Cointegration Regression Research Articles

The Effect of Crude Oil Price and Inflation on Algae Export in Indonesia

The aim of this study is to test and analyze the long-term and short-term effects of crude oil prices and inflation on algae export in Indonesia. The effects were tested using multiple cointegration regression and error correction models. Furthermore, annual time series data from 2012 to 2021 was used in this study and the results of the analysis show that in the long term, crude oil price and inflation have a positive effect on algae export. While in the short term, only crude oil price positively influences its exportation.

Nexus between greenhouse gas emissions and its determinants: The role of renewable energy and technological innovations towards green development in South Korea

The environmental challenges that have arisen as a result of rapid economic growth have become a hindrance to social progress. This article examines the effects of energy consumption, urbanization, industrialization, economic development, and technological innovations on South Korea's greenhouse gas (GHG) emissions. Using data from 1990 to 2021 and the autoregressive distributed lag (ARDL) method for empirical investigations, the findings indicate that fossil fuel energy consumption, urbanization, industrialization, and economic expansion all pose a threat to environmental sustainability due to their positive impact on GHG emissions. In contrast, the results suggest that renewable energy usage and technological innovations improve environmental sustainability by reducing GHG emissions in both the short and long term. In addition, the findings were validated using Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegration Regression (CCR) techniques. The most significant contribution is that the findings of this study provide various policy recommendations for achieving environmental sustainability and net zero emissions in South Korea.

The symmetric and asymmetric effects of climate change on rice productivity in Malaysia

The current study aims to examine the symmetric and asymmetric effects of climate change (CC) on rice productivity (RP) in Malaysia. The Autoregressive-Distributed Lag (ARDL) and Non-linear Autoregressive Distributed Lag (NARDL) models were employed in this study. Time series data from 1980 to 2019 were collected from the World Bank and the Department of Statistics, Malaysia. The estimated results are also validated using Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegration Regression (CCR). The findings of symmetric ARDL show that rainfall and cultivated area have significant and advantageous effects on rice output. The NARDL-bound test outcomes display that climate change has an asymmetrical long-run impact on rice productivity. Climate change has had varying degrees of positive and negative impacts on rice productivity in Malaysia. Positive changes in temperature and rainfall have a substantial and destructive impact on RP. At the same time, negative variations in temperature and rainfall have a substantial and positive impact on rice production in the Malaysian agriculture sector. Changes in cultivated areas, both positive and negative, have a long-term optimistic impact on rice output. Additionally, we discovered that only temperature affects rice output in both directions. Malaysian policymakers must understand the symmetric and asymmetric effects of CC on RP and agricultural policies that will promote sustainable agricultural development and food security.

Are environmental damage and export concentration the major threats for the long-run economic growth in Bangladesh?

Using World Bank and IMF data over the period 1990-2018, this research explores the determinants of economic growth in Bangladesh with particular attention to environmental degradation and export concentration variables. An ARDL (Autoregressive Distributed Lag) bound testing approach is employed as an estimation strategy with FMOLS (Fully Modified Ordinary Least Squares) and CCR (Canonical Cointegrating Regression) for cross-checking the results. The obtained results confirm that CO2 emissions, consumption expenditure, export concentration, remittances, and inflation are the main driving forces for the long-run economic growth in Bangladesh where the effects of the first two variables and the last three variables are positive and negative, respectively. The study also reveals the dynamic short-run relationships among the chosen variables. Environmental pollution and export concentration are found to be the barriers to economic growth; therefore, the country should take necessary steps to mitigate this problem for ensuring long-run sustainable economic development.

How China is mitigating resource curse through infrastructural development?

With the aim to stimulate economic activities, an effective and stable financial system is indispensable in each country, and meanwhile, natural resources should help to promote economic and financial development in that country. Motivated by this, we examine the effect of natural resources on financial development with the cointegration method, the cointegration regression (namely FMOLS and DOLS), and wavelet coherence approach in China. Mainly, we take the effect of transportation infrastructure into account and utilize economic growth as a control variable. The cointegration test indicates a long-run cointegration equation among natural resources, transportation infrastructure, economic growth, and financial development. Natural resources have a negative impact on financial development, which means the resource curse hypothesis exists, while transportation infrastructure and economic growth play a positive role in enhancing financial development in China. Furthermore, the results of a wavelet coherence analysis show that transportation infrastructure, natural resources, and financial development are all vulnerable at distinct frequencies. The results of FMOLS and DOLS are also supported by wavelet coherence. Our results have significant policy recommendations, indicating that efficient use of natural resources is required to promote sustainable and sound financial development.

Powering Qatar’s agricultural growth: Examining the link between electricity prices and development

This study analyzes the impact of electricity prices on the development of the agriculture sector in Qatar, using annual data from 2003 to 2019. Its findings contribute to addressing a gap in current literature and offer valuable perspectives on the sector’s obstacles and potential prospects. An extended Cobb-Douglas production function was employed as a theoretical framework, in conjunction with several econometric techniques, including Fully Modified Least Squares, Dynamic Ordinary Least Square, Canonical Cointegration Regression, and Gets, to analyze the persistent relationship between electricity prices, and the gross value added of the agriculture sector. Our research found that electricity prices exert a positive effect on agricultural development. Although the magnitude of the impact was small over the long term, it remained statistically significant. Specifically, the elasticity of the electricity prices ranged between 0.097–0.11, whereas the elasticity of another examined variable, labor productivity, was also positive and ranged between 0.67–0.74. These empirical findings support the ongoing government policy to reform energy prices, increase vegetable production using modernized hydroponic systems, and reduce groundwater use for irrigation, among other policies to sustain food production. Clearly, if these policy options are managed properly, the agriculture sector can play a significant role in diversifying the economy, maintaining environmental conditions and improving food sustainability.

Does Savings Culminate in Economic Growth? An Investigation Using Cointegrating Regression

One of the most controversial areas in macroeconomics is the saving–growth nexus. This study is an attempt to examine this relationship in the case of Nigeria. Time series data over the period 1981 to 2020 were used. The analysis was based on Augmented Dickey–Fuller (ADF) test, Johansen cointegration test, Dynamic Ordinary Least Squares (DOLS) technique, and Pairwise Granger Causality tests. The results of the DOLS estimation techniques for cointegrating regression show a positive relationship between gross domestic savings (InGDS) and gross domestic product growth (InGDP) (economic growth) in the long run. The results supported the neoclassical viewpoint that saving rate is positively or directly related to the Gross Domestic Product. However, the Pairwise Granger Causality Tests showed no causality between InGDS and InGDP. Nonetheless, policy makers in the country need to stimulate savings the more in order to further increase economic growth. They should encourage the integration of the formal and informal sectors as this would have a strong positive impact on domestic savings. This will deepen the financial sector and assist in mobilizing the much needed savings that will engender investment and growth in Nigeria. In the same vein, policies should be pursued to reduce real interest rate so as to increase private sector investment.

Investigations into Ghana’s Macroeconomics Relationship between Inflation and Unemployment. Quantitative Analysis Based on Time Series in the Years 1990-2014.

Indicators of a country's economic well-being include the unemployment rate and inflation rates. The empirical relationship between inflation and unemployment in Ghana's economy is explained and analyzed in this paper. It appears that the most hotly debated statistics in Ghana are the rate of inflation and the rate of unemployment, yet there is lack of knowledge about the connection between unemployment and inflation, it is imperative that we gain this understanding. In addition, the tax rate, compensation for employees, participation in the labor force, and enrolment in tertiary education are all indicators of unemployment. A quantitative framework is used to explain the empirical relationship between inflation and unemployment in Ghana and determine the sensitivity of unemployment to changing inflation levels using co-integration regression, causality, correlation, and sensitivity analyses. The Augmented Dickey-Fuller test of stationarity was adopted, after which the Granger causality method was used to determine causation between inflation and unemployment The study relied on World Bank data from 1990 to 2014 and found a strong correlation between unemployment and inflation in the Ghanaian economy which is also consistent with Haug and King (2011), whereas Onwioduokit (2006) found a negative correlation. The model's variables are stationary, and the findings of the causality test point to a link between Ghana's high unemployment rate and its high inflation rate. The unemployment will rise by 3.2795 percent if inflation rises by 1 percent. I therefore recommend that the Ghanaian economy maintain a low and stable inflation rate in order to reduce unemployment, equivocally, thanks to the findings of this research. There is evidence to suggest that inflation has a long-term positive impact on and is a direct cause of unemployment in Ghana, so policymakers and the government should put more effort into keeping inflation low in order to address this problem. Since inflation levels can be used to measure unemployment levels to some extent and the R-squared is significantly low in the regression result, the measurement of inflation should be done more accurately to avoid errors. Ghana's Statistical Service must devote more resources to ensuring that the CPI basket includes only relevant items and should be given the appropriate weights. Real-world impact should be reported in relation to other indicators or factors to make it more comprehensible. For example, an increase in unemployment of z percentage points could be explained by an increase in the price of an item purchased on the market rising by y percentage points. Finally, the researcher recommends that other economic indicators, such as employee compensation, tax rate, labor force participation rate, and tertiary school enrollment, be taken into consideration as potential indicators of unemployment in African economies.

A dual risk perspective of China's resources market: Geopolitical risk and political risk

Currently, literature is exploring the factors affecting the economic growth of countries and regions. However, there is still a gap in empirical research regarding the connection between risk factors, natural resources, and economic growth, particularly in emerging economies. This study examines the influence of geopolitical risk, political risk, natural resources, and exports on China's economic development during 1983–2021. The Bayer-Hanck combined cointegration results verified the cointegration association between the variables. Using a novel method of movement quantile regression, this study found that geopolitical risk, mineral rents, and exports are positive, while political risk and oil rents adversely affect China's economic growth. These results are robust and consistent as validated by parametric (dynamic ordinary least square, canonical cointegration regression, and fully modified ordinary least square) and non-parametric (bootstrap quantile regression and simple quantile regression) estimators. Based on the empirical results, this study suggested lowering the risk factors and efficiently exploiting and utilizing natural resources for sustainable development.

How Oil Price Changes Affect Inflation in an Oil-Exporting Country: Evidence from Azerbaijan

This research study aims to explore the inflationary effects of oil price rises in the case of Azerbaijan. The study covers 1997–2021 yearly data to estimate long- and short-term impacts while considering “oil price—money supply” interactions. Autoregressive Distributed Lag Bounds testing (ARDLBT), Fully Modified Ordinary Least Squares (FMOLS), and Canonical Cointegration Regression (CCR) cointegration methods are applied simultaneously. The research findings are: (1) oil price is a significant long-term determinant of inflation in Azerbaijan, affecting overall prices directly and indirectly via the money supply; (2) the effect is statistically significant in the short-term and happens indirectly, moderated by the money supply; and (3) oil price moderates the impact of the money supply over inflation in the short-term. A major limitation of the current study is that it omits the possible moderation impact of oil prices over inflation in Azerbaijan through the resulting inflationary pressure due to oil price rises in trade-partner countries and the government’s fiscal policy. Research findings require the Central Bank to consider indirect effects of oil price changes, especially due to money supply changes, when targeting inflation and addressing policy sustainability in Azerbaijan.

Effects of Monetary Policy on Inflation in Ethiopia: Using ARDL Co-Integration Approach

Although many studies on linkage between monetary policy and inflation have been done in both developing and advanced countries, Notably Ethiopia has not featured in the cross-country studies that have included some of the Sub-Saharan African countries. Similar studies in Ethiopia haven not been conducted by including all necessary variables. Therefore, the study is to empirically investigate the effects of monetary policy on inflation in the case of Ethiopia. The study covers the annual data from 1988 to 2021 using time series ARDL co-integration regression analysis. The consumer price index is used as a proxy of inflation. The empirical results suggest that money supply, the openness of trade and the real effective exchange rate exerts a positive and statistically significant effect on inflation, in the long run, and real gross domestic product and real lending rate have a negative and statistically significant effect on inflation in the long run. The estimate of the speed of adjustment coefficient found in this study indicates that about 21.4 per cent of the variation in the inflation from its equilibrium level is corrected within a year. Based on the findings of the empirical analysis, the study recommends that Spends the money to the economy should not exceed the country's production of goods and services. Government should implement major changes to ensure that more of the money in circulation is in the productive sector.

Natural resources extraction and COP26 target: Evaluating the role of green finance

Efforts concerning environmental recovery are recently attracting scholars' and authorities' attention, particularly in emerging countries. Regarding the COP-26 target, China has increased its share of green finance and the extraction of natural resources during the last few decades. Such an increase in these fields motivates this study to examine the influence of green finance and natural resource extraction on China's carbon (CO2) emissions from 1989 to 2020. This study also considers the role of economic growth by estimating a linear model. This study uses time series fully modified ordinary least square canonical cointegration regression and dynamic ordinary least square. The estimated results asserted that economic growth, green finance, and natural resources are the significant drivers of environmental degradation. This study also estimated a non-linear model that included increased (squared) green finance and (squared) natural resource extraction. This time, the study provides evidence that increased green finance and natural resource extraction degrade CO2 emissions and improve environmental quality. The robustness of results is validated via robust least squares. Besides, the results indicate a bidirectional causal association between CO2 emissions, economic growth, natural resources, and green finance. This study suggests the sustainable extraction and consumption of natural resources and enhancement in green finance related instruments to attain the COP-26 target.

Electricity production, government effectiveness eco-innovation, and public health: Novel findings in the context of sustainability policies

In the recent world of catastrophe, scholars and policymakers have empirically examined the influence of various economic and financial instruments on environmental quality. Still, the literature is limited in terms of displaying the factors affecting public health, particularly in case of China. This study aims is to examine the nexus between green electricity, government efficiency, and health issues to draw novel policies. The current study investigates role of electricity production, eco-innovation and institutional factors for overall public health in China using the data from 2000Q1-2021Q4, which is a novel contribution to the existing literature. For empirical analysis, the study employs cointegration analysis, quantile regression and fully modified ordinary least square methods. The empirical result found the validity of the long-run equilibrium relationship. However, the asymmetric distribution of all variables allows this study to use a non-parametric “quantile regression” approach. The empirics depicts that economic growth and renewable electricity production positively influence public health–increase health-related issues. On the contrary, eco-innovation and government effectiveness substantially reduces public health issues. The empirical results are authenticated by the applying three parametric approaches – fully modified ordinary least square, canonical cointegration regression, and dynamic ordinary least square. To explore the causal connection between the variables, this study uses granger causality test – validates the presence of unidirectional and bidirectional causalities between variables. This study suggests enhancement in government effectiveness, expenditure, and eco-innovation to reduce critical health issues in the country.

Renewable Energy, Urbanization, Financial Development and Environmental Degradation in the ASEAN Countries

The ASEAN countries have increasingly become a new economic force in the global economy. Economic growth and development have significantly improved living standards. However, environmental degradation in the region has been significantly deteriorating. This study examines the long-term effects of renewable energy consumption, urbanization and financial development on environmental degradation in ASEAN countries from 1995 to 2020. Various econometric techniques are used, including the fully modified OLS, dynamic OLS, canonical cointegration regression and Ganger’s causality relationship. The empirical results indicate that increased renewable energy consumption and extended urbanization reduce environmental degradation. However, economic growth and financial development lead to increased environmental degradation. Granger’s causality relationship analysis confirms a bidirectional linkage between renewable energy consumption and urbanization, urbanization and financial development, and urbanization and environmental degradation. Financial development and urbanization also have a bidirectional causality relationship with economic growth. Finally, the results confirm a unilateral causality relationship between environmental degradation financial development and urbanization. The governments of the ASEAN countries will need to consider the significant roles of renewable energy usage and urbanization in their policies in achieving sustainable economic growth and development and improving environmental quality.

Fiscal capacity and public health expenditure in Zimbabwe

Public healthcare financing is important for achieving one of the 2030 Sustainable Development Goals (SDGs), the goal of healthy living and well-being (SDG3). Understanding the impact of fiscal capacity on public healthcare financing helps in assessing the government’s commitment towards attaining SDG3. The study sought to investigate the relationship between fiscal capacity and public health expenditure in Zimbabwe. An Autoregressive Distributed Lag (ARDL) model was estimated using annual time series data for the period 1980–2017. The results of the ARDL model were validated using the Fully Modified Ordinary Least Squares (FMOLS) and Canonical Cointegrating Regression (CCR) methods. The study found that fiscal capacity (measured as the ratio of tax revenue to GDP) impacted positively on public health expenditure. The results suggest that following an improvement in resources, the government prioritised the health sector. The study recommends that the government continues to prioritise health in the national budget to enhance the country’s achievement of SDG3.

Low Frequency Cointegrating Regression with Local to Unity Regressors and Unknown Form of Serial Dependence

This article develops new t and F tests in a low-frequency transformed triangular cointegrating regression when one may not be certain that the economic variables are exact unit root processes. We first show that the low-frequency transformed and augmented OLS (TA-OLS) method exhibits an asymptotic bias term in its limiting distribution. As a result, the test for the cointegration vector can have substantially large size distortion, even with minor deviations from the unit root regressors. To correct the asymptotic bias of the TA-OLS statistics for the cointegration vector, we develop modified TA-OLS statistics that adjust the bias and take account of the estimation uncertainty of the long-run endogeneity arising from the bias correction. Based on the modified test statistics, we provide Bonferroni-based tests of the cointegration vector using standard t and F critical values. Monte Carlo results show that our approach has the correct size and reasonable power for a wide range of local-to-unity parameters. Additionally, our method has advantages over the IVX approach when the serial dependence and the long-run endogeneity in the cointegration system are important.

Do hot money movements drive Turkey s housing prices

Purpose- The aim of this study is to investigate the existence and extent of the impact of "hot money" inflows on Turkey's real estate market, which causes the expansion of the monetary base in the markets. The use of data specific to the Turkish market, which has an emerging market economy and where housing production and sales are significant as an important macroeconomic indicator in this market, where the right to acquire housing has been stretched to foreigners in recent years and where excessive short-term capital flows are preferred, reveals the importance of the study. For this purpose, the "Housing Price Index" indicators created by the CBRT to monitor price changes in the housing market were used to measure the situation of housing markets in Turkey in recent years and to measure the possible effects of short-term international capital inflows on real estate prices. Methodology- As a method in the study, firstly, the changes in the monthly data between January 2010 and August 2022 belonging to both hot money and the Housing Price Index compared to the previous period were calculated, and then these data were seasonally adjusted. Then, the Dynamic Ordinary Least Squares (DOLS) method, which has the equation property of Cointegration Regression, was used to learn the existence and extent of the effect of hot money on real estate prices in the long term. Findings- In the analysis, it was found that hot money had a significant effect on housing prices at the level of 5% (p=0.012). The results of the analysis provide evidence that hot money is an important factor in determining housing prices in Turkey. Conclusion- The study found that due to the size of hot money and the fact that it is a short-term investment, it pulls real estate prices upwards and causes volatility. In addition, the results revealed that excessive liquidity caused by short-term international capital inflows drives the change in real estate prices by increasing it and that a long-term relationship between these two variables is evident. Keywords: Hot money, housing price, dynamic ordinary least squares JEL Codes: C32, C58, E44, G10,

Neighborhood Effect, Agricultural Development and Poverty Level in Nigeria

The study seeks to evaluate the role of agricultural development on poverty level and ascertain whether neighbouring countries have any influence on the relation between agricultural development and poverty level in Nigeria. Annual data covering the period between 1980 and 2019 were employed. Fully Modified Ordinary Least Square (FMOLS), Canonical Cointegrating Regression (CCR) and spatial econometrics were employed. Our results showed that agricultural development is capable of reducing poverty level in the country, while there was no significant effect of spatial factors on the relationship between agricultural development and poverty level in Nigeria between 1980 and 2019. The study therefore recommends that the government should pay more attention towards improvement of agriculture, transportation and oil rent which are capable of complementing each other in reducing poverty in Nigeria.

Infrastructure Spending, Political Instability and Economic Growth

This study intends to examine the effects of political unrest and government infrastructure spending on economic growth in the Democratic Republic of the Congo between 1980 and 2020. This is being done to assess the relationship between public infrastructure and economic growth in the DRC as well as the impact that political instability has on that relationship. According to the findings of an econometric regression that employed estimation techniques ranging from the most basic to the most reliable, such as the ordinary least squares (OLS) method, fully modified least squares (FMOLS, Philips Hansen), canonical cointegration regression (CCR, Park), and vector error correction model (VECM), a positive relationship between infrastructure spending and long-term economic growth has been found at the ground level. It also seems that political unrest has an impact on infrastructure spending, which has an impact on economic growth both directly and indirectly.

Extended Two-Way Fixed Effects Quantile Cointegration Regression and its Application

Extended Two-Way Fixed Effects Quantile Cointegration Regression and its Application