Multivariate Granger Causality Tests Research Articles

The Impact of Macroeconomic Factors on the Banking Sector’s Profitability in Pakistan

This research aims to study and analyze the impact of macroeconomic factor on the banking sector’s profitability in Pakistan. Banking sector of Pakistan is the backbone of its economy. The study explains the importance of banking industry of Pakistan and its achievements and evolvement throughout the years. The objective of the study is to examine the macroeconomic factors’ influence on the banking sector in Pakistan and to investigate that whether the profitability is only determined by the bank-specific internal factors or if it is also influenced by macroeconomic shocks and factors of the economy. The study is quantitative and secondary data is employed in order to conduct this paper. The sample size is financial data of scheduled banks in Pakistan from the year 2000 to2022, which is converted in quarter data and 84 observations have been incorporated in the study. “Returns on Assets” (ROA) is selected as dependent variable of this study since it measures profitability of banks. Whereas “Foreign Direct Investment” (FDI), “Exchange rate” (LER), “Inflation” (INF), “GDP growth” (GROWTH), “Liquidity” (LLIQ), “Total Deposits” (LT_DEP) and “Credit’s ratio” (CREDIT) of the banks are selected as independent variables. The data for bank specific variables is collected from the websites of “State Bank of Pakistan” (SBP) and “Federal Reserves of Economic Data” (FRED) while the data for macroeconomic variables is derived from the database of “World Bank” and “Macrotrends”. For empirical analysis, we have applied “Vector Autoregression” (VAR) model, followed by “Multivariate Granger Causality test”, “Impulse Response Function” (IRF) and “Forecast Error Variance Decomposition” (FEVD). The main findings of study reveals that the profitability of Banking sector in Pakistan is more influenced by its own internal factors rather than the external / macroeconomic factors. Amongst macroeconomic variables, “Foreign Direct Investment” (FDI) and “Inflation” (INF) are found to have the significant impact, while “Exchange rate” (LER) and “GDP growth rate” (GROWTH) are found to have insignificant impact on banks’ “Return on Assets” (ROA). All the internal factors of our study i.e. “Liquidity” (LLIQ), “Deposits” (LT_DEP) and “Credit ratio” (CREDIT) are found to have significant impact on profitability, which is measured through “Return on Assets” (ROA). In short, the profitability of banking sector in Pakistan is mainly influenced by bank-specific internal factors of the industry. The macroeconomic factors are only 50 percent % affective on the banks’ performance.

The effects of the volatilities in global determinants on the Istanbul stock exchange

This study investigates the impact of the VIX, volatility in the oil price, gold price, exchange rate, interest rate, and traded value on volatility in the Istanbul stock exchange. The structural vector autoregression (SVAR) model and pairwise and multivariate Granger causality tests were utilized for weekly data ranging from January 2002 to November 2020. According to causality tests, the volatility in global determinants were the primary factors that determined the volatility in the stock exchange. The variance decomposition analysis indicated that the VIX was the main contributor to stock exchange volatility, and its influence increased in successive time periods. The impact of the volatility in the gold price had the second highest influence, while the influence of the oil price remained more limited. The volatility in the stock exchange was significantly affected by volatility in the exchange rate, but the significance of its movements decreased over time. In the early stages, the gold price had no effect, but then it started to have a significant and continuous positive impact. The volatility in the oil price was initially positive and significant, but after a few weeks, its influence became insignificant. Interest rate volatility was insignificant, but exchange rate volatility remained positive and significant for a few of weeks. The volatility in traded value initially appeared insignificant, but it eventually became significant, had a positive influence, and had a long-lasting effect on stock exchange volatility. Hence, the VIX is the most important global driver of stock exchange volatility in both long and short terms.

Utility of L-band and X-band vegetation optical depth to examine vegetation response to soil moisture droughts in South Asia

Vegetation Water Content (VWC) holds essential information about plant photosynthetic activity, phenological changes, ability to withstand extreme events, and soil-plant-atmosphere interactions. However, there is no consensus on the extent to which Soil Moisture (SM) and atmospheric dryness (Vapor Pressure Deficit, VPD) affect VWC during droughts. In this work, we determine the causal relationships with VWC in response to VPD and SM when plants are subjected to SM droughts during the growing season. We use satellite-derived Vegetation Optical Depth (VOD), obtained from two frequencies (L- and X- bands), to represent VWC in different parts of the plant (stem, branches, and leaves). The holistic response of VOD considering VPD and SM interactions is determined using bivariate and multivariate Granger causality tests. The analysis is carried out over South Asia.The results show a statistically significant coupling between VPD and SM at semi-weekly scales over 53.20% of the study area. In these locations, we observe a dominant influence of VPD compared to SM on VODL and VODX while subjected to soil moisture droughts during the growing season. In the areas where VPD and SM are decoupled, we find both VPD and SM to influence VODL and VODX. VODL and VODX largely exhibit contrasting causal responses. We observe a surge in VODL due to water stress while subjected to soil moisture droughts under all land cover conditions. In the case of VODX, we noticed both an increase and a decrease in leaf water content while subjected to SM droughts. The former scenario is noticed predominantly under moderate to severe drought intensity conditions during which plants attempt to maintain a stable water column (through increased stomatal regulation) for their functioning as a response to SM droughts. The latter scenario is noticed in most pixels, including forests and croplands. The observed leaf water content loss in forests is due to their mild drought intensities. We also observe a stronger causal influence of VPD and SM in low LAI areas than in high LAI areas, indicating a pressing plant water stress in sparse vegetation conditions during SM droughts.

Precious Metals and Oil Price Dynamics

The aim of this study is to examine dynamic relationship among the prices of four precious metals (gold, silver, platinum, and palladium), oil price, and the US dollar/euro exchange rate. Recent literature for precious metals (gold, silver, platinum, palladium) and oil depict a lot contradiction results of their nexus in the long and short runs. So that, there is still much to investigate and learn about their relationship between them and with the exchange rate. The monthly data span from January 1990 to October 2021 is utilized, the study applies the co-integration analysis, multivariate Granger causality test and Variance Decomposition (VDC) analysis. The result shows the evidence of a long-run equilibrium relationship but weak feedbacks in the short run. The precious metal markets respond significantly (but temporarily) to a shock in any of the prices of the other metal prices and the exchange rate. Furthermore, we discover some evidence of market overreactions in the palladium and platinum cases as well as in the oil market. The study implies whether there are overreactions and re-adjustments or not, investors may diversify at least a portion of the risk away by investing in precious metals, oil, and the euro.

Tourism and economic growth in Asia: a symmetric and asymmetric causality

ABSTRACT This study aims to investigate the causal relationship between economic growth and real international tourism revenues (RITRs) in 11 Asian areas from 1995 to 2019. The method employed in this study involves utilizing a special bootstrap multivariate Granger causality test. The results of this analysis complement the findings of the asymmetric panel causality test, as they reveal a causal relationship between positive shocks to RITRs and positive shocks to real gross domestic product (RGDP) in Singapore and Thailand, respectively. Additionally, negative shocks in Cambodia, Hong Kong and Macau demonstrate a causal link between RITRs and RGDP, suggesting that fluctuations in tourism revenues can impact the economic growth of these regions.

The export-economic growth nexus: The case of Saudi Arabia

Export strategies are the means by which the country can dispose of its domestic production surpluses to bridge successive deficits in national balances of payments and achieve economic growth. These strategies are of particular importance to the economy of Saudi Arabia, as it has opted in the last decade to diversify its economy and migrate gradually away from an economy heavily reliant on oil exports. Given the importance of diversifying the economy, this study aims to examine the relationship between exports and economic growth in the Saudi Arabian economy. The multivariate Granger Causality Test and cointegration, which is the most common model, was used in examining the short-term and long-term patterns of exports, non-oil exports, GDP, GDP per capita, and government spending from 1991 to 2016. The findings support a long-standing connection involving Saudi exports and the country’s rate of economic expansion. Unidirectional causality exists between exports, non-oil exports, and economic growth expansion, which means the growth rate rises as exports grow. In addition, the findings revealed the presence of bidirectional causality between the variables. Indeed, export promotion strategies are imperative to fulfill Saudi Arabia’s aspiration of robust and long-term economic growth. AcknowledgmentThe authors acknowledge the Deanship of Scientific Research at King Faisal University (KFU) in Saudi Arabia for the financial support provided under (Grant No. 186183).

Root cause diagnosis of plant-wide oscillations based on fuzzy kernel multivariate Granger causality

BackgroundPlant-wide oscillations are commonly observed in industrial processes and can have significant impacts on product quality and energy consumption. Accurately diagnosing the root causes of these oscillations is essential to mitigate their effects. However, when these oscillations occur, the resulting data often exhibits strong nonlinearity, making root cause diagnosis a challenging task. MethodIn this study, we propose the use of kernel multivariate Granger causality (KMGC) test as an analytical tool for root cause diagnosis. The kernel function allows for low-dimensional data to be mapped to a higher-dimensional space, making it easier to process data with strong nonlinearity. To address the computational complexity associated with complex kernel functions, we have combined the KMGC with a fuzzification function to create the fuzzy kernel multivariate Granger causality (FKMGC) analysis method. This approach improves computational efficiency, particularly in industrial scenarios where Gaussian kernel functions are commonly used. Significant findingsOur simulation and plant-wide oscillation case study demonstrate that FKMGC accurately identifies the root cause of oscillations. Furthermore, FKMGC significantly reduces analysis time compared to Gaussian kernel-based algorithms, with a 95% reduction in running time observed in our experiments. These findings suggest that FKMGC is an effective and efficient tool for diagnosing the root causes of plant-wide oscillations in industrial settings.

What is the link between temperature, carbon dioxide and methane? A multivariate Granger causality analysis based on ice core data from Dome C in Antarctica

This paper continues the work of Kang and Larsson Theoretical and Applied Climatology 116:537–548 (2014) What is the link between temperature and carbon dioxide levels? A Granger causality analysis based on ice core data. Theoretical and Applied Climatology 116:537-548, by adding methane to the study of causality between temperature and carbon dioxide. The data used goes 800,000 years back in time which is possible due to it being extracted from ice cores located at Dome C in Antarctica. First we use linear interpolation to make the three data sets equidistant to be able to employ statistical methods. We adjust for a deterministic trend and find the best model fit to be an autoregressive model with lag two and a piecewise quadratic trend. By employing multivariate Granger causality tests we find strong evidence that temperature, carbon dioxide and methane all Granger cause each other in both directions i.e. carbon dioxide Granger causes temperature and temperature Granger causes carbon dioxide, etc. This is in accordance with the findings of Kang and Larsson, with the extension that we can add methane to establish a trivariate feedback system between temperature, carbon dioxide and methane.

Causal relationships between cryptocurrencies: the effects of sampling interval and sample size

Abstract For this paper, the relationship between seventeen popular cryptocurrencies was analyzed by multivariate Granger causality tests and simple linear regression, using data spanning the period 1 September 2020 to 8 December 2021. The novelty of this work is that it studies the effects of sampling interval and sample size in cryptocurrency markets, which can yield significantly different results. Minute-by-minute, hourly and daily data were collected to examine the Granger causality relationship between cryptocurrencies. It was found that all the currencies demonstrated a significant causality relationship when high frequency (such as minute-by-minute) data was used, in contrast to hourly and daily data. The bigger the sample size, the higher the probability of rejecting the null hypothesis. Hence, the null hypothesis for the Granger causality test can be rejected for minute-by-minute time series data because of too large a sample size. Granger causality test results for hourly and daily data indicated that Bitcoin, Ethereum Classic, and Neo were leading indicators among the cryptocurrencies included in the research. In addition, according to simple linear regression analysis, the short term marginal effect of Bitcoin plays an important role by creating significant impacts on other cryptocurrencies.

FINANCIAL DEVELOPMENT AND ECONOMIC GROWTH IN CAMEROON: AN ARDL BOUND TEST AND MULTIVARIATE GRANGER CAUSALITY FRAMEWORK

Although the financial system development is vital for a country’s economic growth, it is still underdeveloped in most developing countries due to poor financial policies in terms of formulation and implementation. Therefore, this study aims to examine the effects of financial system development on the economic growth of Cameroon from 1980-2020. The study makes use of the ARDL bound test and multivariate Granger causality test. The results indicate that the financial development index and liquid liabilities have short and long run positive and significant effects on economic growth. Also, the result further reveals that in the short run, there is bidirectional causality between liquid liabilities and economic growth and a unidirectional causality running from financial development index to economic growth while in the long run, causal relationship runs from financial development to economic growth. The study recommends that the government should improve its financial system by constantly monitoring the sector to create an efficient, competitive, and stable financial sector that can contribute significantly to boosting economic growth.

Correction of global physiology in resting-state functional near-infrared spectroscopy.

.Significance: Resting-state functional connectivity (RSFC) analyses of functional near-infrared spectroscopy (fNIRS) data reveal cortical connections and networks across the brain. Motion artifacts and systemic physiology in evoked fNIRS signals present unique analytical challenges, and methods that control for systemic physiological noise have been explored. Whether these same methods require modification when applied to resting-state fNIRS (RS-fNIRS) data remains unclear.Aim: We systematically examined the sensitivity and specificity of several RSFC analysis pipelines to identify the best methods for correcting global systemic physiological signals in RS-fNIRS data.Approach: Using numerically simulated RS-fNIRS data, we compared the rates of true and false positives for several connectivity analysis pipelines. Their performance was scored using receiver operating characteristic analysis. Pipelines included partial correlation and multivariate Granger causality, with and without short-separation measurements, and a modified multivariate causality model that included a non-traditional zeroth-lag cross term. We also examined the effects of pre-whitening and robust statistical estimators on performance.Results: Consistent with previous work on bivariate correlation models, our results demonstrate that robust statistics and pre-whitening are effective methods to correct for motion artifacts and autocorrelation in the fNIRS time series. Moreover, we found that pre-filtering using principal components extracted from short-separation fNIRS channels as part of a partial correlation model was most effective in reducing spurious correlations due to shared systemic physiology when the two signals of interest fluctuated synchronously. However, when there was a temporal lag between the signals, a multivariate Granger causality test incorporating the short-separation channels was better. Since it is unknown if such a lag exists in experimental data, we propose a modified version of Granger causality that includes the non-traditional zeroth-lag term as a compromising solution.Conclusions: A combination of pre-whitening, robust statistical methods, and partial correlation in the processing pipeline to reduce autocorrelation, motion artifacts, and global physiology are suggested for obtaining statistically valid connectivity metrics with RS-fNIRS. Further studies should validate the effectiveness of these methods using human data.

Integrated Spillover Effect of Cross-Listed Stock Markets on the Indian Equity Market

The increased integration due to cross-listing leads to the volatility spillover effect on the domestic market posing from the cross-listed global indices viz., Nifty 50 from India, Luxx 100 from Luxembourg, NASDAQ from the US, and FTSE_Aim 100 from the UK. Johansen Co-integration test is applied to check the level of integration, which is further checked by multivariate granger causality showing the causality pattern among the indices. GARCH (1,1) model is applied to examine the volatility spillover effect on the Indian Stock Market. The findings suggest that the series are co-integrated with one vector ‘v,’ which is confirmed by the Trace and Max-Eigen Test. The Multivariate Granger Causality test confirms the bivariate causal pattern between India and US markets, implying the dual effect. In contrast, the Luxembourg market is relatively exogenous, which gives investors an opportunity for portfolio diversification. ARCH term is significant in the GARCH (1,1) model showing that the past innovation in the time series leads to the present fluctuation in the Indian stock market. Also, the results show a significant spillover effect from the US and UK markets. Thus, this will assist the investors that by concentrating on the movement of these markets, they can take specific actions regarding portfolio management.

Empirical Testing of Co-integration of International Financial Markets with Reference to India, the USA, Japan, and Hong Kong

This article examines the co-integration among the financial markets of four countries (India, the USA, Japan, and Hong Kong) from April 2010 to May 2021. Using the Johansen co-integration test, we establish the integration between the stock markets of these countries. The test clearly shows that there exists long-run co-integration between these countries. Furthermore, we checked the relationship of one country with another by applying the multivariate Granger causality test, which shows the influence of the US market over the other countries. The results significantly show the existence of long-run co-integration and linkages between these financial markets. Applying the Vector error correction model and variance decomposition analysis for the period confirms the result obtained from multivariate Granger causality. As the study shows, the stock market of the countries is not entirely inter-linked. However, some tests provide the pairwise linkages but not when talking about all the nations together. That means the rise in one stock market does not necessarily impact the surge in other stock exchanges, which means investors can profit from diversifying between these stock exchanges. It provides the opportunity for portfolio diversification to investors. The study shows that “the international stock market is neither fully integrated nor completely segmented,” giving international diversification an opportunity. As in our study, the “U.S. market is the most exogenous,” and the Japanese market is relatively isolated from the other market, where the impact from the different markets is not that significant.

Does the Number of Hajj Pilgrims Boost Economic Growth in Saudi Arabia?

This paper uses the multivariate Johansen cointegration approach and Granger causality tests to study the causal relationship between the number of pilgrims and the non-oil GDP growth in Saudi Arabia over the period 1980–2016 by integrating the real effective exchange rate index as an additional variable. Our findings indicate that there is only one long-run relationship between the three variables when the non-oil GDP growth is the dependent variable. There is a unidirectional long-run causality from the number of pilgrims to non-oil GDP growth. Besides, we find no short-run causal relationships between the three variables. Therefore, an important policy implication resulting from this study is that pilgrims can be a led growth factor to economic growth in Saudi Arabia in the long run. The pilgrimage event can, therefore, be an excellent opportunity for Saudi Arabia to boost its economic activity as stated by its 2030 vision.

JAPAN’S STOCK MARKET PERFORMANCE: EVIDENCE FROM TODA-YAMAMOTO AND DOLADO-LUTKEPOHL TESTS FOR MULTIVARIATE GRANGER CAUSALITY

This paper empirically examines the causal linkages of Japan's stock market (proxied by Nikkei 225 index) performance with selected key macroeconomic fundamentals. Relatively recent Toda-Yamamoto and Dolado-Lutkepohl, multivariate Granger causality tests are implemented. Monthly time series data from September 1974 to February 2017 with a large sample size of 510 monthly observations covering the floating exchange rate regime were utilized. The study documents some interesting and some unexpected results. Bi-directional causality is evidenced only between the stock market and the industrial production. Somewhat counterintuitively, unidirectional causality runs from stock market to money supply. Furthermore, unidirectional causality flows from interest rate (bond yield) to stock market. Not so surprisingly, no causality is detected between the stock market and the general price level. This is also true for stock market and exchange rate. The above findings may aid Japanese policy makers to formulate appropriate financial, monetary and exchange rate management policies. Japan should give second thought on the efficacy of its over reliance on monetary policy with interest-rates targeting and should prepare itself for launching a pragmatic fiscal stimulus program.Keywords: Stock Market; Macroeconomics Fundamentals; Granger CausalityJEL Classifications: E44, F41, G15DOI: https://doi.org/10.32479/ijefi.11262

Causal Analysis of Health Interventions and Environments for Influencing the Spread of COVID-19 in the United States of America

Given the lack of potential vaccines and effective medications, non-pharmaceutical interventions are the major option to curtail the spread of COVID-19. An accurate estimate of the potential impact of different non-pharmaceutical measures on containing, and identify risk factors influencing the spread of COVID-19 is crucial for planning the most effective interventions to curb the spread of COVID-19 and to reduce the deaths. Additive model-based bivariate causal discovery for scalar factors and multivariate Granger causality tests for time series factors are applied to the surveillance data of lab-confirmed Covid-19 cases in the US, University of Maryland Data (UMD) data, and Google mobility data from March 5, 2020 to August 25, 2020 in order to evaluate the contributions of social-biological factors, economics, the Google mobility indexes, and the rate of the virus test to the number of the new cases and number of deaths from COVID-19. We found that active cases/1,000 people, workplaces, tests done/1,000 people, imported COVID-19 cases, unemployment rate and unemployment claims/1,000 people, mobility trends for places of residence (residential), retail and test capacity were the popular significant risk factor for the new cases of COVID-19, and that active cases/1,000 people, workplaces, residential, unemployment rate, imported COVID cases, unemployment claims/1,000 people, transit stations, mobility trends (transit), tests done/1,000 people, grocery, testing capacity, retail, percentage of change in consumption, percentage of working from home were the popular significant risk factor for the deaths of COVID-19. We observed that no metrics showed significant evidence in mitigating the COVID-19 epidemic in FL and only a few metrics showed evidence in reducing the number of new cases of COVID-19 in AZ, NY and TX. Our results showed that the majority of non-pharmaceutical interventions had a large effect on slowing the transmission and reducing deaths, and that health interventions were still needed to contain COVID-19.

Inflation and Economic Growth in Kenya: An Empirical Examination

Purpose This paper examines the relationship between inflation and economic growth in Kenya from an analytical and empirical standpoint. Design/methodology/approach The paper applies the autoregressive distributed lag (ARDL) bounds testing approach and the multivariate Granger-causality test using time series data covering 1970-2019. Structural breaks in the time series were also conducted using the Perron (1997) (PPURoot) and Zivot-Andrews (1992) (ZAU Root) techniques. Incorporating structural breaks into time series increases statistical inference's overall validity. Findings Inflation and economic growth in Kenya were found to have structural breaks in 1995 and 1991. These years are marked by Kenya's economic, financial, public sector and institutional reforms. The other findings of the study revealed that inflation has a statistically significant negative influence on long-term economic growth. The multivariate Granger-causality results showed a distinct short-run unidirectional causality from economic growth to inflation in Kenya. Practical implications In order to mitigate the negative consequences of inflation and the coronavirus on the economy and welfare, the study recommends that Kenya's government should pursue prudent monetary, financial, and fiscal policies.

Tourism and Economic Development: A Multivariate Panel Granger Causality Test

ABSTRACT This study applies a recently developed bootstrap multivariate panel Granger causality test to examine the causal relationship between international tourism receipts and economic growth in 20 regions of China for the period from 1995 to 2018. The results support evidence for the growth hypothesis in regions, such as Anhui, Henan, Hubei, Ningxia, and Shaanxi. A reverse relationship supports evidence of the conservation hypothesis for regions, such as Hunan and Inner Mongolia. In addition, a reciprocal causal relationship was found in Guangxi, Qinghai, Tibet, and Yunnan while the result of a neutrality hypothesis supported 9 of these 20 major regions (i.e., Chognqing, Gansu, Guizhou, Heilongjiang, Jiangxi, Jilin, Shanxi, Sichuan, and Xinjiang).

The validity of the export-led growth hypothesis: some evidence from the GCC

ABSTRACT This study investigates the validity of the export-led growth hypothesis (ELG) in five GCC countries, namely, Bahrain, Kuwait, Oman, Saudi Arabia and the United Arab Emirates. The study uses an augmented production function and annual time series data over the period 1975-2016. For the estimation of the models, the Johansen cointegration test is employed to test the existence of a long-run relationship between growth and exports. In addition, the multivariate Granger causality test in a vector autoregressive model framework and a modified version of the Wald test are applied to examine the direction of the short-run and long-run causality respectively. The empirical results provide evidence to support the validity of the ELG hypothesis in the short-run for the UAE, while the converse is true for Bahrain. In addition, a bi-directional causality exists between exports and growth in the case of Kuwait. In the long-run, the validity of the ELG is confirmed in the case of Bahrain, while economic growth causes exports in the case of Kuwait and Saudi Arabia.

Interest Rate Deregulation, Financial Development and Economic Growth: Evidence from Bangladesh

This article analyses the relationship among interest rate reforms, financial development and economic growth by using annual dataset for the period covering 1980–2014 for Bangladesh. The effect of interest rate reforms on financial development is examined using a financial deepening model, and the causal relationship between financial development and economic growth is examined, by including deposit interest rate as a third variable, thereby forming a simple trivariate causality model. The empirical results of cointegration and error correction models show that there is a positive effect of deposit rate of interest rate on financial depth in Bangladesh. Besides, multivariate Granger causality tests reveal that there is only one-way causality between financial depth and economic growth—the flow running from financial depth to economic growth. In addition, the study finds there is bidirectional causality between deposit rate of interest rate and economic growth, which is also confirmed by the pairwise Granger causality test. The inference of this study is that a deregulated deposit rate of interest will raise financial depth and eventually enhance the economic growth of Bangladesh. Therefore, the financial reforms should be directed towards accomplishing a more deregulated deposit rate of interest for progressive growth in the economy of Bangladesh.