Structural Breaks Research Articles

Robust variance estimators in application to segmentation of measurement data distorted by impulsive and non-Gaussian noise

The paper algorithmizes the problem of regime change point identification for data measured in a system exhibiting impulsive behaviors. This is a fundamental challenge for annotation of measurement data, relevant, e.g., for designing data-driven autonomous systems. The contribution consists in formulation of an offline robust methodology based on the classical approach for structural break detection. The problem of data segmentation for the changing scale is solved as a process variable sensitive to the occurrence of a critical event in a reorganizing physical system. The advantage is that this approach does not require the existence of a variance of the data distribution. Efficiency has been evaluated for simulated data from two distributions and for real-world data sets measured in financial, mechanical, and medical systems. Simulation studies show that in the most challenging case, the error mean absolute in estimating the regime change is 20 times lower for the robust approach compared to the classical approach.

Natural resources and economic growth: Examining the role of globalization, financial development, and digitalization in India

Natural resources are considered vital for economic growth, laying the foundations for various industries. Therefore, the present study aims to examine the importance of natural resources, globalization, financial development, and digitalization in India's economic performance from 1970 to 2020. The study utilizes the Auto-regressive distributed lag (ARDL) techniques with structural breaks and the Granger causality test to detect the direction of causality. Further, Fully Modified Ordinary Least Square (FMOLS) and Dynamic Ordinary Least Square (DOLS) were used to check the robustness of the long-run coefficients. The finding supports the “Resource curse hypothesis” in India. Globalization and financial development drive the long-run growth performance. In the short run, digitalization and globalization enhance growth performance. The Granger causality result unfolds a bidirectional causal relationship between economic growth and digitalization and unidirectional causation from financial development and globalization to economic growth. The study's outcomes have notable policy implications that emphasize the need to induce financial development, integrate digital technology into core sectors of the economy, and encourage market openness and efficient utilization and management of natural resources for sustainable economic growth.

Links between technological innovation, financial efficiency and environmental quality using quantile regressions: The role of foreign direct investment, institutional quality and natural resources

Policymakers and academics are interested in identifying mechanisms that promote environmental sustainability due to their relationship with climate change. This research evaluates the channels that transmit the effect of technology and financial efficiency on environmental quality. The relationship is moderated by real production per capita, foreign direct investment, natural resource rents, and institutional quality in the context of the Environmental Kuznets Curve. The research covers the 1996–2021 period for a sample of 88 economies classified into three groups according to the World Bank Atlas Method. We use second-generation cointegration techniques with structural breaks and quantile regression models. The findings offer sufficient evidence to conclude that the impact of technology and financial efficiency on environmental quality is heterogeneous throughout the distribution. Our findings suggest that technology is more associated with maximizing production than mitigating or restoring environmental deterioration. A policy implication derived from our research is to encourage the generation of carbon-free technology and promote financial efficiency in upper-middle-income countries to achieve environmental sustainability.

Brief Report: A population-based study of the impact of the COVID-19 pandemic on benzodiazepine use among children and young adults.

The COVID-19 pandemic was associated with increases in the prevalence of depression and anxiety among children and young adults. We studied whether the pandemic was associated with changes in prescription benzodiazepine use. We conducted a population-based study of benzodiazepine dispensing to children and young adults ≤ 24 years old between January 1, 2013, and June 30, 2022. We used structural break analyses to identify the pandemic month(s) when changes in prescription benzodiazepine dispensing occurred, and interrupted time series models to quantify changes in dispensing following the structural break and compare observed and expected benzodiazepine use. A structural break occurs where there is a sudden change in the trend of a time series. We observed an immediate decline in benzodiazepine dispensing of 23.6 per 100,000 (95% confidence interval [CI]: -33.6 to -21.2) associated with a structural break in April 2020, followed by a monthly decrease in the trend of 0.3 per 100,000 (95% CI: -0.74 to 0.14). Lower than expected benzodiazepine dispensing rates were observed each month of the pandemic from April 2020 onward, with relative percent differences ranging from - 7.4% (95% CI: -10.1% to - 4.7%) to -20.9% (95% CI: -23.2% to -18.6%). Results were generally similar in analyses stratified by sex, age, neighbourhood income quintile, and urban versus rural residence. Further research is required to understand the clinical implications of these findings and whether these trends were sustained with further follow-up.

SCN as a local probe of protein structural dynamics.

The dynamics of lysozyme is probed by attaching -SCN to all alanine residues. The one-dimensional infrared spectra exhibit frequency shifts in the position of the maximum absorption of 4 cm-1, which is consistent with experiments in different solvents and indicates moderately strong interactions of the vibrational probe with its environment. Isotopic substitution 12C → 13C leads to a redshift by -47 cm-1, which agrees quantitatively with experiments for CN-substituted copper complexes in solution. The low-frequency, far-infrared part of the protein spectra contains label-specific information in the difference spectra when compared with the wild type protein. Depending on the position of the labels, local structural changes are observed. For example, introducing the -SCN label at Ala129 leads to breaking of the α-helical structure with concomitant change in the far-infrared spectrum. Finally, changes in the local hydration of SCN-labeled alanine residues as a function of time can be related to the reorientation of the label. It is concluded that -SCN is potentially useful for probing protein dynamics, both in the high-frequency part (CN-stretch) and in the far-infrared part of the spectrum.

Mineral resources and equitable economic development: South Asian mineral resources policy perspective with innovation for resources efficiency

In the contemporary era, economies are heavily relying on minerals extraction, where the literature offers ambiguous evidence regarding minerals-growth nexus. To solve the puzzle, this research inspects the empirical association between mineral resources and economic prosperity in the South Asian nations from 1988 to 2022. This study also scrutinizes the importance of financial development, remittances, renewable energy use, and technological innovation. The research utilizes a panel dataset and applies the normality, stationarity, and cointegration tests. The results validates non-normal data distribution, stationarity of variables, and existence of long-run equilibrium association between them. To tackle non-normality and structural break issues, panel quantile regression and structural break ordinary least squares (OLS), are used. The study's results emphasize that mineral rents and remittances harm economic growth, indicating the poor outcomes associated with excessive dependence on external sources income. On the other hand, the factors identified as key drivers of sustainable economic expansion include financial progress, the use of green energy, and technical innovation. The results are tested for robustness via dynamic OLS, fully modified OLS, and canonical cointegration regression. This research recommends the significance of diversified revenue sources, fostering financial expansion, and making investments in renewable energy systems. To create more equitable growth, policymakers must focus on improved R&D and financial inclusion.

Relationship among micro-pollutants, economic growth, renewable energy, nanotechnology and ecological foot prints of China: evidence from panel data analysis.

An important concern is the availability of clean drinking water, which is an essential need for human survival. This issue arises due to the existence of hazardous micropollutants originating from various emission sources. Nanotechnology aids in the mitigation of micropollutants by assimilating and counteracting their effects, hence diminishing their influence on water and other ecosystems. The study investigates the relationship between nanotechnological progress, the adoption of renewable energy, environmental consequences, and economic growth in China, using the Environmental Kuznets Curve theory as a conceptual framework. The study employs panel cointegration tests to analyze structural breaks from 2000 to 2020. Nanotechnology is expected to reduce environmental degradation and the presence of micro-pollutants by increasing the use of renewable energy and promoting energy conservation. Nanotechnology is crucial for mitigating micro-pollutants and advancing sustainable development in this specific context. However, the literature also highlights the harmful consequences of nanoparticle emissions caused by nanotechnology on human and environmental health for a long duration, requiring more examination. This research is the first empirical inquiry into the relationship between improvements in nanotechnology, the use of renewable energy, economic growth, and ecological effect, all within the context of the Environmental Kuznets Curve theory. The results confirm the successful incorporation of all components with a focus on long-term outcomes. The findings suggest that the EKC hypothesis is relevant in China. In China, advancements in nanotechnology have a moderating effect on environmental degradation. The use of renewable energy sources in China enhances environmental circumstances. Given the offered empirical evidence, it is advisable for the government to have a leading role in the development of innovative nanotechnologies that have low emissions of nanoparticles. By using this approach, it will be possible to encourage the conservation of energy and the use of renewable sources in a more secure way, hence improving the effectiveness of sustainable development initiatives.

Do bitcoin electricity consumption and carbon footprint exhibit random walk and bubbles? Analysis with policy implications

One of the main current focuses of global economies and decision-makers is the efficiency of energy utilization in cryptocurrency mining and trading, along with the reduction of associated carbon emissions. Understanding the pattern of Bitcoin's energy consumption and its bubble frequency can greatly enhance policy analysis and decision-making for energy efficiency and carbon emission reduction. This research aims to assess the validity of the random walk hypothesis for Bitcoin's electricity consumption and carbon footprint. We employed both traditional methods (ADF and KPSS) and recently proposed unit root techniques that account for structural breaks and non-linearity in the data series. Our analysis covers daily data from July 2010 to December 2021. The empirical results revealed that traditional unit root techniques did not confirm the stationarity of both bitcoin's electricity consumption and carbon footprint. However, novel structural break (SB) and linearity tests conducted enabled us to discover five SB episodes between 2012 and 2020 and non-linearity of the variables, which informed our application of the newly developed non-linear unit root tests with structural breaks. With the new methods, the results indicated stationarity after accommodating the SB and non-linearity. Furthermore, based on Phillips and Shi (2019)'s test, we identified certain bubble episodes in the bitcoin energy and carbon variables between 2013 and 2021. The major drivers of the bubbles in bitcoin energy consumption and carbon footprint are variables relating to the bitcoin and financial markets activities and risks, including the global economic and political risks. The study's conclusion based on the above findings informs several policy implications drawn for energy and environmental management including the encouragement of green investments in cryptocurrency mining and trading.

Long-Run Trends in Long-Maturity Real Rates, 1311–2022

Taking advantage of key recent advances in long-run economic and financial data, we analyze the statistical properties of global long-maturity real interest rates over the past seven centuries. In contrast to existing consensus, we find that real interest rates are in fact trend stationary and exhibit a persistent downward trend since the Renaissance. We investigate structural breaks in real interest rates over time and find that overall the Black Death and the 1557 “Trinity default” appear as consistent inflection points. We further show that demographic and productivity factors do not represent convincing drivers of real interest rates over long spans. (JEL E43, F30, N20)

Evidence for, and significance of, the Neoproterozoic Xuefeng Orogeny, South China

The recognition of unconformities is important for determining the long-term evolution of orogenic belts. We document an unconformity between Tonian and Cryogenian strata of the Nanhua Basin of South China. This stratigraphic break has previously been termed the Xuefeng Movement and is developed within the Yangtze Block and western Cathaysia Block. Deformation characteristics and detrital zircon U-Pb geochronology from rock units underlying and overlying the unconformity (i.e., the Niuguping and Gucheng formations, respectively) from the Anhua region of the northern Hunan Province in the southeastern Yangtze Block are documented. The Niuguping Formation displays a set of top-to-northwest imbricate thrust faults and related fault-propagation folds, NEE-plunging subparallel crenulation lineations, a foliation, and shows widespread greenschist facies metamorphism. In contrast, the overlying Gucheng Formation exhibits no obvious stratal deformation (apart from a mild inclination of bedding) and is unmetamorphosed. Detrital zircon age spectra of the samples from the Niuguping Formation are characterized by unimodal age peaks of ca. 750–730 Ma, while those of the samples from the Gucheng Formation yield a multimodal age distribution, with a few syn-sedimentary detrital zircons (<700 Ma), and much older zircons with age peaks of ca. 800 Ma, 2.1–1.9 Ga, and 2.6–2.3 Ga. Combining this data with previous geochronology indicates deposition of the Niuguping and Gucheng formations in the late Tonian (725–720 Ma) and Cryogenian (690–660 Ma), respectively. The time gap across the unconformity is some 30 Ma (720–690 Ma). The unconformity and associated structural and metamorphic break extend across the Yangtze Block and western Cathaysia Block, defining a major regional orogenic event. The significantly different detrital zircon spectra of strata across the unconformity suggest a dramatic change in the source due to the Xuefeng Orogeny in the Yangtze Block. Zircon U-Pb-Hf isotopic evidence indicates that detritus of the Niuguping Formation below the unconformity was derived from syn-depositional magmatic activity in the northern Yangtze Block, while a significant increase of Archean to Paleoproterozoic ages for the Gucheng Formation, suggests that the Xuefeng Orogeny resulted in crustal thickening and denudation in the northern Yangtze Block. These changes in provenance, along with changing deformational and metamorphic patterns, indicate evolution of the Nanhua Basin from a compressional to an extensional back-arc basin, which we infer is related to tectonic switching from an advancing to a retreating subduction system in the northern Yangtze Block at the time of the unconformity.

Stock Returns and Volatility Cross-Spillover Effect Between Sri Lanka And Asian Stock Markets

Sri Lanka holds a pivotal position in the Indian Ocean, fostering significant economic ties with South Asia, the Middle East, and Africa. This study examines the financial linkages and stock market behavior between Sri Lanka and key Asian economies (India, China, Pakistan, and Japan) from 2015 to 2021. Using daily stock prices from Bloomberg.com, the analysis employs the EGARCH (1,1) model to assess return and volatility spillovers. Results reveal negative return spillovers from India to Sri Lanka and cross-volatility spillovers from India, China, Pakistan, and Japan to Sri Lanka. Conversely, Sri Lanka exhibits negative return spillovers to India and Pakistan and cross-volatility spillovers to China and Japan. Sub-analysis identifies structural breaks in December 2019, indicating shifts in spillover dynamics pre- and post-Covid-19. These findings offer insights for investors, policymakers, fund managers, and governments to optimize investment strategies, formulate stable policies, and enhance portfolio diversification.

Income inequality and monetary policy regimes

This paper reconsiders the possible effects of monetary policy on income inequality. Accounting for potential structural breaks in the relationship, we find that this relationship varies across time and reverses following the Volcker disinflation during the Great Moderation.

The Velocity of Money and Lessons for Monetary Policy in Nigeria: An Application of the Quantile ARDL Approach

AbstractThe paper investigates the determinants and stability of the income velocity of money while accounting for structural breaks in its approach from the period Q1 1981 to Q4 2023 in Nigeria. The paper extends previous works by accounting for four velocities of money functions having accounted for structural breaks in its approach. The study adopted the Bai and Perron breakpoint test, the Zivot and Andrews, and Perron and Vogelsang structural break unit root tests to determine the break dates and the variables’ stationary properties. In analyzing the main results, the study used the quantile ARDL method to establish the determinants of income velocity of money in Nigeria, while it used the CUSUM tests to establish the stability of money velocity in Nigeria. The study found that the determinants of the velocity of money are per capita income, the exchange rate, financial development, inflation, and the interest rate across the four models. Furthermore, the four velocities of money functions were unstable for Nigeria using the cumulative sum and cumulative sum of squares tests. Consequently, the study recommends effective monitoring and the provision of sound monetary policies to ensure a stable and predictable velocity of money. This can be achieved by understanding and focusing on these key determinants and crafting informed strategies to manage economic growth and stability. This holistic approach ensures that the benefits of economic policies are felt broadly across the economy, ultimately bolstering overall financial health and enhancing the velocity of money, which is crucial for sustained economic progress.

Multi-band enhanced nonreciprocal thermal radiation based on Weyl semimetals

Previous studies manifested that the majority of structures that exhibit nonreciprocal thermal radiation in the mid-infrared are capable of achieving either single-band strong nonreciprocity or multi-band weak nonreciprocity at a large incidence angle. However, few structures can realize multi-band strong nonreciprocity at a small incidence angle. To address such scientific issues, we propose a tunable nonreciprocal thermal emitter based on gallium arsenide (GaAs)/graphene/Weyl semimetal (WSM). This device is capable of achieving strong nonreciprocity at 7.3 μm, 10 μm and 13.6 μm wavelengths at an incidence angle of 25.5°. It is shown that the field enhancement of the GaAs/graphene composite layer can improve the nonreciprocal response of the WSM layer. In addition, by changing the Fermi energy level of graphene and the axial vector b of the Weyl semimetal, tunable nonreciprocal thermal radiation can be realized. What's more, we find that the structure breaks Kirchhoff's law without lithography and an external magnetic field, which reveals the advantages of applying our research in the field of thermal radiation.

Tunable Near-Field Radiative Heat Transfer between Graphene-Coated Magneto-Optical Metasurfaces.

The highly structured design of metasurfaces greatly facilitates the manipulation of near-field radiative heat transfer (NFRHT). In this study, we incorporate magneto-optical materials into metasurfaces to theoretically explore the mechanism for controlling NFRHT between anisotropic magneto-optical metasurfaces. Our findings indicate that the interaction between the magnetization-induced modes, arising from interband transitions of graphene, and the surface modes of InSb under a magnetic field leads to a transition in the heat transfer spectrum from a dual band to a triple band. The modification of the distribution and magnitude of transmission wave vectors in surface electromagnetic modes by magnetic fields serves to modulate the radiative heat flux. By combining active control by a magnetic field with passive structural design of metasurfaces, the regulation of heat flux can be increased by more than 8-fold compared with the planar configuration. Additionally, the magnetic field amplifies the anisotropy of the photon energy distribution induced by the symmetry breaking of the metasurface structure. This study is anticipated to provide a pathway for achieving flexible tuning of NFRHT by combining active and passive regulation. It also opens up possibilities for multiband information transmission and for improving the performance of energy conversion devices.

The Post-Internationalization Evolution of the Price Discovery Pattern in China’s Iron Ore Markets

Previous literature provided evidence of a negative impact of internationalization on the quality of China’s iron ore futures market within a 6-month window (i.e. June 1, 2018 to November 31, 2018), but little is known on the long-term impact. Using a longer, 4-year-7-month window from June 1, 2018 to December 30, 2022, we examine the market’s price discovery function, an important measure of market quality. We find significant structural break in China’s iron ore data series in August 2021, which largely coincide with a period of rising commodity prices to all-time high. Before the breaks, the price discovery function of China’s iron ore futures appears quite weak, with the spot market leading the futures market, which is consistent with the findings of Fan et al. During the post-break period, however, the futures market demonstrates a stronger price discovery function and takes over the leading role. The day-of-the-week effects in iron ore futures also seem to weaken post-break as well. Our GARCH model results exhibit significant bi-directional volatility spillover between the futures and spot markets.

A Markov‐switching spatio‐temporal ARCH model

Stock market indices are volatile by nature, and sudden shocks are known to affect volatility patterns. The autoregressive conditional heteroskedasticity (ARCH) and generalized ARCH (GARCH) models neglect structural breaks triggered by sudden shocks that may lead to an overestimation of persistence, causing an upward bias in the estimates. Different regime‐switching models that have abrupt regime‐switching governed by a Markov chain were developed to model volatility in financial time series data. Volatility modelling was also extended to spatially interconnected time series, resulting in spatial variants of ARCH models. This inspired us to propose a Markov switching framework of the spatio‐temporal log‐ARCH model. In this article, we discuss the Markov‐switching extension of the model, the estimation procedure and the smooth inferences of the regimes. The Monte Carlo simulation studies show that the maximum likelihood estimation method for our proposed model has good finite sample properties. The proposed model was applied to 28 stock indices' data that were presumably affected by the 2015–2016 Chinese stock market crash. The results showed that our model is a better fit compared to that of the one‐regime counterpart. Furthermore, the smoothed inference of the data indicated the approximate periods where structural breaks occurred. This model can capture structural breaks that simultaneously occur in nearby locations.

Testing the Effectiveness of Government Investments in Environmental Governance: Evidence from China

The Chinese government has taken many measures to improve the environment, such as directly investing in pollution control infrastructure, but the effectiveness remains to be tested. This paper employs the Toda–Yamamoto test and the Fourier Toda–Yamamoto test to examine the link between environmental governance investment and environmental quality in China from 2003 to 2020. PM2.5, which measures air quality, is used here as an indicator of the environment. The results reveal that environmental governance investment has notably lessened the average concentration of PM2.5 in certain regions, indicating a positive effect on environmental quality, albeit with regional variations. Taking structural breaks into consideration, the relationship between environmental governance investment and environmental amelioration is significant in a smaller number of regions. Additionally, the cross-sectional correlation is further scrutinized to assess the essential robustness of the causality between the two variables. The findings lend support to the aforementioned conclusion. These conclusions provide valuable guidance for China’s policymakers in optimizing environmental governance investments to curb pollution and achieve sustainable development.

Comparison of the Performance of Structural Break Tests in Stationary and Nonstationary Series: A New Bootstrap Algorithm

AbstractStructural breaks are considered as permanent changes in the series mainly because of shocks, policy changes, and global crises. Hence, making estimations by ignoring the presence of structural breaks may cause the biased parameter value. In this context, it is vital to identify the presence of the structural breaks and the break dates in the series to prevent misleading results. Accordingly, the first aim of this study is to compare the performance of unit root with structural break tests allowing a single break and multiple structural breaks. For this purpose, firstly, a Monte Carlo simulation study has been conducted through using a generated homoscedastic and stationary series in different sample sizes to evaluate the performances of these tests. As a result of the simulation study, Zivot and Andrews (J Bus Econ Stat 20(1):25–44, 1992) are the best-performing tests in capturing a single break. The most powerful tests for the multiple break setting are those developed by Kapetanios (J Time Ser Anal 26(1):123–133, 2005) and Perron (Palgrave Handb Econom 1:278–352, 2006). A new Bootstrap algorithm has been proposed along with the study’s primary aim. This newly proposed Bootstrap algorithm calculates the optimal number of statistically significant structural breaks under more general assumptions. Therefore, it guarantees finding an accurate number of optimal breaks in real-world data. In the empirical part, structural breaks in the real interest rate data of the US and Australia resulting from policy changes have been examined. The results concluded that the bootstrap sequential break test is the best-performing approach due to the general assumption made to cover real-world data.

Examining the factors of fertilizer pricing

AbstractA strong surge in fertilizer prices in 2021 led farmers and policymakers to question the factors that drive fertilizer pricing. This study examines the relationship among natural gas, corn, and fertilizer prices, finding five structural breaks from 1997 to 2022. The pass‐through rates of natural gas and corn price changes to fertilizer prices show significant shifts after the passage of the biofuel mandate, with those changes fading as the market matured. Meanwhile, natural gas price changes are the main contributing factor in the latest period. However, the linkage among these prices continues to evolve and is likely to diminish over time.