Quantile Autoregression Research Articles

Bubbles and crashes: A tale of quantiles

Periodically collapsing bubbles, if they exist, induce asymmetric dynamics in asset prices. In this article, I show that unit root quantile autoregressive models can approximate such dynamics by allowing the largest autoregressive root to take values below unity at low quantiles, which correspond to price crashes, and above unity at upper quantiles, that correspond to bubble expansions. On this basis, I employ two unit root tests based on quantile autoregressions to detect bubbles. Monte Carlo simulations suggest that the two tests have good size and power properties, and can outperform recursive least‐squares‐based tests. The merits of the two tests are further illustrated in three empirical applications that examine Bitcoin, US equity and US housing markets. In the empirical applications, special attention is given to the issue of controlling for economic fundamentals. The estimation results indicate the presence of asymmetric dynamics that closely match those of the simulated bubble processes.

Election and Wallet: The Effect of Election Periods and Consumer Expectations on Consumer Confidence with ARDL and QARDL Model Approach

The growing significance of consumer confidence and the explanation of the factors influencing this confidence is an important issue worthy of study. This study aims to examine the effects of election periods and consumer expectations on the Consumer Confidence Index (CCI) in Turkey. To this end, the Autoregressive Distributed Lag (ARDL) and Quantile Autoregressive Distributed Lag (QARDL) models will be employed. Data on the variables in question were collected on a monthly basis from Turkish Statistical Institute (TUIK) over the period spanning 2012:1 to 2024:3. The ARDL model findings indicate that election periods exert a positive and significant influence on the TGE, both in the short run and in the long run. The intention to buy or have a house built and the intention to save have positive and significant effects on the TGE in both the short and long run. Conversely, the intention to buy a car and the cost of living index have a positive and significant effects on the TGE in the short run, but no significant effect was found in the long run. The QARDL model reveals that asymmetry is present, with all variables exhibiting disparate effects across different quartiles. The findings of this study indicate that in order to enhance consumer confidence and guarantee economic stability, it is essential to prioritise the housing market, savings patterns and reducing the cost of living. The development of policies that ensure the sustainability of the confidence and stability created during election periods is crucial. The research offers invaluable insights into ARDL and QARDL models that can inform Turkey's economic policies.

Exploring the impact of industrialization and electricity use on carbon emissions: The role of green FinTech in Asian countries using an asymmetric panel quantile ARDL approach

PurposeThe purpose of this study is to investigate how industrialization, financial development, electricity consumption, trade openness, and green FinTech affect on carbon emissions asymmetrically in Asian countries. DesignMethod/Approach: The study examined 29 years of panel data from 39 Asian countries from 1995 to 2022, sourced from World Development Indicators (WDI) and the International Monetary Fund (IMF). The study constructs a green financial technology index using principal component analysis (PCA). The study utilizes an Asymmetric Panel Quantile Autoregressive Distributive Lag (A-QARDL) model with pooled mean group (PMG) specifications to explore effects that exhibit cross-sectional homogeneous in the long-run, but heterogeneous in the short-run effects. FindingsIndustrialization and financial development have a strongly asymmetric impact on carbon emissions. Industrialization causes an increase in carbon emissions at various quantiles, while green FinTech plays a crucial role in mitigating these carbon emissions. Trade openness and domestic credit to the private sector also help reduce carbon emissions. Research limitations and implicationsThe study emphasizes the significance of employing green FinTech techniques and using renewable energy sources to meet sustainable industrialization and sustainability goals in Asian countries. The policy consequences include promoting environmentally friendly industrial practices, encouraging green financial investments, and boosting government financing for private sector research and development to mitigate carbon emissions. Originality/valueThe study employs robust modeling to analyze the role of green FinTech to enhance industrial sustainability. Both Industrialization and deindustrialization have an impact on economic emissions, and the potential of green FinTech's to promote sustainability contributes to the environment protection strategy.

How to get green with agricultural footprint: A global analysis of carbon emissions, environmental taxes, and agrochemical use

The agriculture sector, being highly vulnerable to climate change, plays a pivotal role in achieving green and sustainable economies. As the 2030 Agenda emphasizes the integrated development of environmental, social, and economic dimensions, it is crucial to understand how agricultural practices affect sustainability. This study investigates the environmental impact of the agricultural sector on the green economy by examining key environmental factors, such as agricultural energy emissions, environmental taxes, irrigated water use efficiency, fertilizer use, and pesticide application. The analysis is conducted using the Panel Quantile Autoregressive Distributed Lag (QARDL) model, with robustness checks performed through the comparison with the Panel ARDL model, using data from 2000 to 2020. To assess the green economy, an index is constructed based on 20 indicators related to environmental, social, and economic sustainability. The results show that agricultural energy emissions, despite their environmental cost, are positively associated with green economy development, highlighting the trade-off between high productivity and environmental degradation in a resource-efficient economy. Improved water use efficiency is found to enhance agricultural sustainability and production. Additionally, environmental taxes and regulations exert a significant positive effect on both environmental preservation and social welfare. Contrary to the belief that reducing agricultural production will lower carbon emissions, the study suggests that adopting environmentally friendly practices and imposing targeted taxes are more effective strategies. The findings also reveal a negative relationship between excessive fertilizer use and the green economy, indicating the need for minimal fertilizer application to promote sustainability. The study concludes with specific policy recommendations. Governments, particularly in countries that have not yet adopted sustainable agricultural practices, should implement environmental taxes and regulations to promote carbon neutrality and support the Sustainable Development Goals. These measures are essential to balancing economic growth, environmental sustainability, and social equity in the agricultural sector.

Food security in Nigeria amidst globalization, economic expansion, and population growth: A wavelet coherence and QARDL analysis

Food security is crucial on a global scale as it provides essential health, social, and economic advantages to the human population. Therefore, to achieve the sustainable development goal of zero hunger (Goal 2), it is imperative to assess the progress of food security, particularly in developing countries. Thus, this reflects on the relative factors affecting food security in Nigeria from 1980 to 2022. The study utilized advanced econometrics approaches such as Quantile Autoregressive Distributed Lag (QARDL) and Wavelet Coherence (WTC) to analyze the data. Food security is measured using the food production index (FPI), while other variables such as globalization (GLOB), population growth (POP), gross domestic product (GDP) as a measure of economic growth, and inflation are considered. The results of the Quantile Autoregressive Distributed Lag analysis indicate that in the short-term, globalization has a beneficial impact on FPI at the 0.25 quantile, but it has a detrimental effect on food security at the 0.50 and 0.75 quantiles. Nevertheless, over a prolonged period, the effect is detrimental in all quantiles. Additionally, in the short-term, FPI experiences a beneficial effect from GDP in all quantiles. Over time, GDP has a positive effect on all levels of income distribution. However, in the short-term, the size of the POP has a negative effect on FPI for the 0.25 to 0.75 quantiles. Further discussion is dedicated to policy options and their implications.

Endogenous Growth and Environmental Kuznets Curve: Lessons from FDI Impact on Economic Growth in Sub-Saharan Africa

Purpose: This study aims to determine the influence of Foreign Direct Investments (FDI) on economic growth in Sub-Saharan Africa (SSA). It examines the endogenous growth theory and the Environmental Kuznets Curve (EKC) theory, and how they relate to the regional data.Method: Using panel quantile autoregression models, this study explores the relationship between FDI inflows into SSA with energy consumption, carbon emissions, and economic growth. The study is based on data from 1975 to 2018.Result: The study findings conclusively demonstrate that foreign direct investment has a significant impact on the economic growth of the SSA region. Furthermore, the study reveals that energy consumption and carbon emissions in the SSA have consistently increased throughout the study period, with foreign direct investment being identified as the primary driver of this trend. These findings are consistent with the Environmental Kuznets Curve (EKC) hypothesis, as well as the endogenous growth theory, which suggests that FDI operations can have negative consequences on the host environment.Practical Implications for Economic Growth and Development: The study suggests that Sub-Saharan Africa should manage FDI carefully to balance economic growth with environmental sustainability by promoting green investments and creating an investment-friendly environment.

Does green productivity promote agricultural productivity in Vietnamese market?

PurposeThe research aims to assess the relationship between ecological innovation, green productivity, sustainable development and agricultural productivity in Vietnam. The agricultural sector of Vietnam has been observed with new opportunities which have fostered its productivity and growth.Design/methodology/approachThe study uses a range of methods where initially, the researcher used descriptive analysis, cointegration and unit root tests. Secondly, Quantile Autoregressive Distributed Lag (QARDL) is used to assess the short and long run effects. The QARDL methodology is employed to capture the relationship between variables. Through this approach, the researcher is able to examine the scale of the interaction between dependent and independent variables.FindingsThe unique findings drawn through statistical techniques are also a great addition to the context of literature related to Vietnam’s agricultural productivity. Practical insights can also not be denied as the study provides beneficial guidelines for Vietnam’s agricultural sector to refine agricultural productivity.Research limitations/implicationsScholars are advised to use strong literary techniques to overcome these limitations and give a more thorough investigation into the same ideas. The availability and dependability of data was one of the primary challenges in carrying out this study. Vietnam has made significant advances in the collection and documentation of agricultural data, but there might still be gaps in the availability of thorough and current data on ecological innovation, green production and sustainable development.Originality/valueVietnam’s unique socioeconomic, cultural and environmental features influence how ecological innovation, green productivity, sustainable development and agricultural production are interconnected. Consequently, consideration should be taken when applying the results to various scenarios.

Development of Functional Quantile Autoregressive Model for River Flow Curve Forecasting

AbstractAmong several hydrological processes, river flow is an essential parameter that is vital for different water resources engineering activities. Although several methodologies have been adopted over the literature for modeling river flow, the limitation still exists in modeling the river flow time series curve. In this research, a functional quantile autoregressive of order one model was developed to characterize the entire conditional distribution of the river flow time series curve. Based on the functional principal component analysis, the regression parameter function was estimated using a multivariate quantile regression framework. For this purpose, hourly scale river flow collected from three rivers in Australia (Mary River, Lockyer Valley, and Albert River) were used to evaluate the finite‐sample performance of the proposed methodology. A series of Monte‐Carlo experiments and historical data sets were examined at three stations. Further, uncertainty analysis was adopted for the methodology evaluation. Compared with the existing methods, the proposed model provides more robust forecasts for outlying observations, non‐Gaussian and heavy‐tailed error distribution, and heteroskedasticity. Also, the proposed model has the merit of predicting the intervals of future realizations of river flow time series at the central and non‐central locations. The results confirmed the potential for predicting the river flow time series curve with a high level of accuracy in comparison with the benchmark existing functional time series methods.

A novel green learning artificial intelligence model for regional electrical load prediction

Load prediction is crucial to unit commitment and scheduling in electricity systems; however, load prediction models consume considerable electricity. Most conventional prediction methods are based on deep learning (DL) models, such as long short-term memory and gated recurrent units. The nonlinear activation functions and backpropagation mechanisms used in these methods result in high computational complexity and correspondingly high energy consumption. This paper proposes a prediction model based on a green learning (GL) framework aimed at reducing energy consumption. The proposed GL model replaces the activation functions conventionally used for feature extraction with a hybrid scheme combining categorical and numerical features, such as seasonal climate features and multi-autoregression terms. The proposed GL model also eliminates the backpropagation methods used to optimize hyperparameters by employing seasonal centroids and the quantile auto-regression forest algorithm for classification/regression. In a case study, the proposed GL model significantly outperformed conventional DL models in terms of energy consumption without compromising accuracy. The energy savings of the proposed GL model are equivalent to the annual electricity consumption of 211 ∼ 565 households, increasing with model penetration. This paper demonstrates the importance of energy-saving models in mitigating the linkage effect of industrial carbon emissions. It also explores policy implications for the implementation of GL models.

Forecasting crude oil volatility and stock volatility: New evidence from the quantile autoregressive model

This paper employs the quantile autoregressive (QAR) model to examine the forecasting relationship between stock volatility and crude oil volatility. We first utilize the sup-Wald test to evaluate Granger causality across various quantile levels, providing valuable information for forecasting. Our findings reveal that the causal effects between stock volatility and crude oil volatility differ considerably across different quantiles, with a V-shaped relationship evident at the quantile level. Results from out-of-sample forecasts indicate that the forecasting effect of oil volatility on stock volatility has both positive and negative impacts. In contrast, when using stock volatility to forecast crude oil volatility, predictability improves relative to the benchmark, particularly at more extreme quantiles. Further analysis highlights the necessity of forecast combinations to achieve an overall improvement in forecasting tasks.

Tehran Stock Market efficiency: A quantile autoregression approach

The purpose of this paper is to evaluate the price efficiency of the Tehran Stock Market. For this aim, we used daily stock prices of 30 large companies on the Stock Exchange. In the first stage, a unit root test with the endogenous break and without a structural break was performed using augmented dickey-fuller test (ADF) tests and Phillips-perron (PP) tests. The results indicate that the price of 9 companies has a random walk process with intercept and 21 companies follow a random walk without intercept and trend component process which is known as the pure random walk process. Thus, considering the ADF and PP tests, most companies’ stock prices are efficient. Quantile autoregression results in the second stage show that the stock prices in the middle price deciles have weak efficiency, but in the lower and upper price deciles, the stock price does not follow the weak efficiency conditions. So, if the stock price deviates (up or down) from the long-term mean, the market becomes inefficient, but when the stock price is at the median level, the market is efficient. The general conclusion is that median prices are the long-term average prices that change over time, and stock prices tend to move toward that price.

Interrelations between bitcoin market sentiment, crude oil, gold, and the stock market with bitcoin prices: Vision from the hedging market

PurposeThe purpose of this study is to examine Bitcoin's price behavior across market conditions, focusing on the influence of Bitcoin's historical prices, news sentiment and market indicators like oil prices, gold and the S&P index. The authors also assess the stability of Bitcoin-inclusive hedging portfolios under different market conditions, for example, bearish, bullish and moderate market states.Design/methodology/approachThis study uses the Quantile Autoregressive Distributed Lag model to explore the effects of different factors on Bitcoin's prices across various market situations. This method allows for a detailed analysis of historical trends, investor expectations and external market influences on Bitcoin's price movements and systematic stability.FindingsKey findings reveal historical prices and investor expectations significantly influence Bitcoin in all market scenarios, with news sentiment exhibiting substantial volatility. This study indicates that oil prices have minimal impacts on Bitcoin, whereas gold is a stabilizing asset in bear markets, with the S&P index influencing short-term fluctuations. At the same time, Bitcoin's volatility varies with market conditions, proving more efficient as a hedging tool in bear and stable markets than in bull ones.Originality/valueThis study highlights the intrinsic correlation between Bitcoin's prices, news sentiment and financial market indicators, enhancing understanding of Bitcoin's market dynamics. The authors demonstrate Bitcoin's weak direct correlation with commodities like oil, the stabilizing role of gold in crypto portfolios and the stock market's indirect effect on Bitcoin prices. By examining these factors' impacts across various market conditions, the findings offer strategies for investors to improve hedging and portfolio management in cryptocurrency markets.

Bayesian quantile inference and order shrinkage for hysteretic quantile autoregressive models

Hysteretic quantile autoregressive model combines the hysteretic patterns and quantile autoregression, which can capture the dynamic relationship and nonlinear characteristics at different quantiles in time series data. In this paper, the Bayesian quantile inference and order shrinkage are studied for a class of hysteretic quantile autoregressive time series models. By using Markov Chain Monte Carlo (MCMC) techniques, the proposed Bayesian quantile method can handle the sparse hysteretic quantile autoregressive model well. It can accurately determine order of the model and estimate non-zero coefficients. Both simulation studies and a data example show that the proposed methods are feasible, reliable and appropriate for analysing the US Gross National Product data set.

Analyzing the impact of fiscal conditions on private health expenditures in OECD countries: a quantile ARDL investigation

Organization for Economic Co-operation and Development (OECD) countries have embraced the aim of universal health coverage, as established in Sustainable Development Goal (SDG) 3.8. This goal guarantees access to quality healthcare services without financial hardship or poverty. Additionally, it requires correct and adequate financing sources. A country with weak protection for its population tends to spend less on healthcare and experiences a high share of out-of-pocket payments (OOPs), increasing the likelihood of people falling into poverty. This study aims to understand the relationship and causal effects between macroeconomic and public fiscal conditions and private health expenditure in OECD countries between 1995 and 2019. We retrieved OECD data for 26 OECD countries for the period 1995–2019. Panel AutoRegressive Distributed Lag (PARDL) and panel quantile AutoRegressive Distributed Lag (PQARDL) models were estimated to examine the relationship between private health expenditures and macroeconomic and public fiscal variables. Our results reveal a positive influence of government debt and economic freedom on private health expenditures. They also show a negative influence of the government budget balance, government health expenditures, and economic growth on private health expenditures. These results collectively suggest that public fiscal conditions will likely impact private health expenditures. The findings of this study raise concerns about the equity and financial protection objectives of universal health coverage in OECD countries.

The asymmetric effect of G7 stock market volatility on predicting oil price volatility: Evidence from quantile autoregression model

This paper investigates the asymmetric effect of G7 stock market volatility on predicting oil price volatility under different oil market conditions by using the quantile autoregression model. Both in- and out-of-sample results demonstrate the prediction superiority and effectiveness of the quantile autoregression model. The US and Canada's stock markets exhibit the strongest predictive ability across the entire distribution, while the UK demonstrates strong predictive power specifically during periods of high oil price volatility. Japan, Germany, France, and Italy as oil importers can predict low and median oil volatility. The strong predictability of G7 stock volatility may be attributable to their significant impact on the business cycle and investor sentiment. This asymmetric prediction ability arises not only from the average volatility shocks at various quantiles but also from the bad and good stock volatility at different quantiles. Further research suggests that bad stock volatility appears to be more predictable than good stock volatility, especially in high oil price fluctuations. Furthermore, the superiority and effectiveness of the quantile autoregression model in predicting oil volatility are proven to be applicable to emerging markets. This study may provide useful insights for policymakers, businesses, and investors to improve crude oil risk prediction and risk management under different market conditions.

Study on agricultural water resource utilization efficiency under the constraint of carbon emission and water pollution

Agricultural water resource utilization efficiency in China is facing significant challenges due to the dual constraints of carbon emissions and water pollution. The inefficiency in water usage in agriculture not only impacts the sustainability of water resources but also contributes to environmental degradation through increased carbon emissions and water pollution. Agricultural water resource utilization efficiency under the constraint of carbon emission and water pollution has been a critical issue in China from 2005 to 2022. This study employs the Quantile Autoregressive Distributed Lag (QARDL) method to comprehensively assess and analyze the complex relationship that exists between agricultural water usage, carbon emissions, and water pollution. By analyzing distinct quantiles of the data distribution, the research investigates how different levels of water resource utilization efficiency relate to carbon emissions and water pollution under various conditions. The findings reveal nuanced insights into the dynamic interactions among these components within the agricultural sector. This research project focuses on the efficiency of water resource utilization in agriculture while considering the constraints of carbon emission and water pollution. Given the dynamic and time-dependent character of these components, the QARDL methodology makes it possible to get a detailed knowledge of how they interact within the framework of agriculture. The study aims to give significant insights and policy suggestions to improve agricultural practices while minimizing environmental concerns linked to carbon emissions and water pollution.

Transition towards natural resource rents and green technology to achieve China's COP26 success: A novel insights in the case of trade openness and environmental pollution

This study aimed to investigate the impact of various factors such as natural resource rents, trade openness, green technology innovation, oil prices, and economic growth on environmental pollution in China by covering the period from 1970 to 2021, aligning with the COP26 agenda. These factors included. To accomplish this, we utilized an advanced Quantile Autoregressive Distributed Lag model to scrutinize the short- and long-term quantile patterns for both the dependent and independent variables. The findings reveal that green technologies and natural resource rents play a negative role in reducing emissions, whereas economic growth and trade openness have positive effects on emissions levels in China in the short and long run during different quantile patterns. Additionally, we employed the Wald test to confirm the presence of asymmetric patterns in environmental pollution concerning economic growth, natural resource rents, green technological innovation, and trade openness in both the short and long terms. These results emphasize the significance of prioritizing key policy measures to align with the COP26 agenda, including enhancing green technology via natural resource rents and addressing immediate climate change challenges in China.

Impact of Information and Communication Technologies on Energy Consumption and Economic Growth: An Empirical Assessment for China

Regarding the association between information and communication technology (ICT) and economic growth and energy consumption, particularly in developing economies, there is a great deal of controversy and a lack of empirical study. This study applies Quantile Autoregressive Distributed Lag Model (QARDL) approach to empirically probe into the effect of ICT on energy consumption and economic growth in China. Two corresponding models for energy consumption and economic growth are formulated and the data of all of the variables spanning over 1995-2021 period is taken from different secondary sources. The findings of the study reveal significant and positive impact of ICT on economic growth at all quantiles in the short and long run. Similarly ICT has significant and positive impact on energy consumption also at all quantiles. The Quantile Granger Causality test provides evidence for bidirectional causality between ICT and economic growth and ICT and energy consumption. Wald test proved the presence of and symmetric and linear association between ICT and economic growth and ICT and energy consumption at quantiles in the long and short run both. In the light of these findings, the study recommends the Chinese government and investors to have more investment in energy efficient ICT products that consume less energy and help increase in economic growth in China.

Effects of ICT and Energy Transition on Sustainable Fiscal Growth: Empirical Evidence from China

It is crucial for sustainable growth policies and strategies, which are currently being used internationally, to balance the various aspects of development, including the economic, environmental, and social ones. There is currently broad agreement that energy transition and Information and communication technologies (ICT) offer fresh momentum for long-term sustainable fiscal growth. The main goal of the current research is to study the effects of energy transition and ICT on sustainable fiscal growth in China over 1995-2020 period. To empirically estimate the above mentioned nexus, the recently introduced Quantile Autoregressive Distributed Lag Model (QARDL) is applied. The study findings for long run indicate that energy transition impacts sustainable fiscal growth negatively over lower qauntiles and at extreme higher quantiles. However, the ICT is found to promote sustainable fiscal growth at all quantiles. The short run dynamics also indicate that current and previous values of ICT and energy transition have significant impact on sustainable fiscal growth. The Wald test of parameter constancy rejects the null hypothesis and shows that non linear and asymmetric impact is present between sustainable fiscal growth and ICT and energy transition in the short run and the long run. Test of Quantile Granger Causality reveals bidirectional causal association between study variables. The study recommends the Chinese government and policy makers to adopt efficient policies to promote ICT use among all the sectors of the economy that can help in achieving sustainable fiscal growth.

Towards Achieving Sustainable Development in China: What Role Does Digital Technologies and Green Technologies Play?

There is always discussion about how to use digitalization to promote sustainable development. While one side vehemently defends using ICT to direct and carry out sustainable development, another strongly struggles to highlight its many drawbacks. Similarly, green technologies are also under the limelight for promoting sustainable development since these technologies combine economic growth and environmental conservation. The purpose of the study is to empirically estimate the effect of digital technologies and green technologies in sustainable development in China. Time series data spanning over 1995-2021 period is analyzed using recently introduced Quantile Autoregressive Distributed Lag Model (QARDL). The findings of the study suggest that there exists positive association between digital technologies and sustainable development over (0.05-0.80) quantiles. The significant and positive association exists between green technologies and sustainable development at all quantiles (0.05-0.95). The study also finds positive and negative association of economic growth and urbanization respectively with sustainable development. The study results emphasize the limitations of conclusion of past studies and provide significant contributions by confirming that the associations among variables is quantile-dependent. The Wald test results also disprove the parameter constancy for the Chinese economy and the findings of Quantile Granger Causality prove the presence of bidirectional causal association between digital technologies, green technologies and sustainable development. The study recommends Chinese policy makers and government to adopt relevant policies to promote sustainable development by effectively utilizing digital and green technologies.