Cointegration Analysis Research Articles

Statistical analysis of the impact of profitability in the manufacturing and service sectors on the UK Economy’s Growth

The UK economy's performance is closely tied to the profitability of its Manufacturing Sector (MS) and Service Sector (SS). Understanding these sectors' dynamics is crucial for effective policymaking. Integrating sustainable practices in both sectors enhances profitability and operational efficiency while contributing to economic growth and reducing environmental impact. This study investigates the relationship between UK economic growth and profitability in the MS and SS from 1998 to 2022. The primary objective is to examine how profitability influences the UK's growth trajectory. Employing various statistical methods, including correlation, regression, cointegration, and variance decomposition analyses, the study aims to provide insights into the interplay between GDP and sector profitability. A quantitative approach using secondary data on GDP and profitability from reliable sources is adopted. Correlation analysis explores relationships between these variables, while regression analysis assesses the impact of MS and SS profitability on GDP. Cointegration tests identify long-term equilibrium relationships, and variance decomposition analysis reveals the variability of GDP and sector profitability over time. The correlation matrix indicates positive associations between GDP and both sectors, with the SS showing a stronger correlation. Regression analysis confirms significant effects of both sectors on GDP, with the SS exerting a positive influence and the MS a negative impact. Cointegration tests highlight long-term stability, and variance decomposition analysis emphasizes the dynamic nature of these relationships. This study elucidates the intricate link between UK economic growth and sector profitability, underscoring the SS's critical role in driving expansion while addressing challenges in the MS. Policy recommendations include enhancing productivity and innovation in both sectors, informed by evidence-based decision-making. Overall, the study contributes valuable insights into the UK economy's growth dynamics, guiding sustainable economic development strategies.

Effect of capital market on economic growth: An analysis using the autoregressive distributed lag (ARDL) approach

The limits testing method known as ARDL (Autoregressive Distributed Lag) examines Nigerian capital market improvement and monetary development. Relevant indicators about the capital market are taken into account during analysis while considering influencing factors as well. This study aims to explore the relationship between the capital market and financial development in Nigeria. The study’s theoretical framework, which is based on the ARDL framework, includes both short- and long-term dynamics. The data used in this analysis was collected from the Central Bank of Nigeria (CBN), the World Bank database, and the Nigerian Stock Exchange (NSE). In this study, the Nigerian All-Share Index, foreign direct investment, currency exchange rates, and inflation rates are the free factors, while the increase in GDP is the dependent factor. Through cointegration analysis using the ARDL framework, it was discovered that in Nigeria there’s a lengthy equilibrium correlation between economic expansion and financial development. The coefficients determined allow for a deeper understanding of how capital market factors affect economic growth over time. Furthermore, utilizing an error correction model derived from the ARDL analysis offers insight into both brief dynamics and modified speed toward reaching a lasting state of balance. By utilizing the ARDL approach, this study adds to the continuing discourse surrounding how capital markets impact growth in Nigeria. Its empirical evidence provides valuable knowledge for policymakers and stakeholders looking to utilize capital markets effectively toward achieving long-lasting economic development within the country.

Linking Human Development Index, Urbanization, Economic Growth and the Ecological Footprint: The Case of MINT Countries

The main purpose of this study is to investigate the relationship between human development index, urbanization, economic growth and ecological footprint in MINT countries (Mexico, Indonesia, Nigeria and Turkiye) for the years 2003-2022. First of all, the cointegration relationship between human development index, urbanization, economic growth and ecological footprint was examined with Gengenbach, Urbain and Westerlund (2016) panel cointegration. As a result of the cointegration analysis, it was concluded that the variables were cointegrated. The long-term relationship was estimated by the DOLSMG method. According to the DOLSMG estimator, it was determined that a 1% increase in the human development index in MINT countries reduced the ecological footprint by 1.89%, a 1% increase in urbanization increased the ecological footprint by 0.22%, and a 1% increase in economic growth increased the ecological footprint insignificantly. Secondly, the relationship between human development index, urbanization, economic growth and ecological footprint was examined with Dumitrescu and Hurlin (2012) panel causality analysis. According to the findings, there is a bidirectional causality relationship between ecological footprint and urbanization in MINT countries. Additionally, the findings of the study show that there is a unidirectional causality relationship from ecological footprint to human development index and a unidirectional causality relationship from economic growth to ecological footprint.

Static and Dynamic Modeling of Non-Performing Loan Determinants in the Eurozone

The issue of non-performing loans (NPLs) in a bank’s portfolio is important for a bank’s stability and sustainability. Their increased presence indicates a potential worsening of the economy and a lower quality of the bank’s assets. We estimated determinants of non-performing loans in the Eurozone for quarterly data 2015–2020. The results confirmed spatial spillover effects within Eurozone countries, which means that when a shock happens in one country in the Eurozone, it will also affect the other economies of the Eurozone area. Based on the Hausman test, a fixed-effects model was chosen as appropriate and showed that bank-specific and macroeconomic determinants significantly affect NPLs in these economies. In relation to previous studies that dealt with this issue, a co-integration analysis was introduced. A significant impact of return on assets, return on equity, and the loan-to-deposit ratio, as well as the gross domestic product, inflation, and exchange rate on NPLs in the short run and long run, was confirmed using a Pooled Mean Group (PMG) estimator. Bank management should customize credit policy based on both internal and external conditions to improve their performance, focusing on enhancing profitability and maintaining a lower loan-to-deposit ratio to reduce NPLs. The research suggests that a higher gross domestic product (GDP) growth rate is associated with fewer NPLs, while inflation uncertainty and a volatile exchange rate can increase NPLs, highlighting the importance of adjusting strategies to the macroeconomic landscape.

How population ageing affects FDI flows: a tale of South Korea

The neoclassical theory predicts that population ageing would reduce net FDI inflows, which would nullify government efforts to attract FDI. There is a complete paucity of literature on the dynamic relation between population ageing and FDI flows for South Korea. This is a distinctive attempt to test the hypothesis that population ageing reduces net FDI flows (% of GDP) in South Korea. The results of the ARDL bounds testing approach to cointegration analysis under structural breaks provide clear evidence of non-linearity between population ageing and net FDI flows (% of GDP). Support for the neoclassical hypothesis is established only in the short-run.

Energy factors affecting environmental pollution for sustainable development goals: The case of India

This study investigates the nexus between electricity consumption, fossil fuel dependency, renewable energy adoption, population growth, trade activities, economic growth, and environmental pollution in India. The primary objective is to understand how these factors interrelate and influence each other, focusing on their implications for sustainable development. The study used data from the World Bank from 2000 to 2023; the methodology adopted includes vector autoregression modeling, Granger causality tests, cointegration analysis, impulse response functions, and variance decomposition. These econometric techniques were selected due to their ability to capture dynamic relationships, determine causality, and identify long-term equilibrium among the variables. The findings reveal that economic growth significantly increases electricity consumption and fossil fuel usage, leading to higher carbon dioxide emissions. On the other hand, renewable energy adoption reduces environmental pollution. The study also highlights the complex interplay between population growth, urbanization, and trade activities in shaping India's energy demand and environmental outcomes. The implications of these findings are critical for India's sustainable development. The results suggest that while economic growth is essential, it must be balanced with sustainable energy practices to mitigate environmental pollution. The findings emphasize the need for policy interventions that promote renewable energy, enhance energy efficiency, and enforce environmental regulations. Recommendations include accelerating renewable energy adoption, implementing stringent energy efficiency standards, and developing integrated policies that simultaneously address the economic, energy, and environmental dimensions. These actions will help India achieve a sustainable balance between economic growth and environmental protection, ensuring a healthier future for its population.

Climate policy uncertainty and economic growth, a moderating role of crude oil price changes: evidence from the Asian economies

Purpose This study aims to inspect the impact of US climate policy uncertainty (CPU) on the economic growth of Asian countries with the moderating role of crude oil price (COP) changes. Design/methodology/approach The Im-Pesaran Sin and Fisher-type tests are used for stationarity check, while Kao and Pedroni tests are used for cointegration analysis. The Hausman test is applied for model selection, where pooled mean group autoregressive distributed lag (PMG/ARDL) has been selected and applied. Besides, the fully modified ordinary least squares is also used for robustness analysis. Additionally, the literature review and descriptive statistics have been used. Findings The main findings disclosed that US CPU negatively impacted the economic growth of Asian economies with high significance in the long run whereas insignificant in the short run. The results further concluded that COP positively affected economic growth both in the short and long run. Furthermore, the results also revealed that COP significantly and positively moderates the relationship between CPU and COP in the long and short run. Originality/value The study is the first of its kind to examine the impact of the US CPU on the economic growth of Asian economies. Second, it further revealed the moderating role of COP between US CPU and economic growth. Third, a large panel of data from Asian countries has been considered. Fourth, the study adds to the current literature by using the PMG/ARDL model to determine the impact of US CPU on economic growth. Additionally, this study focuses on the US CPU because it is a developed country playing a significant role in energy and climate issues, and has been very uncertain.

Short-term and long-term cointegration analysis of foreign direct investment and value added generation in the manufacturing industry

The contraction of manufacturing economic activity in Latin American countries has been affected by the health crisis in the last few years. This phenomenon has negatively impacted the Latin American countries’ economies. In order to evaluate the impact of the manufacturing economy, this research integrates the impact of Foreign Direct Investment (FDI) on the growth of the Ecuadorian manufacturing sector, from 1981 to 2019, considering the role of the state through public spending using cointegration. The results are not consistent considering the empirical framework used; thus, FDI has a negative and significant influence on the manufacturing sector. Also, the manufacturing sector has a strong relationship with FDI in the short run and a less significant one in the long run. The results presented in this research suggest promoting domestic and FDI in the manufacturing sector, not only towards overexploited and monopolized sectors such as mining and telecommunications.

Liquidity Preference, Interest Rate Spread, and the Transformation of the US Financial System

This article examines empirical patterns of interest rate spreads between yields on long-term corporate bonds and the Federal Funds Rate (FFR) in the United States over the period from 1960 through 2023. Since the early 1980s, discernible patterns of interest spreads have emerged, revealing marked deviations in corporate bond yields from the FFR. We focus on financial market forces particularly arising from financial intermediaries in analyzing these patterns. To this end, the article develops a theoretical framework based on the concept of liquidity preference in Keynes’s 1937 papers and the works of Hyman Minsky and Victoria Chick. We also conduct cointegration analyses for corporate bond yields, the FFR, and financial intermediaries’ leverage ratios. Overall, our empirical results indicate that the relationship between the FFR and corporate bond yields—particularly with respect to their long-run relationship and interest rate markups—has markedly changed since the 1980s and is significantly affected by financial intermediaries’ asset/capital leverage ratios. As a policy implication, regulatory reforms aimed at enhancing the Federal Reserve Bank’s capacity to steer long-term rates and contain intermediaries’ balance sheet leverage, particularly those centered on risky asset trading to increase gains from price differentials, are required JEL Classification: B26, E11, E43, E52

Non-linear effects of three core mineral resources, energy uncertainty, and inclusive digitalization on economic growth: A comparative analysis of US and China

This study examines the asymmetric effects of mineral resources (oil, coal, and gas), digitalization, and energy uncertainty on economic growth in China and the USA. Using quarterly data from 2000 to 2022, we employ the nonlinear autoregressive distributed lag (NARDL) model to analyze these relationships. The study begins by conducting unit root tests, cointegration analysis, and asymmetry tests to establish robust connections between the variables. The long- and short-run results confirm the presence of asymmetry, as positive and negative shocks in oil rents, digitalization, and energy uncertainty affect economic growth differently. Notably, positive shocks in oil rents and digitalization favour economic growth, indicating that increases in oil rents and digital inclusion contribute to economic development. Conversely, positive shocks in energy uncertainty tend to reduce economic growth. Interestingly, the findings for China and the USA show similar asymmetric patterns; coal and natural gas rents positively influence economic growth, while the control variables suggest that capital and labour increase contribute to both countries' development. The error correction terms, 0.731 (73.1%) for China and 0.625 (62.5%) for the USA, indicate the annual rate at which asymmetric discrepancies are corrected. All diagnostic tests confirm the suitability of the NARDL model for this study. Finally, the study reveals significant bi-directional causality among all pairs of variables.

Crude oil price behavior and the adoption of sustainable measures in companies before and after COVID-19

The objectives achieved in the Paris Agreement to reduce greenhouse gas emissions and reduce dependence on fossil fuels have caused, in recent years, a growing importance on sustainability in companies in order to reduce Environmental, social and economic impacts. This study is focused on understanding how the variation in West Texas Intermediate crude oil prices affects the Dow Jones Sustainability Index, and therefore the companies included in it, and vice versa. The research aims to examine the statistical properties of both indices, using fractional integration methods, the fractional cointegration vector autoregressive (FCVAR) approach and the continuous wavelet transform (CWT) technique. The results warn of a change in trend, with the application of extraordinary measures being necessary to return to the original trend, while the analysis of cointegration and wavelet analysis measures reflect that an increase in those adopted based on sustainability by the different companies that make up the index imply a drop in the price of crude oil.

Exchange Rate Pass-Through into Import Prices: Evidence from Türkiye during 2010-2020 Period

This article analyses pass through of exchange rates into the import prices for an emerging economy, Türkiye, which faced severe fluctuations in the nominal exchange rate in the recent years, and experienced significant increases in domestic prices. The Turkish economy continues to struggle due to high inflationary pressures and a steep Turkish Lira (TL) devaluation particularly after 2017. This study aimed at examining the relationship between exchange rate and import prices using quarterly data for the period of 2010q1-2019q1, while the empirical methodology is framed in the multivariate cointegration analysis, along with the application of the Johansen cointegration test and the development of an autoregressive error correction vector for the determination of the pass-through in the long term. Our findings show that 85.6% of the fluctuations of the nominal exchange rate are transmitted to the import prices in the mentioned period, slower than the findings of the study by Akgunduz and his colleagues as the difference of import prices from its long-run relationship implied by the real exchange rate is likely dominated by energy prices in a strong manner. Second significant finding derives from the parameter that measures the speed of the pass-through, whose estimate has been found as -0.0125, inferring that the adjustment in short-term imbalances between import prices and the nominal exchange rate through changes in import prices is rather slow.

Will Southeast Asia be the next global manufacturing hub? A multiway cointegration, causality, and dynamic connectedness analyses

We propose a novel framework to examine relationships among drivers of economic growth in Southeast Asia, a region poised to become a significant manufacturing destination. However, unbalanced economic growth among countries poses risks to multinational companies considering offshoring decisions. Our two-stage framework uses multi-way cointegration analysis and a vector error correction model (VECM) to investigate critical drivers of economic growth. We apply a QVAR model to evaluate dynamic connectedness and spillover effects of offshoring decisions. Using World Bank data, our results show that Southeast Asian countries are interconnected through complex relationships featuring multi-way cointegration and dynamic connectedness, informing evidence-based policy.

Cointegration Relationship between Portfolio Investments and Investment Instruments in Türkiye

As a result of globalization, the removal of barriers to international capital has created a source for countries in need of capital. These capital sources referred to as short-term capital movements or simply portfolio investments, provide significant benefits to the economy when they first enter the country. Investment instruments are instruments used by those who have excess capital in their hands in order to make a profit. The aim of this study is to reveal the cointegration relationships between portfolio investments and investment instruments in Türkiye. The motivation for this study is that there are few studies in the literature that examine cointegration relationships between all investment instruments and portfolio investments. The study comprises quarterly data covering the period from 2009Q1 to 2023Q2. The data has been obtained from the CBRT EVDS system. According to the results of ARDL cointegration analysis; there is a cointegration relationship between the variables both in the short and long run. According to these results, in Türkiye, investment instruments do not significantly affect portfolio investments, but portfolio investments significantly affect investment instruments.

A Panel Cointegration Analysis of Pakistan’s Trade: Linkages with Regional and EU Economies

A Panel Cointegration Analysis of Pakistan’s Trade: Linkages with Regional and EU Economies

ASSESSING THE POTENTIAL OF RAILWAYS TO REDUCE CO2 EMISSIONS: A COINTEGRATION STUDY OF ESTONIA'S TRANSPORT SECTOR

This The transportation sector is a major contributor to CO2 emissions, playing a significant role in exacerbating climate change. Railways, known for their greater energy efficiency compared to other modes of transport, present a viable solution for mitigating emissions, particularly in countries like Estonia. In 2014, transport-related CO2 emissions in Estonia were recorded at 12.7%, marking an overall upward trend since 1995, despite experiencing some fluctuations over the years. This study aims to explore the long-term equilibrium relationship between CO2 emissions from transport and railway passenger kilometers in Estonia through the application of cointegration analysis. Utilizing Engle-Granger and Phillips-Ouliaris cointegration tests, the analysis reveals a stable, long-term equilibrium relationship between the two variables. The findings suggest that an increase in railway passenger kilometers could potentially lead to a reduction in CO2 emissions from transport. These results underscore the importance of enhancing railway infrastructure and usage as a strategic measure to curb transportation-related emissions and contribute to environmental sustainability.

The impact of industry structure on the value of national logistics costs/gross domestic product: Evidence from China

The proportion of national logistics costs to Gross Domestic Product (NLC/GDP) serve as a valuable indicator for estimating a country’s overall macro-level logistics costs. In some developing nations, policies aimed at reducing the NLC/GDP ratio have been elevated to the national agenda. Nevertheless, there is a paucity of research examining the variables that can determine this ratio. The purpose of this paper is to offer a scientific approach for investigating the primary determinants of the NLC/GDP and to advice policy for the reduction of macro-level logistics costs. This paper presents a systematic framework for identifying the essential criteria for lowering the NLC/GDP score and employs co-integration analysis and error correction models to evaluate the impact of industrial structure, logistics commodity value, and logistics supply scale on NLC/GDP using time series data from 1991 to 2022 in China. The findings suggest that the industrial structure is the primary factor influencing logistics demand and a significant determinant of the value of NLC/GDP. Whether assessing long-term or short-term effects, the industrial structure has a substantial impact on NLC/GDP compared to logistics supply scale and logistics commodity value. The research offers two policy implications: firstly, the goals of reducing NLC/GDP and boosting the logistics industry’s GDP are inherently incompatible; it is not feasible to simultaneously enhance the logistics industry’s GDP and decrease the macro logistics cost. Secondly, if China aims to lower its macro-level logistics costs, it must make corresponding adjustments to its industrial structure.

The Relationship between Agricultural Production, Agricultural Exports and Economic Growth in Türkiye: Cointegration and Causality Analysis

This study aimed to assess the influence of agricultural production and agricultural product exports on economic growth within the Turkish economy. To achieve this, annual data spanning from 1980 to 2022 were utilized to construct a model, employing the ARDL Bounds Test to discern the long-term cointegration relationship among variables and the Fourier Toda-Yamamoto Causality Test to determine causality relationships. The empirical findings from the ARDL Bounds Test indicate that increases in both agricultural production and agricultural product exports positively impact economic growth. Moreover, results from the Fourier Toda-Yamamoto Causality Test unveil a two-way causal relationship between agricultural production and GDP, while a one-way causality relationship is observed between agricultural product exports and GDP. Despite the declining share of the agricultural sector in GDP over the years in Turkey, these findings underscore the enduring importance of agriculture in driving economic growth and creating value. As potential solutions to Turkey's balance of payments challenges, strategies such as boosting domestic demand, expanding the domestic market, and augmenting agricultural sector contributions to foreign exchange earnings through agricultural export subsidies are proposed. Furthermore, efforts to mitigate income disparities between the agricultural sector and other sectors could foster socio-economic development in rural areas, thus promoting a more equitable distribution of national income across society.

UKRAINE AND REPUBLIC OF MOLDOVA IN COOPERATION WITH THE EU AFTER 2014 YEAR: MODELS AND IMPACTS

Abstract. The paper is devoted to solving the problem of co-integration between Ukraine and the Republic of Moldova in the context of foreign trade. The study of co-integration makes it possible to determine common trends in the development of foreign trade and, based on the analysis of the dynamics of the development of one country, to forecast the development of foreign trade in another country. The study of the main indicators of foreign trade was carried out: volumes of exports, imports, trade balance. The share of export-import operations with the EU in the total volume of foreign trade of Ukraine and Moldova was chosen as the studied indicator. The article proposes to consider cointegrations both between long-term trends, which characterize the trend of foreign trade development, and between short-term trends, which reflect the cyclical component. A co-integration research mechanism has been developed, which includes three blocks: a block of analysis of literary sources, a block of mathematical tools, and a block of applied use. The use of decomposition models, trend models, spectral analysis, orthogonality indicators and pairwise correlations is proposed as a mathematical toolkit. Using the Foster-Stewart method, an analysis of the presence of a trend in the variance and average was carried out. The above calculations showed the presence of a trend and cyclical component, which allowed the use of additive decomposition models. For the study of long-term trends, a linear trend model was chosen for both Moldova and Ukraine. A spectral analysis of the cyclic component was carried out, which made it possible to identify five main harmonics with a unique frequency and amplitude. The combination of these harmonics caused 95.8% and 90.4% of the cyclical component for Moldova and Ukraine, respectively. An analysis of cointegration between short-term and long-term trend functions was conducted, which revealed medium cointegration in the long-term trend and high cointegration in the short-term trend. The cointegration analysis of the indicator values proved a high degree of cointegration for both long-term and short-term trends.

Exploring the stock market development and sustainable development nexus: insights from emerging market economies

PurposeSustainable development is considered a key factor in addressing environmental issues, global inequalities and poverty. This study aims to investigate the impact of stock market indicators on sustainable development across 16 emerging markets from 2003 to 2020.Design/methodology/approachThe research uses causality and cointegration analyses to explore the relationships between stock market indicators and sustainable development.FindingsUnivariate causality analysis reveals a bidirectional causal relationship between the stock market turnover ratio and sustainable development, as well as a unidirectional relationship from sustainable development to stock market capitalization and total value traded. Panel-level cointegration analysis suggests that only stock market capitalization has a weak positive influence on sustainable development. However, the impact of stock market indicators on sustainable development varies significantly among countries, as revealed by country-level cointegration analysis.Research limitations/implicationsWhile this study provides valuable insights, it is not without limitations. The findings are limited to the selected emerging markets and the specified timeframe (2003–2020). The complexity of factors influencing sustainable development suggests the need for further exploration in diverse contexts.Practical implicationsUnderstanding the nuanced relationships between stock market indicators and sustainable development can offer valuable insights for policymakers, investors and stakeholders.Originality/valueThis research contributes to the existing literature by examining the multifaceted connections between stock market indicators and sustainable development, focusing on country-specific causality relationships. The study highlights the reciprocal nature of this relationship, where financial market development can both influence and be influenced by a country's progress toward sustainability. This approach provides a more nuanced understanding of the complex interaction between stock market maturity and sustainability goals.