Search results

The objective of this study is to explore the dynamic inter-linkage between foreign aid and economic growth in Sri Lanka over the period of 1960–2018.

Both exploratory and inferential data analysis tools have been employed to examine the objective of this study. The exploratory data analysis covered the scatter plots, confidence ellipse with kernel fit. The inferential data analysis included the augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests, the autoregressive distributed lag (ARDL) Bounds co-integration technique and the Granger causality test.

The test result of exploratory data analysis indicates that there is a positive relationship between foreign aid and economic growth. The ADF and PP unit root tests results indicate that the variables used in this study are stationary at their 1st difference. The co-integration test result confirms the presence of long-run relationship between foreign aid and economic growth in Sri Lanka. The estimated coefficient of foreign aid in the long-run and the short-run shows that foreign aid has a positive relationship with economic growth in Sri Lanka. The estimated coefficient of error correction term indicates that approximately 26.6% of errors are adjusted each year and further shows that the response variable of economic growth moves towards the long-run equilibrium path. The Granger causality test result shows that foreign aid in short-run Granger causes economic growth in Sri Lanka which means that one-way causality from foreign aid to economic growth is confirmed. Further, the estimated coefficient of error correction term confirms that there is the long-run Granger causal relationship between foreign aid and economic growth in Sri Lanka.

The findings of this study have some important policy implications for the design of efficient policy related to foreign aid and economic growth, the knowledge of which will help follow sustainable foreign aid and growth nexus.

This study contributes to the existing literature by using the newly introduced ARDL Bounds cointegration technique to investigate the dynamic inter-linkage between foreign aid and economic growth in Sri Lanka.

This study investigates the long-run relationship between workers' remittances and human capital formation in Sri Lanka by using the macro-level time series data during the period of 1975–2020.

In this study, the augmented Dickey–Fuller (ADF) and Philips–Perron (PP) unit root tests, the autoregressive distributed lag (ARDL) bounds cointegration technique, the Granger causality test, the forecast error variance decomposition technique and impulse response function analysis were employed as the analytical techniques.

In accordance with the results of unit root tests, the variables used in this study are mixed order. Results of cointegration confirm that workers' remittances in Sri Lanka have both long-run and short-run beneficial relationship with human capital formation. The Granger causality test results indicate that there is a two-way causal relationship between workers' remittances and human capital formation. The results of forecast error variance decomposition expose that innovation of workers' remittances contributes to the forecast error variance in human capital in bell shape. Further, the empirical evidence of impulse response function analysis reveals that a positive standard deviation shock to workers' remittances has an immediate significant positive impact on human capital formation in Sri Lanka for a period of up to ten years.

This research provides insights into the workers' remittances in human capital formation in Sri Lanka. The findings of this study provides evidence that workers' remittances help to produce human capital formation.

By using the ARDL Bounds cointegration and other techniques in Sri Lanka, this study fills an important gap in academic literature.

The paper investigates the dynamic relationship between oil prices, the USA dollar exchange rate and the Saudi stock market index.

The authors perform a novel dynamic simulated the autoregressive distributed lag (ARDL) on weekly data from 2010 to 2021.

The authors' work reveals three main results: First, a cointegration relationship exists between oil prices and the Saudi stock market index. Second, the Saudi stock market is strongly affected by fluctuations in oil prices in both the short and long run. Third, the exchange rate of the USA dollar has a slight influence on the movements of the Saudi stock market. The simulations show that the Saudi stock market index has a long-run upward trend after an oil price shock, while the dollar index rises moderately after a similar shock. Moreover, the first months of the COVID-19 pandemic coincided with a significant decline in the Saudi stock market index, particularly the substantial drop in oil prices.

These findings encourage domestic and foreign investors to benefit from an upward trend in oil prices, especially after the opening of the Saudi market to foreign investment. On the other hand, it raises questions about the Saudi economy's dependence on oil as the sole vehicle for output growth. It highlights the urgent need for diversification and productivity growth in the non-oil sector and other renewable natural resources to increase Saudi competitiveness.

The novelty of the research lies in the following. First, the authors apply one of the latest developments in time-series modeling techniques. This dynamic ARDL simulation model provides a worthwhile alternative way to explore dynamic correlations in the short and long run and assess the choc effects. Secondly, the study would enable us to track the impact of the COVID-19 health crisis on the Saudi stock market.

The main objective of this research is to investigate if there is a long-term relationship between “terrorism” and sustainable “economic growth” in Big Ten Countries.

The data was tested via Panel ARDL Analysis. The growth rate (GR) is the dependent variable, and the “Global Terror Index (GTI)” is the independent variable as the terror indicator. The ratio of Foreign Direct Investment (FDI) to the Gross Domestic Product (GDP), and the ratio of External Balance (EB) to Gross Domestic Product (GDP) are included in the model as the control variables due to their effect on the growth rate. A Panel ARDL analysis is conducted to examine the existence of long-term co-integration between terror and the economy. The planning of the study, the formation of its theoretical and conceptual framework, and the literature research were carried out in 2 months, and the collection of data, the creation of the methodology and the analysis of the analyzes were carried out in 2 months, the interpretation of the findings and the development of policy recommendations were carried out within a period of 1 month. The entire study was completed in a total of 5 months.

Results showed that “Terror” has a negative impact on “Growth Rate” in the long term while “External Balance” and “Foreign Direct Investment” positively affect the Growth Rate. The coefficients for the short term are not statistically significant.

The sample is only limited to Big Ten including China, India, Indonesia, South Korea, Argentina, Brazil, Mexico, Turkey, Poland and South Africa. The period for annual data collection covers the years between 2002 and 2019 and due to the unavailability of data.

Considering the risks and the mutual negative effect that turns into a vicious circle between terrorism and the economy, it is necessary to eliminate the problems that cause terrorism in the mentioned countries, on the one hand, and to develop policies that will improve economic performance on the other.

Trustful law enforcement bodies have to be established and supported by all technological means to prevent terror. The conditions causing terror have to be investigated carefully and the problems causing terror or internal conflict have to be solved. International cooperation against terrorism has to be strengthened and partnerships, information, experience sharing have to be supported at the maximum levels.

It is certain that terror might have a negative influence on the performance of economies. But the limited number of studies within this vein and the small size of their sample groups mostly including single-country case studies require conducting a study by using a larger sample group of countries. Big Ten here represents at least half of the population of the world and different regions of the Globe.

This study aims to examine the symmetric and asymmetric impact of external debt on inflation in Sudan from 1970 to 2020 within a multivariate framework by including money supply and the nominal effective exchange rate as additional inflation determinants.

The authors utilize an Auto Regressive Distributed Lag (ARDL) model to examine the symmetric impact of external debt on inflation, while the asymmetric impact is examined using a Nonlinear ARDL (NARDL) model. The existence of a long-run relationship between inflation and external debt is tested using the bounds-testing approach to cointegration, and a vector error-correction model is estimated to determine the short parameters of equilibrium dynamics.

The linear ARDL model results show that external debt has no statistically significant impact on inflation in the long run. On the contrary, the results of the NARDL model show that positive and negative external debt shocks statistically affect inflation in the long run. The estimated long-run elasticity coefficients of the linear and nonlinear ARDL models reveal that the domestic money supply has a statistically significant positive impact on inflation. In contrast, the nominal effective exchange rate has a statistically significant negative impact on inflation.

The reliance on symmetric analysis may not be sufficient to uncover the existence of a linkage between external debt and inflation. Proper external debt management is crucial to control inflation rates in Sudan.

To date, no empirical study has assessed the external debt-inflation nexus and its potential asymmetry in Sudan, and the current study aims to fill this gap in the literature.

This study aims to assess the nexus among oil price (OP), renewable energy consumption (REC) and trade balance (TB) for India using annual time series data for the time period 1985–2019. In particular, the authors examine whether REC improves India's TB in the context of high oil import dependence.

The study uses autoregressive distributed lags (ARDL) bound testing approach that has the advantage of yielding estimates of long-run and short-run parameters simultaneously. Moreover, the small sample properties of this approach are superior to other multivariate cointegration techniques. Fully modified ordinary least square (FMOLS) and dynamic ordinary least squares (DOLS) are also applied to test the robustness of the results. The causality among the series is investigated through block exogeneity test based on vector error correction model.

The findings based on ARDL bounds testing approach indicate that OPs exert a negative impact on TB of India in both long run and short run, whereas REC has a favorable impact on the TB. In particular, 1% increase in OPs decreases TBs by 0.003% and a 1% increase in REC improves TB by 0.011%. The results of FMOLS and DOLS corroborate the findings from ARDL estimates. The results of block exogeneity test suggest unidirectional causation from OPs to TB; OPs to REC and REC to TB.

The study underscore the importance of renewable energy as a potential tool to curtail trade deficits in the context of Indian economy. Our results suggest that the policymakers must pay attention to the hindrances in augmentation of renewable energy usage and try to capitalize on the resulting gains for the TB.

Climate change is a major challenge for developing countries like India. Renewable energy sector is considered an important instrument toward attaining the twin objectives of environmental sustainability and employment generation. This study underscores another role of REC as a tool to achieve a sustainable trade position, which may help India save her valuable forex reserves for broader objectives of economic development.

To the best of the authors’ knowledge, this is the first study that probes the dynamic nexus among OPs, REC and TB in Indian context. From a policy standpoint, the study underscores the importance of renewable energy as a potential tool to curtail trade deficits in context of India. From a theoretical perspective, the study extends the literature on the determinants of TB by identifying the role of REC in shaping TB.

This paper aims to empirically examine the impact of economic development and energy consumption in Saudi Arabia (the leading OPEC giant and the Arab energy icon country) between 1971 and 2015, whilst incorporating globalization, financial development and capital accumulation.

This study uses econometric tools and the analytical framework based on the autoregressive distributed lag (ARDL) model.

The study found that, unlike economic development, globalization and financial development increased energy consumption. Also, capital accumulation created a boost in the country’s energy consumption. Results of variance decomposition indicate that the innovative shocks in globalization and financial development affected energy consumption at the rates of 15.28% and 28.98%, respectively, over 15 years’ period, while shocks in capital accumulation affected energy consumption at a rate of only about 1.24%. In addition, the results of impulse response function show that globalization and economic development were highly responsive to shocks in financial development, and capital accumulation greatly spurred financial development.

The findings of this study have implication for promoting an efficient and sustainable energy systems that enhance sustainable development based on the accrued benefits of globalization, financial development and capital accumulation.

Given the increasing level of globalization, financial development and energy consumption, our study uses econometric tools and the analytical framework based on the ARDL model to revisit how energy consumption is influenced by economic development in Saudi Arabia by incorporating other determinants of energy consumption such as globalization, financial development and capital accumulation. The results were validated based on the innovative accounting.

This study aims to explore the short-run and long-run relationships and causality between economic growth and financialization in the new member states (NMS-11) and to provide some policy implications drawn from the empirical findings.

The autoregressive distributed lag (ARDL) bounds test approach to cointegration with the vector error correction model and the cumulative sum of squares (CUSUMQ) test for stability of functions is used between 1995q1 and 2021q4 to examine the existence of cointegration, relationships and causality between economic growth and financialization.

The findings of the ARDL bounds test demonstrate that the variables included in the models are bound together in the long run, as confirmed by the associated equilibrium correction. The estimated models indicate that the association between selected variables and economic growth is stronger and more statistically significant in the short run compared with the long run. Also, for NMS-11 understudied countries, short-run causality prevails over long-run causality. The changes in the level of financialization have a significant negative effect on the growth rates in the short run, which aligns with findings from previous empirical studies.

This study extends the existing very limited literature about short-run and long-run relationships and causality among economic growth and financialization, including inflation and unemployment variables, to determine their link in the NMS-11. Specifically, the present study reveals that the current level of financialization hampers economic growth and promoting such economic policies further can have adverse effects on the overall economic growth.

The purpose of this study is to explore the intricate relationship between oil prices, house prices in the UK and Norway, and the mediating role of gold and stock prices in both the short- and long-term, unraveling these complex linkages by employing an empirical approach.

This study benefits from a comprehensive set of econometric tools, including a multiequation vector autoregressive (VAR) system, Granger causality test, impulse response function, variance decomposition and a single-equation autoregressive distributed lag (ARDL) system. This rigorous approach enables to identify both short- and long-run dynamics to unravel the intricate linkages between Brent oil prices, housing prices, gold prices and stock prices in the UK and Norway over the period from 2005:Q1 to 2022:Q2.

The findings indicate that rising oil prices negatively impact house prices, whereas the positive influence of stock market performance on housing is more pronounced. A two-way causal relationship exists between stock market indices and house prices, whereas a one-way causal relationship exists from crude oil prices to house prices in both countries. The VAR model reveals that past housing prices, stock market indices in each country and Brent oil prices are the primary determinants of current housing prices. The single-equation ARDL results for housing prices demonstrate the existence of a long-run cointegrating relationship between real estate and stock prices. The variance decomposition analysis indicates that oil prices have a more pronounced impact on housing prices compared with stock prices. The findings reveal that shocks in stock markets have a greater influence on housing market prices than those in oil or gold prices. Consequently, house prices exhibit a stronger reaction to general financial market indicators than to commodity prices.

This study may have several limitations. First, the model does not include all relevant macroeconomic variables, such as interest rates, unemployment rates and gross domestic product growth. This omission may affect the accuracy of the model’s predictions and lead to inefficiencies in the real estate market. Second, this study does not consider alternative explanations for market inefficiencies, such as behavioral finance factors, information asymmetry or market microstructure effects. Third, the models have limitations in revealing how predictors react to positive and negative shocks. Therefore, the results of this study should be interpreted with caution.

These findings hold significant implications for formulating dynamic policies aimed at stabilizing the housing markets of these two oil-producing nations. The practical implications of this study extend to academics, investors and policymakers, particularly in light of the volatility characterizing both housing and commodity markets. The findings reveal that shocks in stock markets have a more profound impact on housing market prices compared with those in oil or gold prices. Consequently, house prices exhibit a stronger reaction to general financial market indicators than to commodity prices.

These findings could also serve as valuable insights for future research endeavors aimed at constructing models that link real estate market dynamics to macroeconomic indicators.

Using a variety of econometric approaches, this paper presents an innovative empirical analysis of the intricate relationship between euro property prices, stock prices, gold prices and oil prices in the UK and Norway from 2005:Q1 to 2022:Q2. Expanding upon the existing literature on housing market price determinants, this study delves into the role of gold and oil prices, considering their impact on industrial production and overall economic growth. This paper provides valuable policy insights for effectively managing the impact of oil price shocks on the housing market.