling costs

Predicting future failure times by using quantile regression

The purpose of the paper is to study how to predict the future failure times in a sample from the early failures (type II censored data). We consider both the case of independent and dependent lifetimes. In both cases we assume identically distributed random variables. To predict the future failures we use quantile regression techniques that also provide prediction regions for them. Some illustrative examples show how to apply the theoretical results to simulated and real data sets.

Towards green recovery: Can banks achieve financial sustainability through income diversification in ASEAN countries?

Establishing sustainable and balanced development for green financing is critical for improving financial sustainability and banks’ capability. Banks struggle to achieve economic sustainability in the current highly competitive business environment. This research examines the impact of income diversification on financial sustainability proxy by return on assets (ROA) by applying the quantile regression technique to the data from banks of ASEAN countries over the period 2008–2019. In addition, liquidity risk, bank size, interest and non-interest incomes, and market capitalization are studied as control variables. The empirical findings indicate that income diversification positively impacts return on assets at all countries’ lower, middle, and upper quantiles, even though sizes can differ across countries and quantiles. Moreover, market capitalization, non-interest income, and banks’ size favorably impact banks’ performance. In contrast, liquidity risk and interest incomes are negatively linked to the performance of banks for all countries at each quantile. These results have significant strategic implications for managers, regulators, and policymakers who share a common interest in boosting financial sustainability and performance and significantly shaping green recovery.

The Effects of the Volatility Changes in Exchange Rates and Interest Rates from Historical Shocks on the Non-Performing Loans in Turkish Economy

Historical shocks (Lehman Brothers bankruptcy (15 September 2008), the FED?s tapering decision (18 December 2013), the CBRT?s interest rate decision (28 January 2013)) had significant impact on global and domestic financial markets, especially nonperforming loans (NPLs). In this context, main aim of this study is to analyze how volatility changes in exchange rates and interest rates triggered by these historical shocks affect consumer and industrial nonperforming loans in Turkish economy by using quantile regression techniques. The results showed that shocks that cause volatility changes in foreign exchange rates and interest rates have important effects on NPLs in the Turkish banking industry.