Structural Break Tests Research Articles

One Man’s Bubble Is Another Man’s Rational Behavior: Comparing Alternative Macroeconomic Hypotheses for the US Housing Market

Competing macroeconomic hypotheses have been developed to explain the US housing market and possible bubble behavior. We employ both seasonally adjusted (SA) and non-seasonally adjusted (NSA) monthly data for about 30 independent variables to examine alternative macro hypotheses for home prices. Using a neural network model as an atheoretical non-linear approach to capture the relative importance of alternative macro variables, we show that these hypotheses generate different macro relevance. As an alternative to testing housing time series, we focus on bubble identification being hypothesis dependent. Model forecast errors (residuals) identify the potential presence of bubbles through standardized residual CUSUM tests for structural breaks. By testing for housing bubbles from these unstructured models, we generate conclusions on the presence of bubbles prior to the Great Financial Crisis and the post-pandemic periods. Competing macro hypotheses or narratives will generate different conclusions on the presence of bubbles and create bubble identification issues.

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.

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.

Relationship Between Twitter Sentiment Analysis and Bitcoin Prices: Econometric Analysis of Long and Short Term Dynamics

The significance of social media in influencing cryptocurrency pricing has grown considerably in recent years. This study aims to explore the correlations between social media sentiments and Bitcoin pricing, both in the short and long terms, while also investigating the direction of these relationships. Sentiment analysis was conducted using the TextBlob model, which uncovers the underlying meaning in text through analysis. The study tested the hypothesis that there exists a relationship between sentiment analysis scores and Bitcoin prices over both short and long periods. Ensuring stationarity was crucial for time series analysis, involving the use of structural break and traditional unit root tests. Daily data from June 2021 to June 2022 was examined, with December 2021 serving as the focal point due to a peak in Bitcoin prices. The study focused on Bitcoin price data and sentiment analysis scores. Results revealed Twitter data as the dependent variable, showing no long-term relationship with Bitcoin prices. However, a significant and positive relationship was observed in the short term. This research contributes valuable insights into the intricate dynamics between social media sentiments and cryptocurrency pricing.

Alternative monetary policies and renewable energy stock returns

The aim of this study is to determine how monetary policy interacts with the financial sector specialising in renewable energy, especially since the implementation of Quantitative Easing (QE). Using EU data and the VAR approach incorporating the interest rate, representing monetary policy, an index of renewable energy stock prices, oil prices, technology and the VIX, this paper applies Granger causality, generalised impulse response functions and historical variance decompositions to explain this interaction. To account for the changes in monetary policy such as QE, structural break tests have been used to determine their effects on the variables, as the breaks correspond to the main QE events, the data has been divided into subsamples to reflect the possible differing effects of QE. The results suggest that monetary policy has only a limited effect overall on renewable energy stocks, as the long recent period of alternative monetary policies has been found not to influence renewable energy stock prices. More time-disaggregated analysis undertaken by incorporating structural breaks identifies a significant influence during some time periods depending on the type of monetary policy being conducted.

Rebalancing agri-food trade flows due to Russian import ban: the case of direct neighbors

In recent years, research on trade rebalancing in agri-food supply chains has gained prominence due to trade sanctions, supply chain disruptions, and vulnerabilities exposed by pandemics and conflicts. This study focuses on the recalibration of agri-food trade dynamics, using the 2014 Russian import ban as a case study. The ban significantly altered the structure of agri-food export destinations for affected countries, particularly those sharing a border with Russia (e.g., Poland, Finland, Estonia, Latvia, Lithuania). Employing a cross-section regression model and structural break tests, we assess the trade rebalancing process. Our findings reveal short-term trade rebalancing effects, primarily observed in product groups not traditionally considered main trade specializations. There is evidence that significant part of the lost trade was redirected to EU28 or EAEU countries and end up in the common trade areas of these countries. Furthermore, we argue that the Russian import ban initiated a long-term structural shift in export patterns for non-traditionally traded banned products, while rebalancing for traditionally traded products was significantly quicker.

Soccer Bubble: Is There a Speculative Bubble in the Price of International Soccer Players?

An exponentially increasing trend of players’ registration prices has taken place since 2012, and we test the hypothesis that this may be a speculative bubble, using a structural break test (Chow test) on a data set of international soccer players' registration prices, that ranges from 2007 to 2019. The test confirms the presence of an abrupt change in the trend of these prices since the year 2012, both for the whole data set and for subgroups based on the types of competition considered (Champions League, Europa League, and residual) and on the age of the players. We surmise that a possible root at the base of this trend is the need for soccer teams to cope with the Fair Play regulation, which has taken place exactly in the same years, and caused an excess of players’ trading as a means to comply with the obligation for teams to balance their books over time.

The Macroeconomic Effect of Oil Sector Crisis in Libya

This paper analyses and investigates the impact of the oil sector crisis on the Libyan economy from 2012 Q4 to 2022 Q3, using the Bai-Perron model and a structural vector autoregressive model (SVAR). The findings demonstrate that the multiple structural breaks test statistics approved that nearly all of the study's variables had a structural break at years around political division and political agreement. In contrast to inflation, the results of the impulse response functions (IRFs) indicate that the shock to oil revenue had a considerable negative impact on the money supply and exchange rate. Furthermore, the study reveals that, with the exception of claims on the government with the central bank, shocks to oil revenue are the major contributors to variance decomposition for all variables. The results reveal that oil revenue, in particular, accounts for roughly 16%, 13%, and 16% of the variance decomposition of the money supply, exchange rate, and inflation rate, respectively. As a result, the oil sector crisis is a controlling factor in the explanation of variations in these variables. The study concluded that oil sector revenues have a sensitive impact on the Libyan economy and that the latter has a strong tide with a secure environment and political stability.

A self-normalization test for structural breaks in a regression model for panel data sets

A self-normalization test for structural breaks in a regression model for panel data sets

Asymmetric and high-order risk transmission across VIX and Chinese futures markets

Due to the increasing attention to the linkages of financial markets in the context of ongoing economic globalization and financial integration, we delve into the transmission of risk across the Chicago Board Options Exchange Volatility Index (VIX) and volatility estimators of Chinese futures markets, where the VIX serves as an essential indicator of global financial market instability. By conducting tests on structural breaks in different order moments and risk contagions in different order co-moments via the regime-switching skew-normal model, we discover the existence of asymmetric volatility contagions through the correlation channel during the mid-2015 Chinese stock market crash and through the co-skewness channel during the first quarter of 2016. Interestingly, the risk contagion among the VIX and returns of the four futures primarily occurs via the co-skewness channel, rather than the commonly studied correlation channel. The joint structural break tests further reveal significant regime shifts in both first- and third-order moments for the VIX and negative realized semivariances of the four futures.

A review of the shares of the central bank from its creation up to the second world war

Purpose - The aim of this study is to observe the movements of the shares of the Central Bank of the Republic of Turkey during the foundation period and to estimate the events under the influence of these movements. An independent central bank is very important for countries to operate an independent monetary policy. In Turkey, the emphasis on "national banking" first came to the fore at the Izmir Economic Congress, and attempts were made to establish a central bank in 1927. In 1931, the Central Bank of the Republic of Turkey began its official activities. This was the most important step towards achieving economic independence in the Republic of Turkey and preserving the value of the Turkish Lira. This study examines the performance of central bank stocks over this period. Methodology-The data set consists of 1674 observations including daily closing prices for the period 04.01.1934-31.12.1939. The data were obtained from the La Bourse newspaper and the Cumhuriyet newspaper archive was used as a news source. First, the breaks in stock prices over this period are analysed using the Bai-perron test. The Eviews 12 program was used for this purpose. The second step is to examine whether there is a day-of-week effect in stock prices over the period in question. For this purpose, returns were calculated. The non-parametric Man-Whitney U test in SPSS 15 was used to test whether there was a significant difference between these returns on a daily basis. Findings- The results of the Bai-Perron structural break test showed that two breaks occurred on 01/08/1936 and 06/07/1938. Short and long-term increases in the value of the Central Bank shares were observed on these dates, which coincided with the achievement of the Montreux Convention and the victory of the National Army in Hatay. Moreover, these dates indicate risks associated with global conflict. During the second step, the compound return for share prices in the period of 1934-1939 was calculated, revealing the absence of any day-of-week effect. In other words, the results of the Man-Whitney U test show that there is no statistically significant difference between the groups. Conclusion- The Montreux Convention and the independence of Hatay, achieved during this period, had a positive impact on the stock, as the findings suggest. Furthermore, global war risks had a positive impact on the bank's shares. These results can be explained by the fact that the higher the risk, the more confidence is placed in the "national" bank. Finally, the shares of the Central Bank, symbolising the economic independence of the newly established Republic of Turkey, are relatively less sensitive and have low anomalies. This result is in line with expectations, given that financial markets are still underdeveloped. Keywords: Turkiye, banking, national banking, central bank. JEL Codes: B41, E58, G21.

Firm Value, Economic Shock, and Pitfalls of Approach Analysis: The Case of Indonesian State-Owned Bank

Economic shocks are believed to have a major impact on economic stability. The purpose of this study was to determine and analyze the effect of Liquidity, Leverage, Profitability and the Covid-19 pandemic on the firm value of state-owned banks listed on the Indonesia Stock Exchange (IDX). The data used is the financial statement data of state-owned banks listed on IDX for the 2011-2021 period. The population of this study are state-owned bank companies listed on the Indonesia Stock Exchange. Sampling in this study was carried out using purposive sampling method. Based on predetermined criteria, 44 data were obtained. The data analysis used in this research are Multiple Linear Regression Analysis and Data Panel, then at the end of analysis is also strengthened by Structural Break Test and Panel Cointegration Test. The results from Data Panel showed that Liquidity has a negative and significant effect on Firm Value, meanwhile Leverage and Profitability has insignificant effect on Firm Value. Structural Break Test and Panel Cointegration Test both strongly showed there is an impact the Covid-19 pandemic on Firm Value.

Unveiling the Relationship Between Oil Price and Exchange Rate: New Insight from Time-varying Versus Fixed Coefficient Cointegration

This study provides a brief analysis of time-varying cointegration between the INR–USD bilateral exchange rate and Brent crude oil prices in the post–subprime crisis period. Prior studies established this relationship using the assumption that the long-run relation is intertemporally constant. However, there is much recent evidence demonstrating that this assumption may not be feasible. To address this issue and to go beyond the restrictive time-invariant environment, we employed the time-varying cointegration framework of Bierens and Martins (2010) , which was assessed through orthogonal Chebyshev time polynomials. The result shows that the Rupee was decoupled from oil price shocks in the first two samples. However, the oil price pass-through effect will become stronger in the third and fourth samples. The endogenous structural break test suggests the presence of serious parameter instabilities due to fluctuations in oil prices and the exchange rate over the period under study. This indicates the ability of international crude oil prices to influence domestic economic activities through the exchange rate. Policymakers should consider this factor while making monetary and foreign exchange policies. JEL Codes: E44, G14, G15

Dynamics of innovation over time: Evidence from the Spanish business elite

Although Spain was not a leading country in technology, it generated numerous innovations, which must be analyzed in order to understand its particular development trajectory. This paper makes a contribution in this line by quantifying the innovation activity of the country's business elite between c. 1870 and 1970. In addition, we have sought to verify whether some of the well-known debated dynamics of innovation over time are fulfilled in this case. First with a descriptive analysis and then through the VAR technique, it has been found that innovation occurred in waves, as Schumpeter pointed out despite Kuznets; major innovations were followed by incremental advances; process innovations predominated in the early stages of modern development and product innovations gained preponderance later; and that indigenous innovation favored the importation of technology. A structural break test has identified sharp fluctuations in innovation, apparently related to institutional factors and relevant changes in the economy.

Unraveling Producer Price Inflation Pass-Through: Quantification, Structural Breaks, and Causal Direction

Abstract Producer price inflation has long been considered a leading indicator for consumer price inflation. However, the evidence supporting the cost-push theory of inflation over extended periods is inconclusive and lacks direct quantification. To address this gap, we employ structural break and causality tests, regression analysis, and local projection impulse-response functions. Our analysis allows us to precisely identify instances when producer prices lead consumer prices and quantify short-run and long-run pass-through rates. We find relatively robust evidence of a producer price pass-through rate between 8 and 12%. However, there are significant periods where unidirectional pass-through does not hold. Local projections reveal that producer price pass-through is small but persistent in states where producer prices lead consumer prices, and larger but shorter-lived in states where there is no causal directionality. Our findings enhance the understanding of producer price pass-through to consumer inflation, providing valuable insights for policymakers and market participants interested in accurately forecasting and managing inflationary pressures.

The dynamics of the relationship between stock returns and trading volumes: An emerging markets perspective during varying market conditions

The dynamic relationship between stock returns and trading volumes is examined during normal and crisis periods by a combining the Bai-Perron structural break test with Granger Causality test. The daily price and volume data of 4 important indices and Nifty Fifty index based companies listed on the National Stock Exchange of India Limited (NSE) are included in the study for the years 2005 to 2022. The results of this study give valuable insights into the influence of periods falling during two important crisis periods, the Global Financial Crisis and the Covid- 19 pandemic, on the nature of relationship between the variables under study. We find that the market is not permanently efficient and our findings indicate the nature of causal relationships to be closer to the postulates of adaptive market efficiency. Although the market appears efficient for the most part at the aggregate market level there are periods at sector level and firm level which are long enough for traders to profit from strategies that exploit associations between price and volume.

Testing Multiple Structural Breaks in the Oil Price–Stock Price Nexus in Asian Oil-Importing Countries During the Russia-Ukraine War

This study tests for structural breaks in the oil price–stock price nexus during the Russia-Ukraine War, by focusing on Asian oil-importing countries. We find evidence of structural breaks in these economies. Also, the regression results from the different regimes show that the spillover effects intensify in some countries but decline in others. The results are mixed in some countries and for the panel estimation.

The rise and fall of excess winter mortality in New Zealand from 1876 to 2020

Excess winter mortality (EWM) has been used as a measure of how well populations and policy moderate the health effects of cold weather. We aimed to investigate long-term changes in the EWM of Aotearoa New Zealand (NZ), and potential drivers of change, and to test for structural breaks in trends. We calculated NZ EWM indices from 1876 (4,698 deaths) to 2020 (33,310 deaths), total and by age-group and sex, comparing deaths from June to September (the coldest months) to deaths from February to May and October to January. The mean age and sex-standardised EWM Index (EWMI) for the full study period, excluding 1918, was 1.22. However, mean EWMI increased from 1.20 for 1886 to 1917, to 1.34 for the 1920s, then reduced over time to 1.14 in the 2010s, with excess winter deaths averaging 4.5% of annual deaths (1,450 deaths per year) in the 2010s, compared to 7.9% in the 1920s. Children under 5 years transitioned from a summer to winter excess between 1886 and 1911. Otherwise, the EWMI age-distribution was J-shaped in all time periods. Structural break testing showed the 1918 influenza pandemic strain had a significant impact on trends in winter and non-winter mortality and winter excess for subsequent decades. It was not possible to attribute the post-1918 reduction in EWM to any single factor among improved living standards, reduced severe respiratory infections, or climate change.

Kolmogorov–Smirnov type testing for structural breaks: A new adjusted-range based self-normalization approach

A popular self-normalization (SN) approach in time series analysis uses the variance of a partial sum as a self-normalizer. This is known to be sensitive to irregularities such as persistent autocorrelation, heteroskedasticity, unit roots and outliers. We propose a novel SN approach based on the adjusted-range of a partial sum, which is robust to these aforementioned irregularities. We develop an adjusted-range based Kolmogorov–Smirnov type test for structural breaks for both univariate and multivariate time series, and consider testing parameter constancy in a time series regression setting. Our approach can rectify the well-known power decrease issue associated with existing self-normalized KS tests without having to use backward and forward summations as in Shao and Zhang (2010), and can alleviate the “better size but less power” phenomenon when the existing SN approaches (Shao, 2010; Zhang et al., 2011; Wang and Shao, 2022) are used. Moreover, our proposed tests can cater for more general alternatives. Monte Carlo simulations and empirical studies demonstrate the merits of our approach.

Analyzing asymmetric ecological performance under structural change, technological innovation, and trade diversification: fresh insights from the USA.

The present research scrutinizes the influences of trade diversification, air transportation, technological innovation, and economic complexity on ecological footprint from 1990 to 2019. The findings of the both time series unit root (with and without structural break) tests confirm that none of a single variable is stationary more than the first difference. Furthermore, the Wald and nonlinear autoregressive distributed lag bound methods check asymmetry and long-term cointegration relationship between the intended variables, respectively. Moreover, this study uses the nonlinear autoregressive distributed lag model to estimate the short-run and long-run coefficients/elasticity of the ecological footprint function. Following the empirical evidence, the findings revealed that positive (negative) components in trade diversification curtail the ecological footprint in the long-run. In addition, a positive shock in air transportation leads to an increase in ecological footprint in the long-run. Nevertheless, a negative shock in air transportation exerts a significant and adverse influence on the level of ecological footprint in the long-run. Furthermore, a positive (negative) shock in technological developments significantly reduces environmental pollution in the US economy in the long-run. Besides, the outcomes from economic complexity discovered a positive shock will significantly overcome the pressure on the environment in the long-run. However, in the short-run, it is observed that negative shock in trade diversification will lead to increase the ecological footprint level in USA. Similarly, a positive shock in air transportation will lead to increase the pollution level in the short-run. In contrast, a negative shock in air transportation will lead to reduce the pressure on the environment in the short-run. Besides, in terms of policy realization, the present research recommends adopting trade synchronization, harmonic trade strategies, and investment in technological innovations to diminish the existing level of ecological footprint in the region. For sustainable development, this study put forward for instantaneously encouraging the expansion of the digital economy and reducing air pollution, accelerating the green transformation, and impelling the industrial agglomeration process in the USA.