Autoregressive Distributed Lag Model Research Articles

The legal foundations and specifics of exercising corporate rights by individuals authorized to perform local self-government

Financial liberalization is a widely adopted policy for countries to enhance growth. Financial liberalization enables financial institutions to proceed with innovative activities by familiarizing themselves with fewer mortgage loans in the financial market. Financial liberalization is a widely adopted policy for countries to enhance growth; however, it is a complex and controversial issue, with different studies providing contrasting perspectives. Financial liberalization is considered an efficient way to measure and change the financial market and structure in order to reach a position to get financial liberalized markets under an efficient and effective guideline. In the case of Vietnam, very few scholars have investigated the issue of financial liberalization-led growth. This endeavor examines the effects of financial liberalization on economic growth in the presence of control variables for Vietnam over the period of 1990 to 2021. This study uses the Autoregressive Distributive Lag Model approach due to its several advantages. The results show that financial liberalization enhances economic growth, which implies that more liberalization results in enhanced growth in Vietnam. Improved access to credit is another positive outcome of financial liberalization, as it can result in greater access to credit for individuals and businesses, enabling them to make investments and expand their operations. Hence, the results support the neo-liberal view of liberalization.

The Impact of Climate Change on Zero Hunger in South Asia

Climate change has raised ample concerns about achieving zero hunger. The goal of the study is to investigate the link between climate change and eradicating hunger in Asian nations. The data span is from 2001 to 2020 from World Bank and KOF. The effect was investigated using the Pool Mean Grouped/Panel Autoregressive Distributed Lag Model which shows that climate change, both in the short-run and long-run, hinders progress toward achieving zero hunger. The conclusion shows that in the long run, a rise in per capita income, globalization, and labor force participation boosts the progress towards achieving zero hunger but, in the short run, per capita income has a significant effect, and globalization and labor force participation have an insignificant effect on zero hunger. Finally, this study offers some recommendations to achieve zero hunger.

The influence of climate change on crop production: Evidence from Somalia

The study examines the effect of climate change on crop production in Somalia for the period 1990 to 2022 obtained from the World Bank and FAOSTAT. Initial assessments were conducted on the data using various techniques, including the Correlation Matrix, Augmented Dickey-Fuller test, and Phillips-Perron unit root test. The findings from both the Bounds test and Johansen test demonstrate the existence of cointegration within the model. To estimate the parameter values of the regression model, three methods were employed: the Autoregressive Distributed Lagged (ARDL) model, Dynamic Ordinary Least Squares (DOLS), and Fully Modified Least Squares (FMOLS). The study's results demonstrate a positive and significant influence of foreign direct investment (FDI) on crop production. Specifically, the impact of FDI is found to be more pronounced in the long term compared to the short term. Similarly, the study finds that the harvested area and labor force have substantial positive impacts on crop production, both in the short run and long run. Based on the ARDL model results, the study finds that rainfall and temperature do not have a beneficial influence on crop production in both the short run and long run. This is becuase, Somalia is considered as one of the most susceptible countries to the effects of climate change globally. The paper advises Somalia's government to focus on developing heat-resistant crops to counter the opposing effects of temperature on crop production and ensure food security. It also requires a comprehensive agriculture funding-framework including emergency assistance.

Vietnam's exports to Korea and the real exchange rate: Post-crisis evidence from the multiple threshold nonlinear autoregressive distributed lag model

In this study, the asymmetric effects of the real exchange rate on Vietnam's exports to Korea were investigated using the multiple threshold nonlinear autoregressive distributed lag (MTNARDL) model. As a result, the MTNARDL model exhibited enhanced estimation accuracy compared to the single-threshold nonlinear autoregressive distributed lag (NARDL) model. Furthermore, the aggregate bias problem was solved using individual export data for 16 major industries instead of relying on the overall export value data. The results revealed the substantial asymmetric impact of real exchange rate fluctuations in the short and long run. Fluctuations in the real exchange rate boosted Vietnam's exports to the Republic of Korea in the long run, with minimal negative influence. This indicates that the depreciation of the Vietnamese dong against the United States (US) dollar encouraged exports in some industries but limited exports in others. However, the short-term effect of the real exchange rate remains uncertain, as it could be either negative or positive depending on the industry and threshold level. The diverse impacts of the real exchange rate fluctuations on exports across industries were attributed to factors such as the capacity to absorb foreign exchange risks, product heterogeneity, and the ease of adjusting inputs and production for each sector. Therefore, these findings may help policymakers develop Vietnam-specific policies to address long- and short-term changes in the threshold levels of real exchange rate fluctuations.

Financial inclusion in Morocco: the impact of financial development, financial innovation and economic growth

The relationship between financial inclusion, social development, particularly poverty reduction, financial innovation and economic growth has been endorsed by a number of studies. Indeed, the development policies pursued by countries such as Morocco often define financial inclusion, growth, financial development, financial innovation, monetary policy and investment as fields of action for economic and social development. The aim of this study is to assess this choice by examining the short- and long-term relationship between financial inclusion, financial development, financial innovation, growth, inflation and the interest rate. Using the ARDL (AutoRegressive Distributed Lag) model and the Granger causality test, the results show the positive short- and long-term effect of financial development and financial innovation on financial inclusion, and the positive short- and negative long-term effect of economic growth on financial inclusion. The inflation and the interest rate that represents monetary policy influence financial inclusion in the short and long term positively and negatively respectively. In terms of causality, the granger test shows that economic growth has an effect on inclusion in only one direction and that financial inclusion has an effect on financial development in only one direction. On the other hand, innovation, inflation and the interest rate have a bidirectional relationship with financial inclusion.

The Impact of Climate Change on Rice Production in Punjab: An Auto Regression Distributed Lag Model

The Impact of Climate Change on Rice Production in Punjab: An Auto Regression Distributed Lag Model

Interplay of Foreign Direct Investment, Remittances, and Economic Growth: Insights from SAARC Countries

The current study focuses on finding and analyzing the nexus between remittances, foreign direct investment, and economic growth in SAARC countries. This study uses secondary balanced data of selected SAARC countries for the years 2000 to 2019. This study uses gross domestic product i.e., GDP, which serves as the proxy of economic growth, as the dependent variable. The core independent variables of this study include (FDI) foreign direct investment and remittances (RMTN). Additionally, gross capital formation (GCF) and carbon dioxide emissions (CO2) are the other two independent variables in this study. For the proxy for environmental degradation the study uses data of CO2 emissions. The data is collected from the world development indicators, WDI. The study focuses on how all the independent variables affect the growth of economy in selected SAARC countries which include Pakistan, India, Bangladesh, Sri Lanka, and Nepal. The study applies cross sectional dependence test, panel cointegration test, unit root tests, and PARDL (panel auto regressive distributed lag model), also called pooled mean group methodology, to obtain empirical estimates. The results of PARDL show that FDI, RMTN, and GCF are all positively and significantly related to GDP. This means that GDP or alternatively economic growth, is improving with rise in FDI, RMTN, and GCF in selected SAARC countries. The results show that CO2 emissions have a negative significant impact on GDP i.e., increase in pollution levels is harmful for the growth of the selected economies.

Can environmental taxes and green technological investment ease environmental pollution in China?

This study examines the complex relationship between environmental taxation, green technology, green investment, real GDP, and environmental pollution by employing the Nonlinear autoregressive distributive lag model for China. The findings indicate that environmental tax policies have significant short- and long-term effects, with positive shocks leading to pollution reductions. The positive and negative shocks to green investment increase pollution levels in the long run. Moreover, the real GDP displays pollution reductions in the short run. The results provide valuable insights into China's environmental and economic situation and offer policy recommendations. These findings were confirmed using the Wald test. The findings emphasize China's need for stronger environmental taxes, green technology adoption, evaluated green investments, and sustainable economic growth to achieve cleaner production and environmental sustainability.

COVID-19 and Uncertainty Effects on Tunisian Stock Market Volatility: Insights from GJR-GARCH, Wavelet Coherence, and ARDL

This study rigorously investigates the impact of COVID-19 on Tunisian stock market volatility. The investigation spans from January 2020 to December 2022, employing a GJR-GARCH model, bias-corrected wavelet analysis, and an ARDL approach. Specific variables related to health measures and government interventions are incorporated. The findings highlight that confirmed and death cases contribute significantly to the escalation in TUNINDEX volatility when using both the conditional variance and the realized volatility. Interestingly, aggregate indices related to government interventions exhibit substantial impacts on the realized volatility, indicating a relative resilience of the Tunisian stock market amidst the challenges posed by COVID-19. However, the application of the bias-corrected wavelet analysis yields more subtle outcomes in terms of the correlations of both measures of volatility to the same metrics. Our econometric implications bear on the application of such a technique, as well as on the use of the realized volatility as an accurate measure of the “true” value of volatility. Nevertheless, the measures and actions undertaken by the authorities do not exclude fear and insecurity from investors due to another virus or any other crisis. The positive and long-term impact on the volatility of US equity market uncertainty, VIX, economic policy uncertainty (EPU), and the infectious disease EMV tracker (IDEMV) is obvious through the autoregressive distributed lag model (ARDL). A potential vulnerability of the Tunisian stock market to future shocks is not excluded. Government and stock market authorities should grapple with economic and financial fallout and always instill investor confidence. Importantly, our results put mechanisms such as overreaction to public news and (in)efficient use of information under test. Questioning the accuracy of announcements is then recommended.

Exploring the impact of oil revenue on Nigeria's economic growth: A non‐linear autoregressive distributed lag model

AbstractThe influence of oil proceeds on an economy remains a subject of debate among scholars. Most scholars agree that income from oil has direct effect on economic expansion. We explore the impact of revenue from oil on economic growth in Nigeria from 1986 to 2016. Data were collected from the Nigerian Central Bank and the World Data Indicators. We employed a non‐linear autoregressive distributive lag approach to analyse the data. The outcome exposes a direct and significant association between revenue from oil and GDP both in the short and long term. Trade openness exhibits a significant negative effect on GDP from both short‐ and long‐term perspectives. Gross capital formation directly influences GDP in the short and long run, with significant impacts primarily in the long term. The study suggests that revenue from oil has a direct and significant impact on the Nigerian economy during the period from 1981 to 2016. We recommend that the Nigerian government create a conducive environment for private investment to thrive, thereby sustaining the nation's growth potential. Additionally, the government should wisely utilise revenue from oil to revitalise non‐booming productive sectors in the economy, thus diversifying revenue sources.

An Analysis of the Impact of Seaport Efficiency on Nigeria’s Economic Growth.

This research is driven by the objective to assess the impact of the seaport efficiency on Nigeria’s economic growth. The western ports of Lagos are selected as the Decision-Making Units (DMU) for this study covering the period of 2000 to 2020. Two stage estimation process is employed. Data Envelopment Analysis (DEA) was deployed in the first stage to generate the efficiency index required to test the relationships. In the second stage, the efficiency index so generated is introduced as one of the independent variables in a multiple regression model, using Autoregressive Distributed Lag Model (ARDL). The finding shows that seaport efficiency has a negative but significant relationship with economic growth in both the long run and short run. The study concludes that seaport efficiency has an inverse relationship with Nigeria’s economic growth. The result of this study suggests that reforms may not have deepened the level of efficiency in the Nigerian seaport to drive positive economic growth, hence the recommendation for further investment in the terminal operational equipment and technological infrastructure to drive further efficiency in the sector.

Investigating Factors Affecting Renewable and Nonrenewable Electricity Generation in Iran

This study identifies key elements that facilitate the transition of power generation from non-renewable to renewable sources. Annual data from 1977 to 2018 and an autoregressive distributed lag model are used in the study. The gross domestic product, industrialization, urbanization, fuel, and power prices, as well as the dollar exchange rate, are employed as independent variables. The study takes the Iran-Iraq War and nuclear sanctions into account. The findings show that, in the short run, all variables—aside from the exchange rate—have an impact on the generation of electricity from renewable sources. Only natural gas prices, industrialization, and gross domestic product are effective in the short run, for power generation from non-renewable energies. In the long run, all variables, with the exception of electricity price, are effective in producing power from renewable sources, and all variables, with the exception of fuel prices and industrialization, are effective in producing electricity from non-renewable sources. The findings also indicate that nuclear sanctions have a detrimental impact on the production of electricity from renewable sources and a beneficial impact on the production of electricity from non-renewable sources.

The Relationship between Geopolitical Events and the Crude Oil Prices: An Application of ARDL Model

This paper aims to explore the short-run and long-run relationship between geopolitical events and crude oil prices for the period 2000-2023. In addition to geopolitical events, we included the market factors whose data were available in the right part of the equation. To investigate long-run cointegration, this paper used quarterly data and employed the Autoregressive distributed lagged (ARDL) bounds testing approach developed by (Pesaran et al., 2001). Study findings from the ARDL bound testing approach confirm the existence of a long-run and short-run association between geopolitical events and crude oil prices. Furthermore, the findings from the ARDL model revealed that, among others, world crude oil production; OECD’s crude oil stocks, and OECD economic growth have a significant effect on the dependent variable (crude oil prices) both in the long run and short run.

Globalization, Migration, and Economic Growth in Nigeria

The study aimed to empirically examine the effect of globalization and migration on Nigeria's economic growth. The study covered the period 33 years (1990 – 2022). The variables used in the study are foreign direct investment, trade openness, foreign exchange rate, remittances, and gross domestic product. The ARDL (autoregressive distributed lag) model was employed for the study and a long-run relationship was established. Findings revealed that remittance had a significantly positive effect on Nigeria's economic growth in the long run. The finding of the error correction mechanism revealed a speed of adjustment to equilibrium of 31.1%. The study suggested that the government, as a key player, should take immediate action to formulate policies that bolster and optimize the advantages of remittance inflows. This is crucial as remittances have been found to positively and significantly affect economic growth in the short and long run. These measures may encompass reducing transaction expenses, improving financial literacy among recipients, and encouraging investments to amplify the developmental effects of remittances.

Synergistic impacts of technological advancement and environmental hazards on social change and human well-being in South Asia

Sustainable Development Goals (SDGs) and the challenges in attaining these goals are always an eye-catching research theme for academics, politicians, and stakeholders around the world. Subjective well-being (SWB) is one of the central SDGs but is under-researched so far. Addressing this concern is crucial to maintaining societal welfare and promoting the realization of a sustainable future. Hence, this study explores the determinants of SWB with a particular focus on technological advancement and environmental damage. The paper examines SWB determinants in six South Asian nations from 2005 to 2022 using the cross-sectional augmented auto-regressive distributed lag model. The conclusions demonstrated that technology advancement improved SWB in the long term. Meanwhile, environmental deterioration has no appreciable short-term effects and harms SWB in the long term. This study adds to the field by examining how technological advancement and environmental deterioration explain SWB in South Asian economies. The study recommends policies that aim at improving innovations and reducing CO2 emissions.

Exploring the LCC Hypothesis in the Nordic Region: The Role of AI Innovation, Environmental Taxes, and Financial Accessibility via Panel ARDL

This study investigates the impact of artificial intelligence (AI) innovation on environmental sustainability in the Nordic region. Additionally, it tests the Load Capacity Curve (LCC) hypothesis by incorporating factors such as financial accessibility, environmental tax, and urbanization, using data spanning from 1990 to 2020. The methodology includes the Cross-Sectional Dependence test and the slope homogeneity test, revealing issues of heterogeneity and cross-sectional dependence. Furthermore, first and second-generation panel unit root assessments indicate that the variables are free from unit root problems. Panel Cointegration tests confirm that the variables are cointegrated in the long run. To analyze both short-run and long-run relationships, the study employs the Panel Autoregressive Distributed Lag (ARDL) model. The results from the Panel ARDL model support the LCC hypothesis in the Nordic region, showing a U-shaped relationship between income and load capacity factor. Moreover, AI innovation and environmental tax significantly and positively contribute to environmental sustainability in both the short and long run. In contrast, higher financial accessibility and urbanization degrade environmental sustainability over these timeframes. To validate the robustness of the Panel ARDL estimations, the study also uses Fully Modified OLS, Dynamic OLS, and Fixed Effects OLS approaches, all of which corroborate the ARDL findings. The study employs the D-H causality test to explore causal relationships among the variables. The test results reveal a unidirectional causal relationship between income and AI innovation to the load capacity factor and a bidirectional causal relationship between financial accessibility and the load capacity factor, as well as between urbanization and the load capacity factor. However, no causal relationship is found between environmental tax and the load capacity factor.

How does geopolitical risk affect sustainable nuclear energy development?

Nuclear energy can play a vital role in the global transition toward sustainable energy sources because nuclear energy is widely recognized as a carbon-free energy source and helps mitigate the environmental impacts of economic activities. Thus, it is essential to shed light on the factors that can promote global nuclear energy production. This analysis aims to investigate the impact of geopolitical risk on nuclear energy production in the top nuclear energy-consuming nations from 1996-2022. The study employs a cross-sectional autoregressive distributed lag (CS-ARDL) model for empirical analysis and disaggregates the data into groups of developed and developed economies. The analysis confirms that the geopolitical risk boosts long-run nuclear energy production in developed and developing economies. Additionally, the GDP, financial development, research and development spending, and environmental technologies cause nuclear energy production to rise in the long run in developed and developing nations. However, in the short run, only GDP and environmental technologies boost nuclear energy production in both groups of economies, while geopolitical risk, financial development, and research and development technologies do not exert a significant impact on nuclear energy production. These findings imply that policymakers should take advantage of the risky geopolitical situations and increase reliance on domestic energy sources by increasing nuclear energy production.

Algeria's pathway to COP28 and SDGs: Asymmetric impact of environmental technology, energy productivity, and material resource efficiency on environmental sustainability

This study examines the asymmetric effects of material resource efficiency (MRE), energy productivity (EP), and environmental technology (ENT) on environmental sustainability (ES) in Algeria from 1990/Q1 to 2022/Q4. Using a nonlinear autoregressive distributed lag model (NARDL), we find that both positive and negative shocks to both MRE and ENT contribute to ES, highlighting their effectiveness. Interestingly, positive EP shocks have a stronger mitigating effect compared to negative shocks. This suggests a magnified benefit during periods of efficiency gains. Additionally, asymmetric causality analysis verifies a causal link between MRE, EP, ENT, and ES. Given these results, it is recommended that Algerian policymakers should prioritize investments in MRE and ENT while emphasizing policies that enhance EP due to its magnified benefit on ES. Asymmetric policy responses for green practices alongside alignment with SDGs 7, 8, 12 and COP-28 objectives will optimize Algeria's ES.

Public debt dynamics in Brazil through the lens of its conditioning factors

ABSTRACT This article aims to investigate the short and long-run behaviour of the Brazilian public debt, through the lens of the conditioning factors, from December/2006 to September/2022. The econometric approach consists of estimating Nonlinear Autoregressive Distributed Lag Models (NARDL) – Bounds Testing Approach to Cointegration. The main empirical results show: i) a long-term relationship (cointegration) between net and gross debt and their respective conditioning factors; ii) higher speed of adjustment to long-run equilibrium for the gross debt (20%), due to exchange rate adjustments and its asset-only approach; iii) slow net debt adjustment due to its asset-liability consolidation; iv) accumulation foreign reserves closely related to the response given by the gross debt, when affected by exchange rate shocks; iv) the search for a balance in public accounts seems to be a good strategy, as shown by the relationship between primary results/net issuance and net/gross debt; v) an economic activity expansion helps control public debt in Brazil, with a greater sensitivity of the gross debt, compared to the net debt; vi) the role played by the Brazilian Central Bank, together with the accumulation of foreign reserves, is crucial to understanding the recent differences in the trajectories of net and gross debt in Brazil.

Energy efficiency and greenhouse gas emissions: A panel ARDL model of top five emitters in Africa

The Paris Agreement has mandated drastic decarbonization by developed and developing economies. Many of the top African emitters are now exploring drastic restructuring of the production processes in a way that promotes energy utilization efficiency. This is critical for ensuring an environmentally benign growth process. This study presents a unique addition to the body of existing knowledge by analysing the effect of some indicators of energy efficiency on three indicators of environmental quality among the top five emitters of GHG in Africa. The data were from the World Development Indicators (WDI), covering the 1990–2020 periods for South Africa, Egypt, Nigeria, Algeria, and Libya. The panel Autoregressive Distributed Lag (ARDL) model was used for data analysis. The results showed that all the included series were stationary at first difference. The optimum lag lengths were determined independently in STATA 17 software, and the most frequent lags across the countries were selected. The Hausman test revealed that the pooled mean group (PMG) estimation was preferred to other estimators. The results showed that in the long-run, particulate emission damages, CO2 intensity, and energy use intensity positively impacted GHG emissions, while GDP per capita and renewable energy utilization produced mixed results. In the short run, the error correction mechanism parameters were all significant (p < 0.01) in Libya for all emission indicators, while Algeria showed significance (p < 0.01) in total GHG and CO2 per capita models. The coefficient of particulate emission damages significantly reduced (p < 0.01) total GHG emissions in Libya and Nigeria and renewable energy significantly increased (p < 0.01) GHG emissions in South Africa but reduced them in Nigeria. Also, CO2 emission intensity significantly (p < 0.01) and positively impacted GHG emissions in South Africa, Egypt, Libya, and Nigeria, while electricity power transmission losses increased total emissions and CO2 emissions in Libya and Algeria. It was concluded that initiatives to reduce GHG emissions should focus on developing countries’ sensitivity to emission damages, and promotion of initiatives for using renewable energies. Also, promotion of energy efficiency across industries offers a prospect for ensuring environmental sustainability.