Granger Causality Test Research Articles

Inflation, exchange rate, and economic growth in Ethiopia: A time series analysis

This study aims to investigate the dynamic relationship between inflation, exchange rate, and economic growth in Ethiopia for the period 1991–2020. This is because of rampant inflation, continuously devalued domestic currency, and the declining growth rate of the country due to violent conflicts. The study uses the ARDL model for short-run and long-run dynamics, the Bound test of co-integration to confirm if the target variables go together or not, and Granger causality tests are performed to detect dynamic effects. The ARDL model estimates suggested that inflation and exchange rate are negatively related to economic growth. This implies that an increase in the price level reduces the production of goods and services, while devaluation has a negative impact by reducing imports of intermediate goods. Thus, increasing domestic resource mobilization, improving regulatory quality, and deepening the financial sector can be viewed as solutions to decrease chronic foreign exchange shortages, lower inflation, and boost economic growth rates.

Will peer-to-peer online lending affect the effectiveness of monetary policy?

Online lending is a product of digital transformation, which has had a profound impact on the traditional money market. This paper discusses the impact of peer-to-peer (P2P) online lending on the effectiveness of monetary policy. Through the bootstrap sub-sample rolling-window Granger causality tests show that P2P has both positive and negative impacts on the money supply (M2). The positive impact of P2P on M2 indicates that online loans increase the amount of money supply. The negative impact of P2P on M2 shows that it may cut the money supply, thus weakening the monetary policy effectiveness. The general equilibrium model is inconsistent with these results, which underlines a positive effect from P2P to M2. In turn, the negative impact points out that the adjustment of monetary policy will hinder the development of P2P. The negative impact of M2 on P2P indicates that through the regulation of money supply, the online lending market can be correctly guided to prevent financial market from getting out of control. Through the supervision of online lending industry, we can accurately grasp the development of the internet financial industry and reduce its impact on monetary policy.

The Relationship between Tourism, Financial Development and Economic Growth in MP

Tourism and economic growth in India from 1960 to 2014 are examined in this research, takinginto account the relative importance of financial development in that time period. It has been shown thatboth the long-term and short-term development of India's economy is enhanced by inbound tourism. Alsofound is a long-term one-way Granger causality between tourism and economic development.Furthermore, the outcomes of the Granger causality test indicated a one-way association between twovariables ranging from GDP growth to tourist development. The findings support the rise of economicdriventourism in MP.

Infant Mortality, Education and Financial Development: Evidence from World Level Data Using Heterogeneous Panel Granger Causality Analysis

Education and financial development are crucial factors in achieving better health, including the reduction of infant mortality. This article investigates the causal relationship between education, financial development and infant mortality rate using annual data for 81 countries for the period 1980–2015 and employing the Emirmahmutoglu and Rose (2011) Granger causality test procedure for heterogeneous mixed panels. The results show that, individually and jointly, education and financial development Granger cause infant mortality in some countries across the major regions of the world. This suggests that improved education and a developed financial system are likely to reduce the infant mortality rate. JEL Codes: C33; E25; I

Assessing Fiscal Sustainability with Panel Unit Root, Cointegration, and Granger Causality Tests: Evidence from the Broader Groups of Countries

Abstract The question of fiscal sustainability of countries has become one of the central topics in economic policy, especially in times of increasing public debts. One way to assess fiscal sustainability is to examine compliance with the intertemporal budget constraint, which involves testing the stationarity of government revenues and expenditures, the primary budget balance, and the first differences of public debt. Part of this approach includes testing cointegration and causality among different pairs of variables. Under this approach, the paper is focused on both first- and second-generation panel unit root tests, cointegration, and Granger causality test. This paper focuses on assessing the fiscal sustainability of four panels of countries divided by continents: Europe, Asia and Oceania, Africa, and Latin America, the Caribbean, and North America. It has been found that fiscal sustainability is present in all the broader groups of countries considered but in a weak form. Fiscal sustainability was confirmed when considering a constant in calculations, while deviations were observed among groups of countries when both a constant and trend were considered. The study serves as a starting point for a more extensive analysis of fiscal sustainability. For more accurate findings, it would be necessary to categorize countries into smaller and economically more homogeneous groups and analyze them using other fiscal sustainability methods as well.

Geo-political risks, uncertainty, financial development, renewable energy, and carbon intensity: Empirical evidence from countries at high geo-political risks

Geopolitical risks and uncertainty negatively affect the economy and environmental quality. High geopolitical risks and high uncertainty increase carbon emission intensity by increasing dependence on fossil fuels. However, research in this area is still in its infancy and more empirical evidence is needed to justify the causal link. This research examines the causal mechanism linking geopolitical risk, uncertainty, financial development, renewable energy, and carbon intensity in a sample of eighteen countries with high geopolitical risk between 1985 and 2021. The selected countries have a high intensity of geopolitical risks due to internal and external conflicts that have worsened in recent years. We employ cointegration techniques from Hatemi-J [1] and Maki [2] in the presence of structural breaks, fully modified ordinary least squares (FMOLS), canonical cointegration regression (CCR) models, and static time-variant Granger causality (1969) test. We find robust empirical evidence that geopolitical risk, financial development, renewable energy, uncertainty, and carbon intensity have a cointegrating relationship in the presence of structural breaks. Results from FMOLS and CCR models indicate that carbon intensity responds differently to changes in geopolitical risk, renewable energy, uncertainty, and financial development. Environmental and energy policymakers should mitigate carbon intensity through long-run rather than short-run mechanisms to achieve SDG12 and SDG13.

Exchange Rate Volatility and Foreign Remittances in SAARC Countries

This study explores the relationship between exchange rate volatility and foreign remittances on the economies of SAARC countries over the period 2000 to 2023. With the ongoing debate among policymakers and researchers on this subject, the study employs the Cross-Sectional Autoregressive Distributed Lag (CS-ARDL) model to analyze data from eight SAARC nations. The variables considered include remittances, foreign reserves, trade, foreign direct investment (FDI), political stability, and GDP growth, sourced from the World Development Indicators (WDI), while exchange rate volatility is calculated using the GARCH method. The analysis involves two models. In the first model, remittances serve as the dependent variable, with exchange rate volatility, GDP growth, trade, inflation, and political stability as independent variables. In the second model, exchange rate volatility is the dependent variable, with remittances, GDP growth, trade, inflation, foreign reserves, and FDI as the independent variables. The results indicate both long-run and short-run relationships between exchange rate volatility and foreign remittances in SAARC countries. The findings show that remittances are positively associated with GDP growth and inflation, while exchange rate volatility, trade, and political stability have negative relationships with remittances. Additionally, exchange rate volatility has a positive association with inflation, while GDP growth, remittances, trade, FDI, and foreign reserves exhibit negative associations with exchange rate volatility. Granger causality tests reveal complex bidirectional and unidirectional causal relationships among the variables. These insights offer valuable implications for policymakers aiming to manage exchange rate volatility and enhance remittance inflows in the SAARC region.

Is There a Relationship between Macroeconomic Variables and Stock Market Indices in Bosnia and Herzegovina?

Abstract The economic growth and development of a country are reflected in many aspects, one of them being the stock market indices. The purpose of the article is to examine and determine the relationship between selected macroeconomic variables and stock market indices in Bosnia and Herzegovina (BiH). Using quarterly data over the 2010q1-2019q4 period, a cointegration analysis was applied to model this relationship. The Vector Error Correction Model (VECM) was used to explore the short-run relationship as well as the long-run relationship. The article examined the predictive ability among variables of interest by applying the Granger causality test. The results indicate a stable long-run relationship between the analysed macroeconomic variables and stock market indices in BiH, while no short-run relationship was found. The results contribute to the scientific discussions about the relationship between selected macroeconomic variables and representative stock market indices in BiH which considers their direction and strength.

A study on the impact of investor sentiment on individual stocks based on text mining

This paper investigates the influence of investor sentiment on individual stock prices through text mining. By reviewing relevant theories and literature, we utilized web crawler technology to collect comment data from the Eastmoney Stock Bar, constructing a comprehensive index of investor sentiment and an index of investor sentiment for individual stocks. Employing the VAR model and Granger causality test, we analyzed the correlation and dynamic relationship between investor sentiment and stock price fluctuations. The study found that stocks of different types of financial institutions react differently to investor sentiment, and that investor sentiment significantly affects stock prices. This paper puts forward corresponding policy recommendations, which can help market investors make scientific decisions and promote the healthy development of the market.

Social sharing of emotion during the collective crisis of COVID-19.

We collected data from two sources - social media and online questionnaires - to investigate the emotional consequences of social sharing during the COVID-19 pandemic. Study 1 tracked and analysed sentiment of tweets posted over the course of a month in the crisis period and found that users who tweeted more frequently about COVID-19 expressed decreasing negative sentiment and increasing positive sentiment over time. Granger causality tests confirmed that this association was better interpreted in the forward direction (sharing levels predicting sentiment) than in the reverse direction (sentiment predicting sharing levels). Study 2 focused on immediate emotional consequences of sharing COVID-19-related events and found that participants reported improved overall affect about an event after sharing it, especially when that event was a personal experience rather than a news story. Reported positive feelings about both kinds of events were also significantly higher after sharing. Taken together, both studies suggested that social sharing is linked with emotional relief and may therefore help people to deal with their negative experiences during a persistent collective crisis.

Achieving and Sustaining the Green Economy and It Potential Benefit to the Economic Growth of Sierra Leone: Empirical Analysis

The study examines the possible advantages of establishing and maintaining a green economy in Sierra Leone, focusing on social progress, environmental sustainability, and economic growth. The research aims to provide a scientifically grounded and research-based understanding of the process of transitioning to a green economy and its effects on Sierra Leone's economic development by examining important sectors like aquaculture, fisheries, and agriculture. This empirical study looks at the relationship between fishing, aquaculture, and agriculture and Sierra Leone's economic growth from 1990 to 2018 using regression models and statistical analyses. The study looks at the relationships between green economic practices and economic growth using a number of techniques, including correlation, Granger causality, and unit root tests. According to the findings, Sierra Leone urgently needs a green economy because of its vulnerability to environmental degradation, climate change shocks, and macroeconomics issues. The study accentuates the possible advantages of agriculture, fishing, and aquaculture for economic expansion and upswing. It also emphasizes how important it is to adopt sustainable practices and green technologies to reduce the risks associated with climate change. The recognition of the links between green economic principles and Sierra Leone's economic growth has increased as a result of this study. To facilitate the shift to a green economy, the study suggests implementing economic and fiscal policy tools, stepping up current initiatives, and enforcing regulations. In addition, building a supportive investment climate, encouraging stakeholder engagement, and fostering institutional collaboration are essential to establishing and maintaining a green economy in Sierra Leone. The analysis examines the potential advantages of establishing and maintaining a green economy in Sierra Leone, focusing on social progress, environmental sustainability, and economic growth.

Bitcoin: a Ponzi scheme or an emerging inflation-fighting asset?

Under the dual impact of the COVID-19 pandemic and the Russian-Ukrainian conflict, the excessive stimulation of monetary policy continuously pushes up global inflation (INF). Therefore, this article explores whether Bitcoin can serve as a safe haven for INF. We apply the rolling-window Granger causality test to solve the issue of parameter instability in vector autoregression (VAR) systems and investigate the time-varying interaction between INF and Bitcoin price (BP). The negative influence of INF on BP means a high inflation shock causes BP to decline, indicating that Bitcoin cannot be a safe asset against INF. This is because investors have decreased their willingness to hold Bitcoin under the high INF expectations and cause BP to fall. This finding is not supported by the Intertemporal Capital Asset Pricing Model, emphasising that INF positively impacts BP. Conversely, BP has positive and negative impacts on INF. The positive effect highlights the effectiveness of Bitcoin in predicting INF fluctuations, but economic factors could undermine this effectiveness. In the context of economic stagnation and market turmoil, investors can adjust their portfolio investments based on Bitcoin. The government should utilise the trend of BP to regulate the dynamics of INF to reduce uncertainty in the financial system.

The mechanism and path of pollution reduction and carbon reduction affecting high quality economic development - taking the Yangtze River Delta urban agglomeration as an example

In the wake of the COVID19 pandemic, what is crucial for the continued promotion of ecological civilization construction and sustainable green development in China is to achieve the dynamic and balanced development between pollution reduction, carbon emission reduction, and economic growth. Therefore, the indexes of CO2 emissions, pollution reduction intensity, and high-quality economic development are quantified in this study by using the panel data related to 26 cities in the Yangtze River Delta urban agglomeration(YRDUA) from 2010 to 2020. Furthermore, kernel density analysis, GIS spatial analysis, and other methods are employed to demonstrate the spatial and temporal patterns of pollution reduction intensity, CO2 emission levels, and the characteristics of high-quality economic development in the YRDUA. In order to reveal the dynamic relationship between pollution reduction, carbon emission reduction, and high-quality economic development, a panel vector autoregressive model (PAVR) was established and the system generalized method of moments was used to estimate the model coefficients. Additionally, Granger causality tests, impulse response analysis, and variance decomposition were conducted. There is a fluctuating upward trend shown by the overall level of CO2 emissions in the YRDUA, with significant spatial nonuniformity exhibited in carbon emissions. Despite the self-promotion mechanism playing a more significant role in the high-quality economic development in the YRDUA and its three regions, the impact of pollutant and carbon emissions on high-quality economic development is quite limited. A significant promoting effect is exhibited in those high and moderately high-quality economic development regions, while nonlinear effects are observed in those high and moderately high-quality economic development regions. As for the interactive relationships, the three factors are correlated with each other to a more significant extent in those high and moderately high-quality economic development regions, which contributes to mutual influence and causal relationship. However, there is no effective interactive mechanism observed among the three factors in the whole area and low-quality economic development regions, with high-quality economic development playing a major role in environmental pollution and carbon emission reduction.

The Relationship Between Public Debt and Economic Growth in Tanzania

This study examines the dynamic relationship between public debt and economic growth in Tanzania, focusing on causal links between the two variables and exploring potential debt thresholds. Using quarterly time series data from March 2005 to June 2022, we employ individual unit root and Johansen’s cointegration tests to establish long-term relationships. We estimate the Vector Error Correction Model and conduct the Granger causality test. The Threshold Model explores the debt threshold. Our findings reveal a significant positive long-term relationship between the two variables, with the direction of causality running from economic growth to domestic debt. Notably, the positive impact of domestic debt is pronounced when the domestic debt-to-GDP ratio falls between 31.3% and 35.2%. Beyond this threshold, the established relationship becomes statistically insignificant. The paper advocates leveraging debt financing to support productive investments for long-term economic growth. This recommendation holds significance for Tanzania and other countries across sub-Saharan Africa. However, it is crucial to undertake tailored country analyses that account for the distinct socioeconomic and political landscapes of the region's economies. These tailored analyses give valuable insights for domestic economies and global financing institutions, facilitating the formulation of context-specific debt management strategies that contribute to sustainable economic growth.

Analysis of the Factors Influencing House Prices Based on the Var Model

Var model is established to study the relationship between house price, inflation rate and GDP in this article. By using Granger causality test, cointegration test, impulse response function and variance decomposition to analyze, we conclude that the value of inflation rate and GDP can be predicted by the past data of house price, and GDP has a significant impact on house price.

Skill intensity in manufacturing exports: Do basic, technology-intensive or differentiated exports cause growth in Kuwait?

This study examines the causality between basic, technology-intensive, and differentiated manufacturing exports and economic growth in Kuwait using data from 1970 to 2021 and two augmented production function models: one with natural resource exports (Model 1) and the other without on both sides of the model (Model 2). The Johansen cointegration and the autoregressive distributed lag model (ARDL) bound tests are conducted to examine the long-run relationship between the variables. In addition, the Granger causality test in a vector autoregressive framework (VAR) and the Toda–Yamamoto test are employed to explore the directions of the short- and long-run causality between variables, respectively. The empirical results of Model 1 indicate that neither of the decomposed manufacturing exports directly causes economic growth in the short or long run at any conventional significance level, whereas natural resource exports cause economic growth, basic and technology-intensive manufactured exports in the short-run at the 5% level. Model 2 estimations confirm the absence of direct causality between decomposed manufacturing exports and economic growth, whereas a long-run causality runs from output net of natural resource exports to basic manufactured exports at the 10% level. Both model estimations indicate that all the variables jointly cause economic growth and basic manufactured exports in the short and long run, directly or indirectly through imports, confirming the existence of a circular causation. These findings can serve as the basis for designing specific export–import policies to foster diversification and a sustainable economic growth in line with Kuwait’s Vision 2035.

Coffee in Indian Economy: Performance and Prospects

Coffee is a universal hot beverage, and its international trade volumes point to significant approval in favour of coffee consumption. For many nations in the tropics, coffee is a major source of foreign exchange. This study provides a comprehensive analysis of the global and domestic coffee industry, with a particular focus on India's role based on the secondary data taken from different sources like ITC trade map, Coffee Board of India and international coffee organization statistics, etc. In 2022, global coffee production reached 10.89 million tons, with Brazil, Vietnam, and Indonesia leading the output. Despite being the eighth-largest coffee producer, India's export ranking is relatively low due to significant domestic consumption. This study employs standard econometric tools, such as compound annual growth rate, Cuddy Della Index, Markov chain analysis, etc. The Johansen co-integration test revealed a long-run equilibrium relationship among selected Arabica and Robusta coffee markets. Furthermore, the pair-wise Granger causality test identified unidirectional causality in price transmission among certain Arabica markets, while bidirectional causality was observed among Robusta markets. The paper also highlights the declining coffee yield in India due to biotic and abiotic stressors, despite an increase in production and cultivation area. The study concludes with a discussion on the dynamics of Indian coffee exports, where high domestic demand and fluctuating international prices contribute to the sector's variability. This research provides valuable insights into the performance and prospects of the coffee industry within the Indian economy, offering a basis for future policy and market interventions.

Extreme co-movements between CO2 emission allowances and commodity markets and their response to economic policy uncertainty

This study employs the generalized extreme value - autoregressive conditional Fréchet - tail quotient correlation coefficient (GEV-AcF-TQCC) analytical framework to investigate the extreme co-movements between the EU carbon futures market and the commodity futures market. Subsequently, it also examines the response of the dynamic tail quotient correlation coefficient (TQCC) values to economic policy uncertainty stemming from the US using the linear Granger causality test. The empirical results demonstrate that the tail risk indices for both upside and downside trends of the two futures markets experience changes most of the time and show drastic changes during extreme events. More importantly, before the outbreaks of the COVID-19 pandemic (from September 2019 to February 2020) and the Russia-Ukraine conflict (from May 2021 to February 2022), the dynamic TQCC values for the downside trends of the two futures markets remain at relatively high levels. Of note, when examining the dynamic TQCC values between GSCI_down and EUA_down, as well as between GSCI_down and EUA_up, it can be observed that these TQCC values rise to higher levels during periods of extreme volatility. However, they quickly decline as the GSCI futures market experiences significant fluctuations during its downside trend. This pattern indicates a tail risk spillover effect from the GSCI futures market to the EUA futures market. Further analysis using the linear Granger causality test reveals that economic policy uncertainty is a significant driver of the extreme co-movements between the EU carbon and the commodity futures markets in most cases, in addition to the extreme co-movements between the downside trend of the EU carbon futures market and the upside trend of the commodity futures market. The insights gained from this study can provide valuable guidance and enlightenment for policymakers, financial investors, and academic researchers in understanding the dynamics of the extreme relationship between the two markets under discussion, and the impacts of economic policy uncertainty on their extreme co-movements.

Econometric Analysis of South Africa’s Fiscal and Monetary Policy Effects on Economic Growth from 1980 to 2022

This study examined South Africa’s economic growth rate from 1980 to 2022 through an econometric analysis of fiscal and monetary policies. The study sought to investigate the relationships between the economy’s growth rate and various fiscal and monetary policy variables, taking into account different economic approaches such as Keynesian, monetarist, and Wagner’s perspectives. The methodology used consisted of data preparation, multiple unit root tests, Autoregressive Distributed Lag (ARDL) cointegration analysis, diagnostic tests, and pairwise Granger causality analysis. The empirical analysis found a long-term cointegration among the economic growth rate, government debt, expenditure, and revenue in fiscal policy, though government debt and expenditure were not statistically significant. Contrary to economic theory, increased government revenue had a negative correlation with economic growth. There was no long-term relationship found between the economic growth rate and monetary policy variables such as the official exchange rate, inflation rate, real interest rates, and M3 money supply. Pairwise Granger causality tests revealed a one-way relationship between government spending and economic growth, providing support to the Keynesian approach to fiscal policy. This study also discovered evidence that economic growth Granger-causes inflation, implying that economic growth may have predictive power for inflation, consistent with the demand-pull inflation hypothesis. However, no direct predictive relationships were found between the selected monetary policy variables and economic growth, supporting the long-run theory of monetary neutrality. This study suggests evaluating spending, managing inflation, implementing reforms, closing infrastructure gaps, encouraging investment, and ensuring fiscal sustainability.

Cycle Fluctuations in the U.S. Real GDP and National Income and Product Accounts Aggregates

This article provides a new set of empirical regularities describing the U.S. macroeconomy, focusing on business cycle fluctuations in the real GDP and eleven major aggregates from the U.S. National Income and Product Accounts (NIPAs). Patterns of the cyclical fluctuations are assessed using filtering methodologies that adapt to the properties of the series being studied. We employ a recent dataset that includes the Great Recession caused by the 2008 financial crisis and the ensuing recovery. We aim to (1) examine the lead-lag relations via cross-correlations between the aggregate cycle in the U.S. real GDP and the cyclical movements in the eleven major NIPA aggregates; (2) investigate econometrically the inter-linkage between the cyclical component of real GDP and that of each NIPA aggregate by way of the Granger-causality test; and (3) evaluate the capability or the predictive power of each NIPA aggregate to forecast real GDP growth via three forecasting models. Comparisons are made with popular nonparametric HP and Baxter-King (BK) filters. The basic conclusion from the empirical analysis is that the adaptive model-based filters have demonstrated advantages over the commonly used HP and BK filters for business cycle analysis across very diverse economic series from the U.S. national accounts, and the structural time series model using smoothed trend and cycle estimates produced more accurate forecasts than the AR(p) model that forecasts directly using the unfiltered time series.