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In changing context of the present-day world, trade openness has a crucial role to play in economic development of different countries. Besides other factors, institutional quality plays a vibrant role in achieving a high growth rate. The objective of the present study is to understand how institutional quality influences economic growth and trade openness in India. To achieve the objectives of the study, Autoregressive Distributed Lag bound testing approach has been used. The findings show that there exists long-run relationship between the variables used in this study. From the findings, it can be concluded that total trade has a negative impact, whereas export enhances economic growth in the country. The results also show that improvement in institutional quality has a positive impact on economic growth. Thus the findings suggest that the country needs to adopt policies that can improve the quality of institutions and can enhance the formation of physical and human capital. [ABSTRACT FROM AUTHOR]

The re-assessment of energy – economy nexus in developing economies like India is necessitated by the constant evolution of their growth path and sectoral progression. This sectoral level examination is intended to find new evidence for the dependence of economic growth on the electricity sector in India. Using annual data of per capita real GDP and sectoral level consumption of electricity from 1971 to 2019, the combined co-integration test and Frequency domain causality approaches are employed. The estimated results show the negative impact of agricultural sector electricity consumption on growth, whereas both the Industrial and service sector enhances the production. In addition, the results of the Frequency domain causality approach support the Growth hypothesis for the study period in India, as Uni-directional causality from three sectors to economic growth were found at least in a frequency of short run to medium run. The sustainability of the growth-enhancing role of electricity consumption will depend on more sector-specific energy policies and public spending on energy infrastructure development than bland subsidies. [ABSTRACT FROM AUTHOR]

Purpose: The paper aims to examine the effects of floods on economic growth in India from 1980 to 2019, taking into account the role of foreign direct investment (FDI) inflows and foreign aid. Design/methodology/approach: The study uses augmented Dickey–Fuller (ADF) and Phillip–Perron (PP) tests to determine the stationarity of the variables. Several models, including autoregressive distributed lag (ARDL), fully modified ordinary least square (FMOLS), dynamic ordinary least square (DOLS) and canonical cointegration regression (CCR), are used to examine the impact of floods on economic growth. Findings: The bounds test determines the long-term relationship between floods, FDI inflows, economic growth and foreign aid. According to the ARDL and FMOLS models, floods have a negative long-term and short-term impact on India's economic growth. Furthermore, FDI inflows and foreign aid are beneficial to economic growth. The findings of the ARDL and FMOLS models are confirmed by the DOLS and CCR models. Granger causality establishes a unidirectional causality that extends from floods to economic growth. Further diagnostic tests show that the estimates are free of heteroskedasticity, serial correlation and parameter instability. Practical implications: Indian government needs to invest more in research and development on flood management techniques. Institutional strengthening is also required to implement pre- and post-flood prevention measures properly. Sound disaster financing strategy and proper water bodies management should be prioritised. Foreign investment opportunities should be encouraged by strengthening international relations. Originality/value: This is the first time-series study that analysed the effects of floods on economic growth in India. Moreover, the paper contributes to floods literature by applying several econometric models for robustness check. [ABSTRACT FROM AUTHOR]

Understanding the relationship between electricity consumption and economic growth is crucial for formulating efficient energy policies for all sectors in general, and for agriculture in particular. To study this, an empirical examination of the long-run co-movement and the causal relationship between electricity consumption and real gross state domestic product (GSDP) from agriculture and its allied sectors is attempted. The agricultural sector involves the use of different input technologies that are further influenced by electricity consumption. To account for this technology-enabling effect of electricity, we further take up an analysis of the relationship between electricity consumption and agricultural technology factors: fertilizer consumption, the share of irrigation, area under cereal, and the extent of mechanization. We use both state (data from 17 states for the period 1993–2017 for the electricity–GSDP relationship) and country-level data (for the period 1980–2018 for the electricity–technology factors relationship) in the analysis. Since short-period time series datasets analysis may yield unreliable and inconsistent results, we employed modern heterogeneous panel co-integration and panel-based error correction model techniques for analyzing the energy–growth linkage. When the heterogeneous states effect is taken into account, the empirical results fully support a positive long-run co-integrated connection between GSDP and electricity consumption. We could detect both long-run and short-run unidirectional causality running from electricity consumption to agricultural growth. Further electricity consumption was also found to augment the use of technology factors in agriculture. This calls for implementing policies and strategies for achieving higher electricity use in agriculture and improved efficiency in its utilization simultaneously. [ABSTRACT FROM AUTHOR]

Total factor productivity (TFP) is an important indicator for measuring the relationship between outputs (total product) and inputs i.e., factors of production. It is primarily related with labour and capital. Increased TFP is the result of economic growth, technological change, education, innovation, etc. India, as one of the emerging market economies, should play an effective role in ensuring resilience and sustainability with respect to productivity. The objective of the study is to examine the influence of Infrastructure Investment (PII) on TFP in India. Annual time series data from the Federal Reserve Economic Data (FRED) for the time period 1961 to 2020 has been used for this. Econometric tools such as Augmented Dickey-Fuller (ADF) and Philips-Perron (PP) tests confirm the problem of non-stationarity at level. This problem was eliminated by taking first difference of all variables. Johansen Co-integration test was applied to find out co-integration. To capture the long-run and short-run causal relationships between PII and TFP, the Vector Error Correction Model (VECM) was applied. The results confirm that there is an impact of PII on TFP. Short-run causal effects of PII on TFP have also been found. The Error Correction Term (ECT) value reveals that the model is in equilibrium and adjusts itself to external shock. Each result shows evidences of validation of PII for TFP. This study can serve as a guideline for policy makers so that they can strive to make productivity sector resilient and sustainable in post-pandemic era. [ABSTRACT FROM AUTHOR]

This paper investigates the association among energy consumption, carbon‐dioxide emission, and India's economic growth along with supplemental variables including foreign direct investment, population density, inflation, and agricultural land. An autoregressive distributive lag model is applied over annual time‐series data from 1985 to 2019. Further, the robustness and novelty of the model are confirmed by applying fully modified ordinary‐least‐square, dynamic ordinary‐least‐square, canonical co‐integrating regression, variance decomposition, and impulse response function. The analysis results indicate that a cointegration association exists among variables. In the short run, the profound determinants of economic development are energy consumption, carbon‐dioxide emission, inflation, and agricultural land. In the long run, both inflation and agricultural land significantly negatively influence economic growth while carbon emission drives a positive impact. Interestingly, a one‐way Granger causality runs from economic growth towards energy consumption, carbon emission, and agricultural land. The new research presents a comprehensive understanding of economic growth indicators, aiding policymakers, and advancing existing literature. Consequently, as a recommendation, the policymakers should constitute explicit goals for renewable energy consumption to stimulate sustainable economic growth with carbon emission reduction. [ABSTRACT FROM AUTHOR]

Covid-19 pandemic has adversely affected the different industrial sectors of Indian economy. It has thus adversely disturbed the parameters of the sustainable development in this country. This paper shows how different macroeconomic sectors of India have reacted to the adverse impact in the Indian Economy differently. Some sectors are highly affected while some sectors are not appreciably affected by the pandemic. Few major sectors have been affected badly by the pandemic. For example the highest impact on industrial growth during the pandemic has been assessed for the sectors like, mining -26.9%, manufacturing -66.6%, infrastructure -37.9%, steel - 65.2%, cement - 40%, crude oil -5.22% etc. I have studied in this paper the impact of Covid-19 pandemic on different sectors and got different findings for different sectors. The study involved with the Cointegration and Vector Error Correction Estimation of Time Series Econometrics. It may be noted that the pandemic has been going on for one and half year and therefore it is expected to be a transitory phenomenon. We, therefore, cannot expect a very long run relationship between pandemic and other sectors of the economy. That's why, the Cointegration study has been indirectly established through the estimation of Vector Error Correction model. In the estimated VEC model it has been found that there is a strong relation between changes in macroeconomic parameters and the pandemic in the short run. I have found that the growth rate in manufacture, infrastructure and mining etc have been affected adversely by the pandemic. Growth rate of different sectors have been significantly related to the 1st and 2nd lag variation of the Covid-19 dataset. The Covid-19 dataset have been found to exert negative impact on the sectors under study. [ABSTRACT FROM AUTHOR]

The significance, nature, and direction of the effect of inflation on economic growth and macroeconomic stability are contentious both in theory and empirical analysis. This paper examines the causal relationship between inflation and macroeconomic variables - interest rate, exchange rate, money supply, GDP, and fiscal deficit - in India, during the period 1986 to 2016, applying the vector correction (VECM) estimation method. The macro variables are stationary at first difference, and a cointegrating and causal relationship exists between the wholesale price index and interest rate, exchange rate, GDP, broad money, and gross fiscal deficit. The VECM estimates reveal that money supply and GDP are the most important macro variables in explaining the variations in inflation. The estimated error correction term shows that the shortrun disequilibrium is corrected by about 20% every period towards the long-run equilibrium. The impulse response results show that inflation responds positively to the money supply from the start to the 9th period. To promote economic growth and keep inflation low, money supply and budget deficits need to be rationalised. [ABSTRACT FROM AUTHOR]

Since 1991 Foreign Direct Investment(FDI) has been continuously contributing towards the economic development of Indian economy. On the basis of various literature review it can be said that foreign direct investment contributes positively towards growth domestic product(GDP) growth and employment generation in the developing countries like India. Therefore, the present study is conducted to analyse the role of the foreign direct investment on the economic growth of India for the sample period 1992 to 2019. In the study the economic growth is represented by the variables like gross domestic product(GDP) and employment generation in Indian for the time period 1992-2019. For the study various econometric tools like Augmented Dickey-Fuller(ADF) unit root test, Granger Causality test, Ordenary Least Square(OLS) regression method, Johansen Cointegration test and Error Correction test are applied. The ADF unit root test reflects that all three variables i.e GDP, FDP, and employment are stationary at 1st difference I(1). The result of Granger causality test between Foreign Direct Investment and Gross Domestic Product shows unidirectional causality from economic growth to foreign direct investment in India. But The Granger Causality test shows no casual relationship between foreign direct investment and employment generation. The OLS regression analysis shows a positive relationship between FDI and GDP in India with R square value 0.886177. But the relationship between FDI and number of employment generation is not significant as it has the P value 0.3559. Johansen Cointrgration Test shows a long run positive relation among GDP, FDI and employment generation as both trace statistic and eigen value statistic reject the null hypothesis at 5% level of significance. The result of Error Correction Model(ECM) shows that the speed of adjustment among the variables toward long run is 25.5 percent per year. As FDI has positive impact on the economic growth in India so the government of India should take various policies to attract the foreign investors to India and Increase the FDI inflows. [ABSTRACT FROM AUTHOR]

Transport infrastructure has often been observed to influence urbanization which is again associated with economic development and improvement in quality of life. However, the direction of causality between the two is unclear as it may depend on the particular context which is very much determined by the socio-economic conditions, political policy decisions, etc. Also, the role of different transport sectors (e.g. road, rail, port, etc.) may change over different phases of development and historic conditions. This study, thus, attempts to explore the causal direction between transport infrastructure and urbanization during the post-liberalization era in India (1991–2011). It has used eleven variables to represent various transport sectors and also categorizes the Indian states into leading, intermediate and lagging groups to find out how the role of various transport sectors changes with various phases of development. The study has used dynamic time series models such as Vector Error-Correction (VEC) and Vector Auto-Regression (VAR) for this purpose, which is absent in the present literature. Results showed support in favor of modernization theory of urbanization in most of the cases. • Causality between transport infrastructure and urbanization is explored. • The study focuses on the post-liberalization period in India. • Relationship changes across regions and sub-sectors of transport infrastructure. • Support in favor of modernization theory has been obtained. • Urbanization causes urban bias (in road infrastructure), mainly in lagging region. [ABSTRACT FROM AUTHOR]

The primary aim of the research is to address the question 'Matters of agriculture for economic growth?' In order to do this, over the period 1961 to 2016, we have to examine the connection between farming production and economic growth in India. In this research, the methods of vector error correction and co-integration are utilised to analyse the connection between farm production and economic growth. The Johnson test results show that the balance between agricultural production and economic development in India is long-lasting. The vector error correction test shows that University causalities extend over the long term, from economic growth to farming. This implies economic growth leads to farming, but agricultural production does not, in the long term, contribute to economic growth. But in the near term, there is a bidirectional causation between agricultural and economic growth. In the short term, economic growth leads to farming and production leads to economic growth. [ABSTRACT FROM AUTHOR]

Consistent estimation techniques need to be implemented to obtain robust empirical outcomes which help policymakers formulating public policies. Therefore, we implement the least squares (LS) and the high breakdown robust least trimmed squares (LTS) regression techniques, while using econometric regression model based on a growth equation for the two countries, namely, India and Pakistan. We used secondary annual time series data which covers a long period of 41 years. The adequacy of the time series econometric model was checked through cointegration analysis and found that there is no spurious regression. Classical and robust procedures were employed for the estimation of the parameters. The empirical results reveal that the overall fit of the model improves in case of LTS technique, while the significance of the predictors changes significantly in cases of both countries due to the removal of outliers from the data. Thus, empirical findings exhibit that the results, obtained through LTS, are better than LS techniques. [ABSTRACT FROM AUTHOR]

Purpose: Due to large amounts of coal burning, huge carbon dioxide emission and poor environmental quality, it is important to identify whether environmental Kuznets curve exists in China and India since in downward period of environmental Kuznets curve, economic growth in these countries will largely contribute to world environmental quality. Further, it helps to make a comparative analysis between China and India on how economic growth will contribute to the environmental quality in both upward and downward period of environmental Kuznets curve due to energy consumption. Design/methodology/approach: This study uses the data of carbon dioxide emission, per capita GDP and energy consumption from 1972 to 2017 to identify individual and panel-level environmental Kuznets curve of China and India. Before going to regression and causality analysis, unit root and cointegration tests are performed. Findings: This study finds the existence of environmental Kuznets curve in China and India at both individual and panel level. Further, due to high energy consumption, environmental quality in China will deteriorate at a lower rate in the long run than that of India. Next, the increase in economic growth or per capita GDP in the long run will deteriorate environmental quality at a lower rate in China than that of India. Besides, with the zero level of energy consumption and per capita GDP, the environmental quality of China will be worse than that of India. However, increase in per capita GDP after threshold level will improve environmental quality in India at a higher rate than that of China. Research limitations/implications: It helps to formalize the comparative relationship between the two large Asian economies by knowing the influence of economic growth on environmental degradation due to energy consumption. However, this study cannot conclude exactly when China and India can avail the downturn in environmental Kuznets curve. Originality/value: It firstly establishes a link among energy consumption, economic growth and environmental quality between China and India including comparative pace in both upward and downward period of environmental Kuznets curve. [ABSTRACT FROM AUTHOR]

To examine the asymmetric effects of militarization on economic growth and environmental degradation, this empirical research analyzes annual data of Pakistan and India over the period 1985–2018 using the NARDL econometric model. The empirical results show significant positive militarization effects on economic growth, while non-militarization also shows positive effects on the economic growth in Pakistan and India. Estimation showed that a 1% increase in militarization (non-militarization) led to 8.818% (3.849%) increase in GDP growth, whereas a 1% increase in militarization (non-militarization) decreased carbon emissions by − 1.034% (− 0.225%) in the long run in Pakistan, while militarization has also decreased the carbon emissions − 0.337% in India in the long run. The relationship between militarization and economic growth has an asymmetry in Pakistan and India in the short and long run, while asymmetry also exists between militarization and CO2 in Pakistan and India in the short and long run. Our findings offer significant policy implications for promoting economic growth and environmental quality in Pakistan and India. [ABSTRACT FROM AUTHOR]

The growth stimulating effect of energy has been long established in economic literature. Consequently, researchers and policy makers are trying to identify socio-economic determinants of energy consumption in both developed and developing countries to conserve energy. In the present study, we attempt to examine the non-linear and asymmetric effect of human capital and income disparity on energy demand in India over the period 1970–2014. The findings propose long-run co-integrating relationship between human capital, income inequality, and energy demand in the presence of asymmetric framework. It is found that increased level of human capital results in energy conservation as it decreases the energy demand, and higher income gap also results in lower energy demand as poor section may have restricted access to energy sources. Finally, the asymmetric causality test results reveal that only positive shock in income inequality tends to cause energy demand. From policy stand point, it can be recommended that government policies should be designed and implemented towards improvements in human capital to achieve sustainable economic growth in India since human capital found to be a significant component in energy conservation. [ABSTRACT FROM AUTHOR]

This study uses two different approaches to explore the relationship between pollution emissions, economic growth, and COVID-19 deaths in India. Using a time series approach and annual data for the years from 1980 to 2018, stationarity and Toda-Yamamoto causality tests were performed. The results highlight unidirectional causality between economic growth and pollution. Then, a D2C algorithm on proportion-based causality is applied, implementing the Oryx 2.0.8 protocol in Apache. The underlying hypothesis is that a predetermined pollution concentration, caused by economic growth, could foster COVID-19 by making the respiratory system more susceptible to infection. We use data (from January 29 to May 18, 2020) on confirmed deaths (total and daily) and air pollution concentration levels for 25 major Indian cities. We verify a ML causal link between PM2.5, CO2, NO2, and COVID-19 deaths. The implications require careful policy design. [ABSTRACT FROM AUTHOR]

The nexus of remittances and CO2 emission is very important and gathers a significant place in empirical research. This paper tries to find out the asymmetric relationship between carbon emissions, remittances, and financial development in India for the period 1980–2014. Based on the theoretical linkages, we develop a nonlinear ARDL model with the use of time series data in this study. The results of the NARDL bound test suggest that there is long-run cointegration among the variables. The findings show that positive shock in remittances causes an increase in CO2 emissions, where negative shock reduces it. The coefficient for financial development is positive but becomes statistically insignificant. Empirical results also support the existence of asymmetric long-run relationship among the variables. Based on the findings, the paper recommends the proper channelization of remittances and financial development towards environment-friendly energy sources and projects without compromising economic growth. [ABSTRACT FROM AUTHOR]

This study aims to examine the impact of economic growth, financial openness, trade openness, and energy intensity on the ecological footprint of BRICS countries for the period 1996–2016 in the framework of the environmental Kuznets curve (EKC). In the research phases, the effects of financial openness and trade openness on ecological footprint were examined both individually and as a whole using three models. The results indicate that the EKC hypothesis is not valid in all BRICS countries. Specifically, the individual results demonstrate that the EKC model using financial openness is valid only for India, while the EKC model using trade openness is valid both for India and South Africa. Furthermore, financial openness has reduced environmental pollution in India and South Africa. Trade openness has reduced environmental pollution in China and India, while it has increased in South Africa. Lastly, energy intensity has increased environmental pollution in all countries except Russia for both models. Overall, policy-makers should develop policies to reduce energy intensity in BRICS countries. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to study impacts of changes in crude oil price, money supply, fiscal deficit and effective exchange rate on India's economic growth (expressing all variables in real term). Design/methodology/approach: First, a simple macroeconomic model is formulated to this effect. Next, linear autoregressive distributed lag procedure and vector error-correction model are applied for growth empirics. Annual data are used from 1977 through 2015. Findings: Rises in real crude oil price and monetized real fiscal deficits have negative short-run and long-run effects on real economic growth. Increase in real money supply and real effective exchange rate appreciation helps promote real economic growth in both short run and long run. In all cases, there is evidence of net interactive positive feedback effects among the variables in the short run. Real effective exchange rate appreciation dampens exports, but it is helpful to imports of capital goods and crude oil that contribute to economic growth. So, the net effect on the economy may be conjecturally positive. Originality/value: To the best of the authors' knowledge, this paper is unique because of the formulation of macro-economic model pertaining to the topic and its subsequent empirical verification. Moreover, this paper seems more comprehensive than some other studies, cited in the literature review. [ABSTRACT FROM AUTHOR]

Energy is the lifeblood of the global economy, a key input to almost all the goods and services of the modern world. The primary objective of this study is to examine the relationships between per capita energy consumption and per capita GDP in India from 1970–1971 to 2014–2015. The data analysis was conducted using the three‐stage Johnson cointegration, vector autoregression and Granger causality test. The results of the study documented that there was the presence of unidirectional causality running from per capita GDP to per capita energy consumption and the absence of a long‐term equilibrium relationship between per capita energy consumption and per capita GDP in India. More significantly, India has already demonstrated an impressive ability to develop, deploy, replicate and scale up innovative, market‐based approaches to energy efficiency. The country has an excellent basis for further energy efficiency policy innovation and market creation, including a strong community of non‐governmental organisations, research institutes and think tanks active in energy efficiency. [ABSTRACT FROM AUTHOR]

India's horticulture production has shown manifold increase in the past two decades and the country is ranking second in the world only next to China. Keeping in mind, the increasing trend in horticulture production and individual product, the purpose of the current paper was to study the short run and long run relation between economic growth and horticulture production as a whole. The study used secondary data on horticulture production and Gross Domestic Product (GDP) from 2001 to 2018. The econometric tools used were Granger Causality Test, Johansen Cointegration test, VECM model and OLS. Johansen Cointegration Test showed that there is long run relation between the variables; The Vector Error Correction model and its probability output showed that there is no significant short run relation between variables except one, that is when horticulture production is considered as independent variable and GDP as dependent variable. Granger Casualty Test showed absence of cause and effect relation (both way) between the three variables. Out of all variables, OLS showed a significant relation only between GDP and horticulture production. The study revealed that horticulture production shares a linear relationship with GDP both as dependent and as independent variable, but the association does not satisfy Granger Causality Test, indicating that there was no cause and effect relation between the variables. Given that agriculture is the backbone of Indian economy, the absence of short run relation between horticulture and food grain production and food grain with GDP is a matter of concern. [ABSTRACT FROM AUTHOR]

The objective of this paper is to explore the nexus of innovation–environment and economic growth in the context of the Indian economy. To achieve the study objective, we explored the role of technological innovation, FDI, trade openness, energy use, and economic growth toward carbon emissions. Using the data of 1985–2017, the study employed ARDL bound testing and vector error correction model (VECM) methods to capture the effects of technological innovation, trade openness, FDI, energy use, and economic growth on CO2 emissions. Empirical estimation has confirmed the existence of long-run cointegration. Similarly, in the long run, it is found that trade openness, energy use, and economic growth positively reinforce CO2 emissions. In contrast, technological innovation and FDI negatively reinforce CO2 emissions in the long run. Furthermore, VECM indicates that the relationship among innovation, trade openness, and energy use is bidirectional in the long run. Whereas, unidirectional relation has been found that is coming from GDP to carbon emissions, FDI, innovation, trade, and energy use. In the short run, unidirectional link found which is coming from FDI, innovation, and energy use to carbon emission. However, the association between emissions and trade openness is bidirectional. The conclusions put forward policy implications that innovation is a way to reduce environmental degradation. [ABSTRACT FROM AUTHOR]

Rising greenhouse gas emissions is an important issue of the current time. India's massive greenhouse gas emissions is ranked third globally. The escalating energy demand in the country has opened the gateway for further increase in emissions. Recent studies suggest strong nexus between energy consumption, economic growth, and carbon emissions. This study has the objective to empirically test the aforementioned interdependencies. The co-integration test and multivariate vector error correction model (VECM) are used for the analysis and the Granger Causality test is used to establish the direction of causality. The time-series data for the period of 1971-2011 is used for the analysis. The results of the study confirm strong co-integration between variables. The causality results show that economic growth exerts a causal influence on carbon emissions, energy consumption exerts a causal influence on economic growth, and carbon emissions exert a causal influence on economic growth. Based on the results, the study suggests a policy that focuses on energy conservation and gradual replacement of fossil fuels with renewable energy sources, which would be beneficial for the environment and the society. [ABSTRACT FROM AUTHOR]

Abstract In this study, the relationship between economic growth and biomass energy consumption has been examined within the framework of the production function for Brazil, Russia, India, China, and South Africa—the BRICS countries from 1992 to 2013 using the cross-sectionally augmented Im, Pesaran and Shin (CIPS) panel unit root test, the Westerlund and Edgerton bootstrap LM panel cointegration test, and the bootstrap panel causality test that allow cross-sectional dependence. The results show that the growth hypothesis is valid in Brazil and India; however, the conservation hypothesis is valid in China and South Africa. The hypothesis that applies to Russia is the feedback hypothesis. The main finding is that BRICS countries should increase the use of biomass energy consumption to sustainable environment, promoting economic growth and reducing energy dependency. Highlights • The effect of biomass energy consumption on economic growth is investigated. • Launches BRICS countries' data for the period 1992–2013. • Heterogeneous panel data analysis are used and made inferences. • Country-specific results were obtained. • Overall, biomass energy consumption is an important for promoting economic growth and reducing energy dependency. [ABSTRACT FROM AUTHOR]

For the last three decades, both China and India are considered as the largest emerging market economies in the world. Both of these economies play an essential role in the global economy in terms of economic output and CO2 emissions. Hence, these countries are expected to play an important role in setting up environmental and sustainable development policies. Therefore, our paper aims to examine the role of natural gas and renewable energy consumptions on CO2 emissions and economic growth during 1965–2016 within a multivariate framework. The autoregressive distributed lag bounds testing approach to cointegration and vector error correction model (VECM) is employed to explore the long-run and causal nexus among the natural gas consumption, renewable energy consumption, coal and petroleum consumption, CO2 emissions, and economic growth, respectively. The empirical results show existence of long-run equilibrium association among the variables. The Granger causality results indicate that the short-run bidirectional causality between renewable energy consumption and economic growth in India, while no causality is found between these two variables in China. However, natural gas consumption causes economic growth in China whereas no causality is confirmed in India in the short-run. The findings further suggest that there is long-run bidirectional causality among the considered variables in both countries. Our paper addresses several important policy implications. [ABSTRACT FROM AUTHOR]

Understanding the causality between energy consumption, carbon emissions and economic growth is helpful for policymakers to formulate energy, environmental and economic policies. For the first time, based on nonlinear dynamics, this paper employs multispatial convergent cross mapping (CCM) to revisit the energy-carbon-economy causation for China, India and the G7 countries using both aggregate data and per capita data. The findings indicate that there are significant differences between developing countries and developed countries. A bidirectional nexus between energy consumption, carbon emissions and economic growth is found in China and India, but various causal relationships are identified in the G7 countries, including bidirectional, unidirectional and neutral nexus. The results confirm that the decoupling phenomenon is common in most G7 countries. By leveraging a variety of samples and a new approach, this study provides new evidence for policy authorities to formulate country-specific policies to obtain better environmental quality while achieving sustainable economic growth. [ABSTRACT FROM AUTHOR]

Large cap equity funds are very popular amongst the retail investors because they offer stability and sustainable returns, over a period of time. This study investigates relationship between Indian stock market index and large cap mutual funds of growth category over the period of ten years. This study has been carried out to confirm the strength positive relationship dynamics of the mutual funds and what is the range of actual contribution of index in the returns of mutual funds. To understand as to what extend effect can be visible; various tests have been carried out across 5 open-ended Equity large cap funds. After series of tests were conducted like arithmetic mean, correlation, standard deviation, causality test, co-integration analysis and vector auto regression (VAR), which concluded that unrelated to any other factors, index returns have strong positive relationship with mutual funds returns in India and contribute significantly in majority of large cap equity mutual funds returns. [ABSTRACT FROM AUTHOR]

There has been increasing focus by emerging market researchers, policymakers and regulators for investigating price discovery, relationship between future and physical market and accessible trading and risk management instruments for the benefit of various stakeholders and thus contributing to the development of literature. The central question of this paper is examining the role of influence of one market on the other and the role of each market segment in price discovery in the Indian context. Johansen Vector Error Correction Model (VECM) has been employed to examine the relationship between the spot and futures prices. The cointegration results do not confirm the existence of long-run relationship between spot and futures prices. It is thus, implied that futures prices unlikely serve as market expectations of subsequent spot prices of selected agri-commodities in India and do not help in price discovery process. [ABSTRACT FROM AUTHOR]

Purpose Understanding the role of financial intermediaries towards financial development and thereby the growth of an economy, this study aims to examine the long-run relationship between the development of banking and insurance sector and economic growth in India by covering different regimes including the regulated and the liberalized period.Design/methodology/approach For examining the long-run relationship between these sectors, the study uses VAR-VECM technique. Further, Granger causality test is used to check if there is the presence of any causal link among these sectors.Findings The findings clearly indicate long-run relationship between economic growth and the development of banking and insurance sector, while the causality results show demand following relationship in the complete period where there is bi-directional causality in the post-liberalized period from insurance to economic growth.Research limitations/implications As banking development is not found to support economic growth, this raises serious concerns towards the complex role of banks as against theory and demands further analysis to understand their role in an economy.Practical implications As causality pattern has changed from demand following to bi-directional causality, it is vital to understand the importance of liberalization towards the economic growth of the country as well as the contribution of insurance sector towards economic growth in the liberalized environment.Originality/value This is the first effort to empirically explore the relationship between economic growth and the development of banking and insurance sector in India by covering the complete period (regulated and liberalized). [ABSTRACT FROM AUTHOR]

The interrelationship between flow of Foreign Direct Investment (FDI) and the volume of country's export has been a matter of controversy among economists for many decades. This study provides some empirical evidences on the short and long run dynamic relationship between FDI and Export of India for the period of 1996-97 to 2016-17. The result of Johansen's cointegration test suggests that there exist significant positive long-run comovement between the FDI and Export in India. Further, The Vector Error Correction Model and VEC granger causality test confirms a unidirectional long and short run causal relationship between these variables. In both the cases changes in export is found to be positively impacting FDI but the reverse is not found to be true. The study is highly expected to foster some basic understanding on the dynamic relationship between these variables and is targeted offer some practical insights for financial regulators and policy makers in formulating economic policies in India. [ABSTRACT FROM AUTHOR]

The aim of this study is to explore the dynamic linkage between energy efficiency and total factor productivity ( TFP) using carbon emission and trade openness as control variables for the period of 1971-2013. For this purpose, vector error correction mechanism ( VECM) is employed to determine the direction of causality. A popular and simple measure of energy efficiency, GDP per unit of energy use and trade as a percentage of GDP are used for the proxy of trade openness. The result shows negative impact of energy efficiency on TFP, in other way energy intensity has a positive impact on the growth of TFP. Hence, the negative sign indicates a trade-off between energy efficiency and TFP. There is a unidirectional causality running from energy efficiency to TFP. Whenever energy intensity increases, the TFP level increases as well. So, in the long run, a higher TFP implies higher energy consumption. Further, TFP and carbon emission Granger cause trade openness, and there is unidirectional causality from TFP to CO2 emission. [ABSTRACT FROM AUTHOR]

The present study examines the impact of the 2008 financial crisis on the hedging effectiveness of three index futures contracts traded on the National Stock Exchange of India for near, next and far month contracts over the sample period of January 2000 - June 2014. The hedge ratios were calculated using eight methods; Naive hedging, Ederington's Model, Autoregressive Integrated Moving Average, Vector Autoregressive, Vector Error Correction Methodology, Generalized Autoregressive Conditional Heteroskedasticity, Exponential Generalized Autoregressive Conditional Heteroscedasticity and Threshold Generalized Autoregressive Conditional Heteroskedasticity. The study finds an improvement in hedging effectiveness during the post-crisis period, which implies that during the high-volatility period hedging effectiveness also improves. It was also found that near month futures contracts are a more effective tool for hedging as compared to next and far month contracts, which imply that liquidity is a more important determinant of hedging effectiveness than hedge horizons. The study also finds that a time-invariant hedge ratio is more efficient than time-variant hedging. Therefore, knowledge of sophisticated econometrical tools does not help to improve hedge effectiveness. [ABSTRACT FROM AUTHOR]

The present study empirically investigates the dynamic linkages between American Depository Receipts (ADRs) and their respective underlying stock returns of Indian stock market. Study analyzes daily data from the respective date of issue of that ADR to April 30, 2013 by applying augmented Dickey-Fuller unit root test, Johansen cointegration test, Granger causality test, vector error correction model, impulse response function and variance decomposition. The empirical result shows that both underlying stocks and ADRs are level stationary and long-run equilibrium relationship exists between them. Further, Granger causality test uncovers that ADRs lead underlying stocks. Additionally, impulse response function reveals that both underlying stocks and ADRs positively affect each other. Likewise, variance decomposition provides evidence that underlying stocks explain around half of the variance of ADRs. Major conclusion of this study is that price discovery takes place in ADR market, proposing that arrival of new information disseminates faster in ADR market. [ABSTRACT FROM AUTHOR]

The objective of this paper is to study the factors influencing commodity futures prices of the Indian commodity market. Five factors have been considered in this study, namely: spot price, the U.S. dollar exchange rate against rupees, market wide information, risk free rate of interest, and financial speculation. To test the long-run equilibrium relationships between commodity futures prices and the factors, Johansen's Cointegration test has been used. The short-run relationships have been verified by Vector Error Correction Model. Finally, the lag relationships of these factors with the commodity futures price, is modelled by Almon Polynomial Distributed Lag model. The study shows that spot price, market wide information, financial speculation and exchange rate of the US dollar influence the futures price. However, the degree of impact depends on the commodity and on the period of analysis. Additionally, risk free rate of interest does not show influence on the futures prices. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to demonstrate importance of usage of sector indices which provides insight for sector specific investment strategies and direction for suitable policy formulation for the Indian industry. It investigates long run, short run and causality relationships between eight identified sector indices and Sensex for the post subprime period. Design/methodology/approach – The study uses Vector Error Correction Model (VECM) for econometric analysis. It employs Generalized Impulse Response and Variance Decomposition analysis for developed multivariate framework in order to provide information about precise interplay of the sector indices. Findings – Long-term relationships between sector indices were determined by the usage of VECM indicating minimal benefits from diversifying investments to different sectors. Limited lead – lag short run relationships between sector indices were observed. Banking index played a predominant and integrating role in moving other indices. During this period of recovery; most sectors were protected and provided marginally better returns due to robust Banking policy. Realty and Metal were other significant drivers influencing remaining sectors contemporaneously. The study for the post subprime crisis period helps to understand the importance and behavior of interrelated sector indices and Sensex in the dynamic economic environment. Practical implications – The study clearly provides direction for sector specific investment strategies and policy formulation. Originality/value – The study highlights utility and importance of usage of sector indices. No study using sector indices for the Indian economy have been done earlier employing VAR for the post subprime crisis period. [ABSTRACT FROM AUTHOR]

This study examines the macroeconomic factors underlying FDI flows to India using cointegrating VAR with I(1) exogenous variables. The results indicate that conventional determinants such as a depreciating exchange rate, higher domestic returns, higher domestic output and better infrastructure are conducive to FDI flows to India. The results also indicate that macroeconomic instability has adverse effects while credit worthiness is conducive to FDI flows. A negative relation between trade openness and FDI is observed suggesting that FDI flows to India may be tariff jumping in nature. Empirical estimates also indicate that an increase in global FDI flows to other emerging economies reduces FDI flows to India indicating that India competes with other emerging economies in receiving FDI. Further, the results show that higher foreign output is conducive to FDI flows indicating procyclicality of FDI with economic performance of foreign countries. [ABSTRACT FROM AUTHOR]

Oil is one of the most important forms of energy and is a significant determinant of global economic performance. Commodities like oil are fairly homogeneous and internationally traded. The impact of dollar nominated oil prices on stock prices may not be quite relevant for Indian context. In this context, the study of crude oil prices in dollar terms along with the exchange rate would be more meaningful to understand the impact of oil prices on stock market. The study investigates the dynamic relationships between oil price, exchange rate and Indian stock market during 1993 to 2013. The estimated results of the Johansen's cointegration test and vector error correction model suggest that there exist a long run cointegrating relationships between crude oil price and Indian stock indices, but it cannot be said with sufficient confidence that the direction of the relation in the long run is from the oil price to the Sensex. The Granger causality test also reveals that the volatility of stock prices in India can be explained to cause the movement of oil price and exchange rate in short run. The observed relationship between oil price and stock indices is not due to the effect of the exchange rate fluctuations, because the change in exchange rate has no significant impact on oil prices or stock prices in India during the study period. The variance decomposition analysis reveals that the Indian stock prices are strongly exogenous in the sense that the crude oil price or exchange rate explains only a very small portion of the forecast variance error of the market index. Finally, from the impulse response functions analysis it is noticed that a positive shock in one variable have a persistent and prolonged effect on other variables. [ABSTRACT FROM AUTHOR]

This paper investigates the causal nexus between energy consumption, CO2 emissions, economic growth and trade in India using the Perron (1989) unit root test, Gregory and Hansen (1996) cointegration test and Vector Error Correction Model. The study results exhibit a long-run relationship between energy consumption, CO2 emissions, economic growth and trade in India. The empirical results confirm that energy consumption influences the economic activity in the short-run, implying that higher rate of economic growth is driven by consumption demand for energy in the economy. This is also well in consistent with the findings of Paul and Bhattacharya (2004) in the Indian context. Further, the study detects one way causation exist from energy use to CO2 emission and trade, and CO2 emissions to economic growth in the short-run. [ABSTRACT FROM AUTHOR]

This article examines the dynamic relationships among output, carbon emission and renewable energy generation of India and China during the period 1972 to 2011 using a multivariate vector error correction model (VECM). The results for India reveal unidirectional short-run causality from carbon emission to renewable energy generation and from renewable energy generation to output, whereas in the long run, the variables have bidirectional causality. Causalities in China give a rather different scenario, with a short-run unidirectional causality from output to renewable energy and from carbon emission to renewable energy generation. In the long run, for China, unidirectional causality is found from output to renewable energy generation, while bidirectional causality is found between carbon emission and renewable energy generation. [ABSTRACT FROM PUBLISHER]

The structural shift in India's services export since 2003-04 may be attributed to rapid expansion in international trade and investment facilitated by an increased liberalization and the use of technology. The study aims to investigate the possible co-integration and the direction of causality between software services export and non-software miscellaneous services (business, financial, communication and other miscellaneous) export and economic growth during the period 2000-01:Q1-2008-09:Q4. Granger Causality Test conducted in a Vector Error Correction Model (VECM) framework revealed unidirectional causality from software services export to economic growth. To investigate the long and short run dynamics among the variables impulse response analysis is also done. [ABSTRACT FROM AUTHOR]

Domestic and export demand of Guar Seed and its derivative Guar Gum has increased phenomenally due to its application in multi-billion dollar shale energy industry in the US. To examine the performance of Guar Seed futures market, its efficiency and price discovery is tested in the present study using robust models like Johansen's cointegration, Vector Error Correction method, Impulse Response and Variance Decomposition. Temporal relation between spot and futures prices from 2004 to 2011 studied using the above techniques indicates that both spot and futures prices observe long-run co-movement and therefore futures contracts can serve as a useful hedging instrument. In the short run, unidirectional flow of information from the futures to the spot market is observed, i.e., futures market leads the spot market for Guar Seed. Error correction is taking place in both the markets, although futures market is seen to make quicker adjustment to restore market equilibrium. But the process of adjustment to disequilibrium is slow in both the markets. This gives an indication that participation in futures market is low which makes the process of information transmission slow. [ABSTRACT FROM AUTHOR]

Abstract: This paper examines the effect of transportation (road and rail) infrastructure on economic growth in India over the period 1970–2010. Using Vector Error Correction Model (VECM), the paper finds bidirectional causality between road transportation and economic growth. It also finds bidirectional causality between road transportation and capital formation, bidirectional causality between gross domestic capital formation and economic growth, unidirectional causality from rail transportation to economic growth and unidirectional causality from rail transportation to gross capital formation. The paper suggests that expansion of transport infrastructure (both road and rail) along with gross capital formation will lead to substantial growth of the Indian economy. Therefore, within its stated scope, this study suggests that a suitable transport policy should be retained to boost transportation infrastructure and hence sustainable economic growth in India. [Copyright &y& Elsevier]

We examine the relationship between spot and futures prices for selected five agricultural commodities, namely, Chilli, Coriander, Jeera, Pepper and Turmeric. We considered all the contracts of the above commodities over a period of 36 months, from Jan 2008 to Dec 2010. The study examined the existence of unit root in the data series by employing Augmented Dickey-Fuller (ADF) test and we found the existence of long run relationship between selected spot and futures market using Johansen cointegration test and the presence of disequilibrium between markets in short run by employing Vector Error Correction Model (VECM). [ABSTRACT FROM AUTHOR]

This paper aims to explore the link between the Indian stock market and other selected stock markets in the Asia-Pacific region. Using the monthly data from January 2000 to December 2010, the stock market indices of India (SENSEX), Hong Kong (HSI), Indonesia (JKSE), Malaysia (KLSE), South Korea (KOSPI), Japan (Nikkei 225) and China (SSEC) are examined. Augmented Dickey-Fuller unit root test is performed to check for stationarity, and it is found that all the natural logarithmic values of series are stationary at their level form. Johansen and Juselius cointegration analysis suggests that Indian stock market is integrated with other stock markets in the Asia-Pacific region and there exists a long-term relationship between these markets. By implementing the Vector Error Correction Model (VECM), the short-term dynamics of the Indian stock market to the long-run equilibrium is studied and the results reveal that the short-term distortion to long-term equilibrium is around 25%. Variance decomposition results reveal that the impact of other Asia-Pacific region stock markets on Indian stock market was mild in the first two months, and from the third to fifth month, there was much increase in the contribution of the other selected markets to the variance in Indian stock market. [ABSTRACT FROM AUTHOR]

This paper investigates price discovery function among the Futures, Option and Cash market. Johansen's Cointegration, Vector Error Correlation Model and Generalized impulse Response Analysis are applied to hypothesis on daily data from NSE. Bilateral causality is observed between future and cash, option and cash, future and option. The result reveals that futures market is a useful price discovery vehicle The lower cost of transaction and huge trading volume in the future market may be the reason that future market seem to be informationally more efficient than their corresponding spot market. The paper revealed that both cash and options markets are influenced by the price movement in the futures market. The paper also found evidence of causality from the future markets to cash and option is much stronger as compared to the causality from the cash to future market and causality from cash to option is stronger as compared to option to cash. [ABSTRACT FROM AUTHOR]

Savings and investment are considered as the most important macroeconomic instruments to enhance the economic growth of any country. Thus, this paper is an attempt to examine the fundamental relationship between domestic savings and investment in the perspective of foreign capital thereby explaining the Feldstein and Horioka paradox in the context of Indian economy over the sample period spanning from 1990-91 to 2009-10. The application of cointegration test indicates that the variables of the study have long-run equilibrium relationships among themselves. Furthermore, the application of vector error correction models with and without foreign capital provides the evidence of the existence of bi-directional causality between domestic savings and investment with and without foreign capital. This supports the findings of Feldstein and Horioka while indicating greater level of international capital mobility. [ABSTRACT FROM AUTHOR]

This paper investigates the nexus between CO2 emissions (CO2E), GDP, energy use (ENU), and population growth (PG) in India from 1980–2018 by comparing the "vector error correction" model (VECM) and "auto regressive distributed lag" (ARDL). We applied the unit root test, Johansen multi-variate cointegration, and performed a Variance decomposition analysis using the Cholesky approach. The VECM and ARDL-bound testing approaches to cointegration suggest a long-term equilibrium nexus between GDP, energy use, population growth and CO2E. The empirical outcomes show the existence of a long-term equilibrium nexus between the variables. The Granger causality results show that short-term bi-directional causality exists between GDP and ENU, while a uni-directional causality between CO2E and GDP, CO2E and ENU, CO2E and PG, and PG and ENU. Evidence from variance decomposition indicates that 58.4% of the future fluctuations in CO2E are due to changes in ENU, 2.8% of the future fluctuations are due to changes in GDP, and 0.43% of the future fluctuations are due to changes in PG. Finally, the ARDL test results indicate that a 1% increase in PG will lead to a 1.4% increase in CO2E. Our paper addresses some important policy implications. [ABSTRACT FROM AUTHOR]

• We analyze the effects of ICT on economic performance and energy consumption of selected South Asian economies. • In the long-run, ICT significantly and positively contributed to the economic growth of India only. • India is the only country in South Asia that has achieved energy efficiency as a result of increased use of ICT. • GDP per capita has a positive and significant impact on energy consumption in both Pakistan and India. • South Asian economies try to follow their regional partner, India. This study contributes to the literature by analyzing the effects of information and communication technology (ICT) on economic performance and energy consumption of selected South Asian economies i.e. Bangladesh, India, Pakistan and Sri Lanka for the period of 1990–2018. For empirical analysis, we employed the bounds testing approach of cointegration and error correction modelling. The findings of the study confirm that, in the long-run, ICT significantly and positively contributed to the economic growth of India only. Similarly, India is the only country in South Asia that has achieved energy efficiency as a result of increased use of ICT. However, energy consumption proved to be an important determinant of GDP per capita in India and Pakistan. Also, GDP per capita has a positive and significant impact on energy consumption in both India and Pakistan. These results imply that South Asian economies try to follow their regional partner, India, in increasing the role of ICT in their economies, which on one side will boost their economic growth and on the other side will help them in achieving energy efficiency. Moreover, the energy conservation policy could prove detrimental to South Asian economies. [ABSTRACT FROM AUTHOR]

India is one of the world's largest emerging economies and, therefore, has a critical role to play in ensuring global sustainability, while the Indian economy is also prioritizing energy security. The paper explores the dynamic linkages between energy security captured through national-level energy use, foreign direct investment (FDI), economic output, carbon emissions, and trade openness in India spanning the period from 1978 to 2016 in a multivariate framework based on the theoretical premise of an Environmental Kuznets Curve. Time series econometric modelling based on the ARDL model and VECM Granger causality tests are employed for this purpose. The results confirm the presence of a co-integrating relationship and finds a strong energy-output–CO 2 –FDI long-run nexus. A 1% increase in FDI results in a 0.013% reduction in energy use. Energy use is found to be Granger caused by output, carbon emissions, FDI and trade openness in the long-run. The adoption of energy-efficient techniques through FDI is essential for reducing carbon emissions in India based on our findings. The Indian government should also galvanize FDI inflow in the renewable energy sectors by assuring incentives to investors to concurrently achieve favorable macroeconomic outcomes and ensure sustainable economic development. These are globally important policy lessons for other developing and emerging economies. • India is one of the world's largest emerging economy. • Dynamic linkages between energy, economy and the environment is studied for India. • Multivariate modelling framework is based on Environmental Kuznets Curve. • A 1% increase in FDI results in 0.013% reduction in energy use. • A long-run nexus relationships exists between the energy, economy and the environment. [ABSTRACT FROM AUTHOR]