Structural Breaks Research Articles

First vaccine confidence and access index in Argentina: Comparison of results from 2019 to 2022

In this article, we present and empirically illustrate two concepts about vaccines and the way they are perceived by the Argentinean population and the easiness in accessing vaccination in developing countries. First, we focus on the perceptions of people about vaccines in general and develop a confidence index, and second, we analyze barriers to vaccination, measuring the burden citizens have when they intend to receive immunization (or as caretakers, trying to comply with the vaccination calendar of children and adolescents): for this second concept, we develop an access index.The data comes from representative annual surveys from Argentina from 2019 until 2022 (each one with approximately 7000 responders), which allows us to describe trends and check for changes in the confidence in vaccines and barriers towards vaccination. We find high confidence in vaccines in Argentina, although there is a “structural break” in the confidence for all years after 2020. Because we changed the questionnaire and methodology regarding the access to vaccines index in 2022, the discussion focuses on the cross-section of 2022, observing that barriers to vaccination tend to affect less educated caretakers.

Temperature Profile of a Biomass Gasification Power System

Abstract Biomass has been used as a renewable energy resource for years. The coconut tree, considered woody biomass, grows in the tropics and is abundant in the Philippines. To determine the performance of the biomass gasification power system (BGPS) using torrefied coconut shells (TCS), a 1.8-kVA BGPS was designed, fabricated, and tested. The TCS with a calorific value of 34.37 MJ/kg was used as feedstock to profile the temperature of BGPS. The temperature increased from the feeder at 79 °C to a maximum of 904 °C at the combustion zone and goes down to 779 °C at the reduction zone, which causes the breaking of the chemical structure of the torrefied coconut shells to release carbon monoxide (CO), hydrogen (H2) and other contaminants that form part the producer gas. The temperature at the inlet of the internal combustion engine (ICE) generating set had reduced to 37 °C. To improve the performance of the BGPS, it is recommended to review and enhance the cooler’s design to lessen the temperature of the producer gas to at least 25 °C.

The causal relationship between public debt and economic growth in G7 countries: new evidence from time and frequency domain approaches

Investigators of the public debt-economic growth nexus have yet to fully address the crucial issue of determining the direction of causality. There is an implicit assumption—or perception—that the causal relationship is mostly from public debt to economic growth. Beyond this, causal relationships may vary according to the presence of structural breaks as well as different frequency characteristics. The focus of this study is to address these issues. In this context, we comparatively investigate how structural changes and frequency characteristics affect the public debt-economic growth nexus using historical data covering the period 1870–2020 for G7 countries. Methodologically, we use Fourier Toda-Yamamoto and frequency-domain causality techniques from time and frequency-based approaches, respectively. Consistent with our expectations, we show that in the link between public debt and economic growth, they differ from or in some cases confirm each other based on the time and frequency-domain approaches. According to both approaches, in Italy and Japan, the feedback effect is valid, implying a mutual interaction between public debt and economic growth. Also, we find that this relationship is permanent. Similarly, we conclude that there is no causal relationship for France according to both approaches. For the remaining countries, however, we provide diverse evidence on both the direction of causality and the temporary/permanent nature of the causal relationship. The results on temporary or permanent causality at different frequencies offer policymakers and researchers detailed insights into an obscure aspect of the existing literature.

Dynamical simulations of colliding superconducting strings

We study the collisions of elastic superconducting strings, also referred to as current-carrying strings, formed in a U local(1) × U global(1) field-theory model, using three-dimensional numerical field-theoretic simulations. The breaking of U local (1) leads to string formation via the Higgs mechanism, while the scalar field of the second U global(1) carries the current, which condenses onto the string. We construct straight and static superconducting string solutions numerically and identify the regions in which they exist in the model parameter space. We then perform dynamical simulations for colliding superconducting strings with various collision angles and collision velocities. We explore the kinematic parameter space for six sets of model parameters characterising the coupling between the two scalar fields and the current on the string. The final states of the strings (after the collision) are reported diagrammatically. We classify them into four categories: (i) regular intercommutation, (ii) double intercommutation, (iii) bound state, and (iv) expanding string solution. We find that the outcome of the collision process is the regular intercommutation of the colliding strings in most of the kinematic parameter space while they form bound states for small velocities and small angles. We also find that the strings undergo two successive intercommutations and, therefore, pass through one other in a small region corresponding to relatively small angles and velocities of order c/2. The string structure breaks down when there is a relatively large coupling between the two scalar fields, even if each string is stable before the occurrence of the collision.

Artificial intelligence and skilled employment in South Africa: Exploring key variables

The discourse surrounding artificial intelligence (AI) and its repercussions on skilled employment merits careful consideration. While AI technologies have the potential to result in job displacement within specific sectors, they concurrently usher in new employment opportunities, especially for individuals possessing advanced skills. The primary objective of this paper is to thoroughly evaluate the impact of AI on skilled employment within the South African economy. To achieve this objective, the study employs the Autoregressive Distributed Lag (ARDL) model and Granger causality analysis, spanning from 2012Q1 to 2021Q4. The results derived from the ARDL estimation reveal a substantial and positive contribution of artificial intelligence to skilled employment in South Africa, a trend observed in both the long and short run. However, two structural breaks were identified in the data, hence a re-estimation of the ARDL model. The re-estimated ARDL model revealed a negative and significant relationship between AI and skilled employment. In light of these findings, this study advocates implementing regulations and labor market policies that promote the responsible deployment of AI technology while safeguarding workers’ rights and job security. This could include establishing guidelines for AI deployment in the workplace, ensuring transparency and accountability in AI systems, and implementing social safety nets to support workers during job transitions.

The Relationship Between Foreign Direct Investments and CDS Premiums in Türkiye: Time-Varying Causality Test (2011-2023)

Foreign direct investment (FDI) is used as an important policy tool in providing the funds needed by developing countries for development purposes. Countries with insufficient capital are in a race to attract foreign capital to their countries through various political, economic and legal regulations. This study econometrically investigates the relationship between FDI and CDS premiums in Türkiye and analyses whether there is a relationship between the monthly frequency data between 2011-2023. In order to determine the relationship between the variables, the stationarity of the variables should be determined first. "Lee and Strazicich (2003)" unit root tests, which take into account structural breaks, were applied to the data. The determination of whether there is causality between the variables, and if there is a causality relationship, the determination of the direction of the causality relationship is tested by mutual "Granger Causality" and “Time-Varying Causality” test methods. As a result of the study, no causality relationship was found from FDI to CDS premiums and from CDS premiums to FDI at 5% significance level.

All Road User Casualties (Killed) in Great Britain from 1926. Linear and Nonlinear Trends with Persistent Data

This paper deals with the analysis of road casualties in Great Britain, using annual data since 1926. Based on the persistent nature of the data, fractional integration methods are used that include linear and non-linear (structural breaks) models. The results indicate that when the whole data set is employed the series is nonstationary I(1) implying permanency of shocks. However, considering data starting in 1964 we observe a significant negative time trend along with a lower degree of integration that implies transitory shocks. In order to avoid the abrupt change produced by the break, a nonlinear deterministic trend model based on Chebyshev polynomials in time is also considered with the whole sample, and though the order of integration is much lower than 1, the unit root null hypothesis cannot yet be rejected.

Fiscal Sustainability of Latin American Countries Under Cross-Dependency and Structural Breaks

Fiscal Sustainability of Latin American Countries Under Cross-Dependency and Structural Breaks

Impact of oil price, economic globalization, and inflation on economic output: Evidence from Latin American oil-producing countries using the quantile-on-quantile approach

This research examines the impact of oil prices, economic globalization, and inflation on the economic output in oil-producing countries in Latin America. We employ advanced time series techniques to achieve precision in findings and reliability of policy inferences. We use cointegration techniques with and without structural breaks because oil prices are highly volatile, and the economies analyzed are unstable in the face of political and social changes. Additionally, we performed a sensitivity analysis using standard quantile and the newly developed quantile-on-quantile regressions to measure the impacts of the covariates on real per capita output. Finally, we use causality Fourier techniques to identify the direction of causal relationships between the series. We found that the oil price, economic globalization, inflation, and output have a long-term equilibrium relationship in the presence of structural breaks. Likewise, quantile models show that the impacts of inflation, economic globalization, and oil prices on economic output are extensively non-linear across the quantile distribution. We found a negative connection between inflation and economic output in the surveyed territories. We propose that those responsible for energy policy in the countries studied should rethink crude oil policies to maximize the benefits of oil exploitation under stagnant globalization and growing inflation.

Housing search activity and quantiles-based predictability of housing price movements in the USA

Purpose Recent evidence from a linear econometric framework infers that housing search activity, captured from Google Trends data, can predict housing returns for the USA at a national and regional (metropolitan statistical area [MSA]) level. Based on search theory, the authors, however, postulate that search activity can also predict housing returns volatility. This study aims to explore the possibility of using online search activity to predict both housing returns and volatility. Design/methodology/approach Using a k-th order non-parametric causality-in-quantiles test allows us to test for predictability in a robust manner over the entire conditional distribution of both housing price returns and its volatility (i.e. squared returns) by controlling for nonlinearity and structural breaks that exist in the data. Findings The analysis over the monthly period of 2004:01 to 2021:01 produces results indicating that while housing search activity continues to predict aggregate US house price returns, barring the extreme ends of the conditional distribution, volatility is relatively strongly predicted over the entire quantile range considered. The results carry over to an alternative (the generalized autoregressive conditional heteroskedasticity-based) metric of volatility, higher (weekly)-frequency data (over January 2018–March 2021) and to over 84% of the 77 MSAs considered. Originality/value To the best of the authors’ knowledge, this is the first study regarding predictability of overall and regional US housing price returns and volatility using search activity, based on a non-parametric higher-order causality-in-quantiles framework, which is insightful to investors, policymakers and academics.

A MS SHARV-MIDAS model: a new regime-switching model for volatility forecasting

ABSTRACT The recent SHARV and SHARV-MIDAS models incorporate current returns information for volatility forecasting. However, these models fail to capture the intricate transition process of volatility states due to structural breaks caused by extreme events in financial markets. Therefore, we introduce a Markov regime-switching into the SHARV-MIDAS model and develop the MS SHARV-MIDAS model that comprehensively accounts for structural breaks, long memory, and current return information in volatility forecasting. Furthermore, we examine the effectiveness of this model through empirical studies and robustness tests, which demonstrate its superiority over benchmark models regarding in-sample fitting and out-of-sample volatility forecasting.

Fintech industry risk: does investor attention matter?

ABSTRACT The Fintech industry risk challenges the stability of China’s financial system. This paper makes the first attempt to capture the dynamics of the Fintech industry risk, represented by Fintech stock volatility, from the investor attention perspective. The novelty of this paper is that we fully consider the regime changes in the Fintech industry risk by adopting the AR-MS-GARCH-MIDAS with the Markov regime-switching process incorporated in the short-run volatility component, long-run volatility component, or both. We find that (1) Fintech industry risk is subject to structural breaks, (2) higher investor attention tends to alleviate the Fintech stock volatility, while the negative impact strengthens when the industry is in volatile condition, (3) investor attention contributes to improving the accuracy of the Fintech stock volatility forecasting, (4) considering the structural breaks of investor attention is important in volatility forecasting. We enrich the literature on the Fintech industry risk regulation, stock volatility forecasting, and the attention allocation theory.

Analysing the Nexus: Stock Indices and Cryptocurrencies During the Conflict Between Russia and Ukraine

Background: The global economy faced increased instability due to the simultaneous occurrence of two major events, the global pandemic in 2020 and the Russian-Ukrainian conflict in 2022, which impacted the financial markets. Purpose: This paper aimed to evaluate the comovements between the USA (S&P 500), Germany (DAX 30), France (CAC40), Japan (Nikkei 225), Canada (TSX), Russia (MOEX) and Ukraine (PFTS) stock markets and the cryptocurrencies Bitcoin (BTC), Ethereum (ETH), Litcoin (LTC) Dash (DASH/USD), Ripple (XRP) DigiByte (DGB) and Nem (XEM), from February 24, 2022, to April 12, 2023 Methods: The approach to our research question will involve using the causality econometric model, Granger SVAR (Vector Autoregressive). Results: The results showed that stock indices and digital currencies show sharp structural breaks, and not all markets influence cryptocurrencies. The MOEX stock market affects the price formation of BTC, ETH, DGB, XEM, and XRP, while the DAX 30 stock index impacts ETH, LTC, DASH, DGB, and XEM. The Ukraine market (PFTS) influences ETH, but the other stock markets do not influence any of the cryptocurrencies analysed. Conclusion: Investors, policymakers, and other participants operating in the digital currency markets can find valuable information in the study's conclusions when seeking to rebalance their portfolios.

Economic policy uncertainty and equity index in sub-Saharan African (SSA) countries: accounting for multiple structural breaks in a panel framework

Economic policy uncertainty and equity index in sub-Saharan African (SSA) countries: accounting for multiple structural breaks in a panel framework

Simmering tensions on the Russia–Ukraine border and natural gas futures prices: identifying the impact using new hybrid GARCH

Focusing on the Russia–Ukraine war, this paper investigates natural gas futures volatilities. Applying several hybrid GARCH and EGARCH models, which innovatively incorporate both fat-tailed distribution errors and structural breaks, we derive the following new evidence. First, our hybrid modeling approach is effective in timely capturing the natural gas futures volatility spike when tensions simmered on the Russia–Ukraine border. Second, the hybrid modeling approach is effective for not only GARCH modeling but also EGARCH modeling. Third, the volatility estimates from our hybrid models have predictive power for the volatilities of nonhybrid models. Fourth, the volatility estimates from the nonhybrid models lag behind the volatilities of our hybrid models.

Estimating price elasticity of demand for mineral commodities used in Lithium-ion batteries in the face of surging demand

The accelerating adoption of clean energy technologies is driving demand for certain mineral commodities like lithium, essential for electric vehicle batteries. Understanding the influence of the energy transition on each market requires examining their supply and demand price elasticities. However, there have only been a limited number of studies that have estimated mineral demand price elasticities in emerging technologies. We empirically estimate the price elasticity demand (PEDs) for mineral commodities used in lithium-ion battery cathodes and anodes—cobalt, graphite, lithium, manganese, and nickel. We also test whether the price elasticities of these mineral commodities have changed due to any structural changes in recent years, given the recent expansion in the sales of electric vehicles. Using Two Stage Least Squared (2SLS) econometric techniques, we find that demand has become less elastic for lithium (-0.11), cobalt (-0.45), nickel (-0.09), and manganese (-0.04) after structural breaks in 2020, 2013, 2009, and 2014, respectively. In contrast, demand for natural amorphous graphite (-0.36), and natural flake graphite (-0.58) remains relatively stable over the time period assessed.

Revisiting the interdependences across global base metal futures markets: Evidence during the main waves of the COVID-19 pandemic

This paper examines the correlation and connectedness across global base metal futures markets in the periods before and during the COVID-19 pandemic. We find that the impacts of the pandemic shock on the dynamic correlations between global commodity exchanges vary across futures markets, with significant structural breaks being detected in the underlying dynamic correlations at different stages of the pandemic. We document that stronger price signals were generated in the aggregate markets due to the pandemic shock, and the roles of copper and zinc futures as the pricing center in the global base metal markets increased during the pandemic period. Moreover, our findings show that the leading position of the London Metal Exchange in the global base metal futures pricing declined, while the pricing authority of the Shanghai Futures Exchange improved significantly during the pandemic time. Leveraging post-pandemic data, we identify interdependence structures amid some crucial geopolitical conflicts.

Do socially responsible indices outperform conventional indices? Evidence from before and after the onset of Covid‐19

AbstractAccording to traditional portfolio theories constraints, restrictions, and screens applied in portfolio selection reduces the diversification opportunities which can impact financial performance. Is this true in the case of socially responsible investment (SRI)? To answer this, present study analyzes the performance of Socially Responsible (SR) indices in comparison to conventional indices in an emerging economy. The uniqueness of the study is that it analyses the performance of Shariah, ESG, and thematic indices in a single study. Further, the study measures the impact of Covid‐19 on them. Comparative performance evaluation was conducted by using absolute return analysis and risk‐adjusted measures namely, Sharpe ratio, Treynor ratio, tracking error, information ratio, capital asset pricing model (CAPM), Fama–French three‐factor, and Carhart's four‐factor models. The Structural break was identified, hence analysis was conducted for the total period (January 2017–March 2023) and two sub‐periods, that is, pre and post‐Covid‐19 period. No significant difference was found between the returns of SR indices and conventional indices as against the benchmark index on the basis of absolute return analysis. Sharpe ratio and Treynor ratio both were having negative values for all the SR and conventional indices. Tracking error for all the SR and Conventional indices were very low. The CAPM and both multi‐factor models univocally pointed toward the underperformance of all the SR (except S&P BSE 100 ESG index which had equal performance) and both conventional indices against the benchmark index. Noteworthy point is that only Shariah indices gave the highest returns during post‐Covid period. This research will help in deepening the SRI in the capital market. Companies should increase their ESG scores and make efforts to be listed on the SR indices. Policymakers should announce some kind of rebates, or recognition for star‐performing companies in the field of sustainability to encourage other companies to adopt SR practices in their business operations. The novelty of the current study is that it adds to the socially responsible literature by analyzing the performance of Shariah, ESG, and Thematic indices and conventional indices in a single study in the fastest‐growing economy of India and analyses the impact of Covid‐19 on this performance.

Exploring inflation dynamics in Canada: A threshold vector autoregressive approach

Given the pivotal role of inflation expectations in contemporary monetary policy, we posit that if monetary policy has effectively influenced inflation expectations, thereby altering the trajectory of total inflation, a structural break in the path of total inflation should be observable. Conversely, if inflation expectations have remained stable and monetary policy has had limited impact, a stable vector autoregressive (VAR) model should adequately describe the path of total inflation. To address these hypotheses, a non-linear specification of a threshold vector autoregressive (TVAR) model is employed, offering a comprehensive analytical framework for the examination of these dynamics.

What a Puzzle! Unravelling Why UK Phillips Curves were Unstable

AbstractThe UK relationship between nominal wage inflation and the unemployment rate is unstable. Over sub‐periods of the last 160 years of turbulent data, Phillips curve slopes range from strongly negative, slightly negative, flat, slightly positive and strongly positive. Our constant‐parameter congruent model of real wages explains these instabilities, yet also implies a constant negative relationship between nominal wage inflation and the unemployment rate when corrected by its regressors. Disentangling these effects reveals that structural breaks in the real‐wage model's variables do not explain the instabilities, which instead occur during sub‐periods when some of its explanatory variables are insignificant.