Engle Granger Research Articles

Network characteristics and stock liquidity:Evidence from the UK

This article examines the effect of the Engle-Granger (E-G) price spillover network characteristics on firm's stock liquidity with a longitudinal dataset of FTSE 350 from 2006 to 2021. We find that the subprime crisis, European debt sovereign crisis, British Brexit and COVID-19 caused dramatic network structure change. We also find that firms with higher centrality are likely to suffer from more or greater price shocks leading to lower stock liquidity. Finally, the robust of our results has also been identified, and a general framework for network characteristics and stock liquidity has been established to some extent.

The Conundrum of Stock Market Performance and Gross Domestic Product: An Empirical Evidence from Kenya

The main objective of this study was to establish is the difficult question among researchers on the direction of relationship between the stock market performance and the gross domestic product using an empirical evidence from Kenya. Both theoretical and empirical reviews have presented a puzzle especially in the long run relationship between the two variables and empirical investigations in various country contexts , time varying longitudinal periods and other settings have continued to provide conflicting results. The study employed quarterly statistics of the Nairobi All Share Index (NASI) obtained from the Capital Markets Authority Bulletins and quarterly Gross Domestic Product rates from 2012-2019 obtained from Kenya National Bureau of Statistics and applied the Engle granger causality test and establish the link between stock market performance and economic growth in the long run (i.e. whether stock market performance causes economic growth or itself is a consequence of increased economic activity). The statistical techniques used included the unit root Augmented Dickey Fuller (ADF) test in order to test the stationarity for all the time series in their levels and first differences. The Johansen co-integration test to investigate whether the variables are cointegrated of the same order taking into account the trace statistics and the maximum eigen-value tests. The data was transformed into natural logarithms and normalized for further analysis. Both GDP and NASI were found to be stationery at level and upon first difference after ADF tests. The findings were that there was none directional/No causal relationship between the two variables after conducting a pairwise granger causality test, however there was a significant cointergrating equation on the variables, signifying the long run relationship between the two variables. From the results, it was inferred that the movement of stock prices in the Nairobi stock exchange reflect the macroeconomic condition in the long run of the country and can therefore be used to predict the future path of economic growth however the puzzle continues on causality.

Malaysia’s Agricultural Production Dropped and the Impact of Climate Change: Applying and Extending the Theory of Cobb Douglas Production

Under climate change, Malaysia's agricultural production showed decreasing in recent decades. This study tries to fill in the gaps to applying and extending the Cobb Douglas production function theory to examine the impact of climate change and economic factors on Malaysia's agricultural production. Using Engle-Granger (EG) test with 37 years of data from 1980 to 2016. The findings showed that the long-run estimated coefficients for rainfall, temperature, and interest rate were -0.338, -0.024, and -0.029, respectively. This indicates that each additional percent in rainfall, temperature, and interest rate will be affected the agricultural production, on average, to decrease by 0.338%, 0.024%, and 0.029%, respectively, holding others constant. Besides that, the long-run elasticity of real GDP per capita, employment, and Trend showed 0.509, 0.513, and 0.119, respectively. Increase 1% of real GDP per capita will lead to the agricultural production to increase about 0.509%, ceteris paribus. The elasticity of employment showed that each 10% increase in agricultural employment will increase the agricultural production on average 5.13%, ceteris paribus. Furthermore, the trend estimated coefficient showed that the agricultural production will have a constant growth rate which is 0.119% per year. All variables were statistically significant to explain the long-run agricultural production. The short-run rainfall, temperature, employment, and Trend were statistically significant to determine the short-run production growth. Therefore, advanced technology and the latest information on climate change are relevant to boost agricultural production growth. In addition, policymakers also suggested establishing lower interest rate loan facilities and no labor shortage in this industry.

Nexus of Corruption Control and Economic Development in African Least Corrupt Countries

The main objective of this paper is to examine the impact of corruption control on the performance of economies of ten least corrupt African countries spanning the period 1996-2014 and make policy recommendations on how to improve it. In detail, the paper makes an effort to: evaluate the extent to which the control of corruption impact on economic performance of the sampled African nations indexed as least corrupt. The study makes use of data obtain from World Development and World governance Indicators (WDI & WGI) and adopts panel granger causality framework, Engle Granger based panel cointegration, and FMOLS (fully modified ordinary least squares) estimation (weighted Estimation). The findings show that the variables are cointegrated in the long run with regards to economic growth and its determinants, as well as the result of Vector Error Correction Mechanism (VECM) confirms that the variables are dynamically interacted among the 10 selected African countries. The result of FMOLS does not support the hypothesis that corruption control and political stability have positive influence on the growth of the economy. However, government effectiveness and gross domestic savings have positive impacts on economic growth. The study recommends more policy that can strengthen the anti-corruption agency by improving the quality of infrastructure in those countries as well as enhancing social responsibility and collective consciousness.

Assessment of Government Agriculture and Rural Development Expenditure Impact on the Malaysia’s Agriculture Production

This study is focused on determination of the important factors that have affected the agriculture production in Malaysia. The data on interest rate (IR) and government agriculture expenditure (GE) were collected from Bank Negara Malaysia (BNM) monthly bulletin statistic. Besides that, the number of labor use in agriculture sector (LA) was collected from the World Development Indicator (WDI). All data collected were based on the time series (annually) from 1983 to 2016. In this study, the Augmented Dickey Fuller (ADF) and Philips Perron (PP) tests are used to determine the stationary of the time series data and the overall findings showed that the time series data are stationary at order one or I(1). In this study, the result of Engle Granger (EG) co-integration test was used and stated that all the factors were co-integrated with the agriculture productivity. However, the GE2 and interest rate (IR) are the only factors that showed a negative relationship. In the long run, all the factors are significantly affecting agricultural productivity, while the interest rate is insignificant to determine the agriculture production. Furthermore, the Error Correction Mechanism (ECM) model showed that all the factors are significantly influencing the agricultural production in Malaysia except the interest rate. In the nutshell, this study suggests that the policy maker should take the precautions in the budget spending in agriculture sector, which should not exceed the threshold spending of RM3,057 million.

Estimating the Nature of Relationship of Entrepreneurship and Business Confidence on Youth Unemployment in the Philippines

This study estimates the nature of the relationship of entrepreneurship and business confidence on youth unemployment in the Philippines over the 2001-2017 period. The paper employed a range of cointegrating regression models, namely, autoregressive distributed lag (ARDL) bounds testing approach, Johansen-Juselius (JJ) and Engle-Granger (EG) cointegration models, dynamic OLS, fully modified OLS, and canonical cointegrating regression (CCR) estimation techniques. The Granger causality based on error correction model (ECM) was also performed to determine the causal link of entrepreneurship and business confidence on youth unemployment. The ARDL bounds testing approach, Johansen-Juselius (JJ) and Engle-Granger (EG) cointegration models confirmed the existence of long-run equilibrium relationship of entrepreneurship and business confidence on youth unemployment. The long-run coefficients from JJ and dynamic OLS show significant long-run and positive relationship of entrepreneurship and business confidence on youth unemployment. While results of the long-run coefficients from fully modified OLS and canonical cointegrating regression (CCR) found that only entrepreneurship has significant and positive relationship with youth unemployment in the long-run. The Granger causality based on error correction model (ECM) estimates show evidence of long-run causal relationship of entrepreneurship and business confidence on youth unemployment. In the short-run, increases in entrepreneurship and business confidence causes youth unemployment to decrease.

Econometric Analysis of Transport Sector on Economic Growth in Rwanda (1999-2018)

The purpose of this study was to examine Econometric analysis of transport sector on economic growth in Rwanda for the period of 1999 up to 2018. It’s an empirical study which used econometric techniques such as unit root test, Engle granger test/cointegration test, linear regression model and the error correction model to analyze the contribution of transport sector on economic growth based on Gross Capital formation in terms of transport infrastructures, trade balance as import and export can be affected by transport system, and contribution of transport services to economic growth. This study found that, there was a strong statistically significant relationship between GDP and transport sector as measured by transport services for both short run and long run as it is shown by R-squared of 0.997316 and 0.782009 of the long run regression model and error correction model respectively. ECM showed a quick recovery of 81.3% every year after a shock happen. This study concluded that there is a short run and long run relationship between Transport service and economic growth. This study recommends that policy implication that can be deducted from this study to facilitate transport sector through expansion of road networks and maintenance of existing road networks coupled with revitalization of alternative mode of transportation such as air transport, rail system and waterways will significantly improve the growth of the economy

An Empirical Study on Impact of Bilateral Exchange Rate to Trade between Shandong Province of China and Japan

This study explores the impact of bilateral exchange rate on the export and import between Shandong province of China and Japan under supply chains and vertical integration cooperative pattern from regional view. Based on the data over 1998–2017 periods, ADF test, Engle-Granger (EG) test and Granger Causality Test are employed. The results indicate that bilateral exchange rate (RMB/100Yen) is positive to the export trade from Shandong province to Japan, but there is no evidence bilateral exchange rate has effect on the import of Shandong province from Japan. The supply chains and vertical integration cooperative pattern are used to explain the result

GDP and Outward Foreign Direct Investment from India: Co-Integration and Causality Test

India’s outward foreign direct investment has experienced a Dynamic change over last decade after the liberalisation and globalisation of Indian economy that further achieves economic expansion and development in recent years. Approximately seven percent growth rate is witness by India from 1993-2011.This period also witnessed upward trend of outbound investment from India. UNCTAD (2015) has also nailed India as one of the leading outward investing economies. But a very few works have been done for exploring the long run relationship between outward FDI and GDP in context of the emerging economies like India. With this backdrop, the present study empirically investigates the long run and short run co –integrating relationship between India’s GDP and OFDI. For empirical analysis various econometric tools like test of stationarity, Engle granger co-integration ( as we have consider only two macro variables) and ECM model is used to test the causal association between the selected variables. It is found that there is long run causal relationship among OFDI and India’s Growth rate. The results found that no short term causal association among India’s OFDI and GDP by reflecting the fact that there are other factors which have influence on OFDI flows.

Role of Human Capital in Economic Growth: A Comparative Study of India and China

Human capital constitutes a significant ingredient of economic growth. As more and more countries are moving on the path of growth and development one needs to explore the contribution of human capital in economic growth. Considering health and education as two components of human capital, the paper attempts to achieve three-fold objectives. First to examine the status of health and education in both the countries for the period 1970-2016, secondly to comprehend the role of public expenditure on health in economic growth and finally to examine the role played by human capital comprising of both health and education component in economic growth of both the nations. To achieve the first objective, the paper attempts to construct the Index of health and education both for India and China, also comparing it with South Asian region. Public health expenditure by government (PHE), out of pocket expenditure (OPE), Life Expectancy (LE) and Access to health care (HC access) are selected as indicators of health while educational attainment at least completed upper secondary, population 25+ (EDU) indicating the access to education, Literacy rate (LR) and number of patent applications by residents (PA) are considered as indicators of education. Both health and education indices are constructed using the Principal Component Analysis (PCA) method. The study examines the causality between the public health expenditure and economic growth for both the countries by deploying the Engle Granger Causality test using the Vector autoregression (VAR) model. Finally, an endogenous growth model is run selecting the appropriate variables, considering health and education indices as inputs. The study observes uni-directional causality between public health expenditure and economic growth, GDP granger causes PHE in both India and China. Both health and education index have positive impact on growth in India however the results of regression depict that the both health and education negatively affect the growth rate in China. The paper holds a great relevance in terms of policy implications highlighting the need for the developing nations to invest in human capital both health and education. It is crucial to comprehend the significance of accessibility of health and education by removing the political economy, making these facilities more inclusive.

Frequency Co-integration-based damage detection for bridges under the influence of environmental temperature variation

Natural frequencies of structures are usually convenient to acquire with a high precision in engineering. However, it is still difficult and faces challenges to apply the frequencies in the area of structural health monitoring. One of the main reasons is the natural frequency’s sensitivity to the variation of environmental conditions, such as temperature, which often disturbs the frequency based identification and even results in the false evaluation of the structural health condition. To overcome this problem, a novel frequency-based co-integration technique is proposed in this paper. The main principle is that the non-stationarity of the acquired frequencies caused by the variation of environmental temperature, can be transformed to stationary sequence by linear combining two non-stationary frequencies using the co-integration algorithm. The calculated stationary relationship between two frequencies series is insensitive to the influence of environmental variation, and can be employed to detect structural damage. In the study, ADF (Augmented Dickey-Fuller) test and EG (Engle-Granger) test are first introduced to check the non-stationary order and calculate the co-integration coefficients of the frequency series. Subsequently, the theoretical derivation of frequency co-integration based technique is developed using a simply supported beam as an example, and the procedure of damage detection based on frequency co-integration technique under the influence of environmental temperature is introduced in detail. At last, the validity and robustness of the proposed method are illustrated and verified through a numerical simulation of a steel truss bridge and a real cable-stayed bridge under the influence of changing environmental temperature. Both the numerical simulation and the practical application demonstrate that the proposed frequency co-integration technique can effectively eliminate the influence of the changing environmental temperature and identify the structural damage accurately.

Accurate determination of parameters relationship for photovoltaic power output by augmented dickey fuller test and engle granger method

Power output from photovoltaic (PV) systems in outdoor conditions is substantially influenced by climatic parameters such as solar irradiance and temperature. PV manufacturers always provide technical specifications in laboratory conditions but reliable relationship for the power output must be determined for accurate prediction under real operating conditions. For the present study, solar irradiance G, temperature T and electrical power output P data under real conditions are methodically and regularly inscribed in dataloggers. Hence, in this paper, we investigate rigorous and robust statistical methods for small sample such as Augmented Dickey-Fuller and Engle Granger for stationary series to determine the estimate regression between variables P, G & T. A first regression of power output P time series variable on solar irradiance G time series has shown spurious results and thus spurious regression. The first differences of such time series are stationary and a regression is proposed whereas temperature variable is identified as not significant and where autocorrelation of residuals is suspected. Finally, the novelty of this paper is the Engle & Granger method that is used to provide a relationship between variables P and G in a difference level. A final relationship without suspicious heteroscedasticity has been determined. Our model is formulated on the basis of PV real conditions statistical approach and is more realistic than steady approach models.

An analysis of the electricity market and power needs in Shandong using vector autoregression

Based on relevant data of economic and social development in Shandong Province, the authors analyse the status of the electricity market and determined four factors which affect the electricity demand: economic development, energy structure, power supply and demand as well as electricity demand structure. This paper analyses the stability of the electricity demand variable by augmented Dickey-Fuller (ADF) test and projection pursuit (PP) test, and analyses DCO-integration of them by Engle-Granger (EG) method and Johansen method. Based on stability and cointegration analysis, this paper emphasised a VAR model to analyse the correlation between electricity demand indicators and four other influencing factors. In the end, this paper suggested that had a certain theoretical and practical significance for Shandong Provincial Electric Power Development.

An Analysis of the Electricity Market and Power Needs in Shandong Using Vector Autoregression

Based on relevant data of economic and social development in Shandong Province, the authors analyse the status of the electricity market and determined four factors which affect the electricity demand: economic development, energy structure, power supply and demand as well as electricity demand structure. This paper analyses the stability of the electricity demand variable by augmented Dickey-Fuller (ADF) test and projection pursuit (PP) test, and analyses DCO-integration of them by Engle-Granger (EG) method and Johansen method. Based on stability and cointegration analysis, this paper emphasised a VAR model to analyse the correlation between electricity demand indicators and four other influencing factors. In the end, this paper suggested that had a certain theoretical and practical significance for Shandong Provincial Electric Power Development.

Comparison Of Cointegration Tests For Near Integrated Time Series Data With Structural Break

Sample size of data, presence of structural break, location and magnitude of potential break, and having with near integrated process might affect the performance of cointegration tests. Engle-Granger (EG) and Johansen Cointegration tests may have erroneous results since they do not take into account possible structural break unlike Gregory – Hansen (GH) cointegration test. In this study, it is argued that the suitable choice of cointegration tests is quite complex, since outcomes of these tests are very sensitive to specifying these properties. The performance of cointegration tests is compared to each other underlying properties. This study presents how standard residual based tests- Engle-Granger and Gregory-Hansen- for cointegration can be implemented if series is near integrated, that is close to a unit root process. For assessing the finite sample performance of these tests, a Monte-Carlo experiment showed that both cointegration tests have relatively better size and power properties depend on break point, break magnitude, sample size of time series and the hypothesized value of AR(1) parameter. To illustrate the findings of the paper, a financial data is analyzed. The practitioners should be careful about the hypothesized value of AR(1) parameter which represents dependency degree of the data. If the autoregressive parameters is very close to one and the break magnitude is high, any test is acceptable for moderate to large sample size. However, one might need very large sample size to have a good power and actual size of the test. Additionally, GH test becomes liberal test unlike EG test as the magnitude of structural break increases.

Promoting carbon emissions reduction in China's chemical process industry

The chemical process industry is the second largest carbon-emitting sector in China. Therefore, it is extremely urgent and crucial to explore how to reduce carbon emissions in the sector. This paper employs the co-integration method and scenario analysis to investigate how to reduce carbon emissions in the sector. The granger causality test is conducted and the result indicates that all the variables except lnPare granger-causes lnCE. Moreover, comparing the JJ (Johansen-Juselius) co-integration with the EG (Engle-Granger) co-integration based on the squared residual, the fitting effect and the prediction effect, we find that the EG co-integration method is better. Furthermore, we adopt the EG co-integration and scenario analysis and find that the emission reduction potential of the sector will be 63.9 Mt in 2015 and 180 Mt in 2020 under the middle scenario; and 121.4 Mt in 2015 and 327.9 Mt in 2020 under the advanced scenario. Finally, the paper provides some policy implication.

Sector Effects in Emerging Market Returns:Evidence from India

This paper sheds light on the implications of the changing structure of stock returns for asset management. Using latest stock market data, under Engle Granger (EG) cointegration framework, it finds absence of stock return correlations among industrial sectors in India. The findings provide clear evidence of the diversification over unrelated sectors which yield more efficient portfolios than diversification over firms. Mean-variance analysis of a set of experimental portfolios further confirms the prediction on portfolio choice with multi-sector consideration. The study suggests analysis of the risk return indifference curves of the investors for matching the portfolio performance with investor utilities.

The Causal Relationship between the Bond Market Development and Economic Growth in Africa: Case Study of South Africa

The paper empirically examines the causal relationship between bond market development and economic growth in Africa with South Africa as a case study over a period from 1995 to 2012. Quarterly data applying the Engle Granger cointegration method and the pairwise granger causality test was used. Empirical results reveal that there is a relationship between economic activity and bond market capitalization. Our results imply that African governments should put in place policies which will promote the development of the bond market as one of the ways to mobilise domestic resources and promote economic growth. DOI: 10.5901/mjss.2014.v5n3p126

Causality links between consumer and producer price inflation in South Africa

This study investigates causal relationships between consumer and producer inflation. In so doing, it makes the following contributions to the literature. First, the methodology allows causality testing within a bivariate system, with other important variables included in the system. Second, the analysis is based on a model chosen out of three other competing models. The models consider the possibility of both symmetric and asymmetric responses to demand and supply shock inflationary situations. This approach differs from the traditional Engle–Granger (EG) approach, which implicitly assumes symmetric adjustment towards equilibrium (Enders and Siklos, 2001). Third, causality testing is conducted within an error correction framework to handle possible inferential biases resulting from invalid asymptotic critical values. The chosen model was used to conduct short- and long-run causality tests within a framework that takes into account differentiated responses to changes in inflation regimes. The following are major findings of this study: There exists dynamic relationship between producer and consumer prices, characterized by unidirectional causality running from producer to consumer inflation. On the issue of asymmetric responses, it is found that consumer inflation reacts differently to positive and negative trends in producer inflation. In other words, consumer price rises faster than it falls.

Price transmission in the South African food market

This study investigated asymmetric price transmission between producer and retail markets in South Africa. The authors applied the traditional (Engle-Granger) and the standardised (Enders & Siklos) Dickey-Fuller procedures to test for cointegration and asymmetry in price transmission. Four competing models, namely the Engle-Granger (EG), Threshold Autoregressive (TAR), Momentum Threshold Autoregressive (M-TAR), and Momentum Consistent Threshold Autoregressive (MC-TAR) models, were fitted. Following the application of a standard model selection criterion, the MC-TAR was chosen as the best. The following results were obtained: price transmission between producer and retail prices is asymmetric; the direction of causation runs from producer to retail prices; retailers correct about 2 per cent of the negative and 4 per cent of positive disequilibria every month; and it takes fewer months for negative shocks than positive shocks to be eliminated. In general, the results suggest that retailers respond more quickly to shocks that stretch their market margin than to those that squeeze it. This is typical of positive asymmetric price transmission. We attribute this to the anticompetitive behaviour that characterises the food market chain.