Autoregressive Distributed Lag Model Research Articles

Influence of Inflation on the Economic Growth of Saudi Arabia

Primary factors considered that influence inflation movement can experience significant changes due to the COVID-19 pandemic. This research focuses on examining macroeconomic factors which influence the inflation rate in Saudi Arabia which is critical for development and adjusting a suitable policy response. Saudi Arabia’s changing inflation trends are also affected by its internal and external factors including domestic demands, net spending of Government, and liquidity along with exchange rate movements and imported inflation. This research paper focuses on providing insights regarding previous studies conducted on this topic by adopting different approaches. The approach utilized in this study reflects mainly non-oil revenues like value-added tax along with levies related to expatriates on inflation. ARDL (Autoregressive Distribution Lag Model) has been applied instead of the Vector Error Correction model considering its robustness for 40 quarter sample sized integrated in this study. From the results, it has been confirmed that the inflation Movement in Saudi Arabia is more inclined to external factors than domestic factors.

Exploring the Correlation Between Monetary Policy and Food Inflation in Zambia

his relationship isolating food inflation from overall inflation to provide better policy implications that can enhance welfare for the Zambian populace. The Autoregressive Distributed Lag Model and the Error Correction Model were employed for the short run and long run analysis respectively. The findings postulate that there exists a significant relationship between monetary policy and food inflation. The monetary policy rate and the growth in the broad money have a destabilising relationship with food inflation. The depreciation in the domestic currency to foreign currency increase food inflation in the long run. Whereas the exchange rate channel plays a role of greater significance amongst the studied monetary policy tools. Inflation targeting, although significant, adds inflationary pressure of domestic food inflation, highlighting the need for stern policies that can individually factor in food inflation.

Integrating fintech in natural resource management: Assessing the EKC hypothesis for sustainable development in E7 countries

The main objective of the study is to examine the impact of green finance, fintech and mineral resources on environmental sustainability is of great importance in the E7 nations namely Brazil, China, India, Indonesia, Mexico, Russia, and Turkey. The E7 countries are selected as these countries are mineral rich countries and account for a major portion of global mineral export. The data of twenty-five years spanning from 1998 to 2022 is used for the analysis. The study has employed the three measures namely carbon dioxide emissions, ecological footprint, green economic growth to conceptualize the sustainable growth. For the data analysis the study has employed the nonlinear auto-regressive distributed lag (NL-ARDL) model. The results of the study highlights that encouraging and promoting the green finance, and fintech in E7 countries promote sustainable growth, by reducing CO2 emission, improving economic growth, and reducing ecological footprints. The results of the study are in line with the proposition of EKC hypothesis. Moreover, the results of the study also highlight that mineral resource abundance have significant impact of environment sustainability. The study suggests that E7 countries should establish dedicated funds or subsidies to help businesses transition to green technologies and practices, especially for small and medium-sized enterprises. Fintech is seen as a transformative tool in advancing environmental sustainability by improving financial service efficiency and offering new green investment platforms in E7 countries. This could help mitigate initial costs associated with green technology adoption.

Analyzing the dynamics of monetary policy and economic growth in Ethiopia: an autoregressive distributed lag (ARDL) approach

Numerous studies conducted by various scholars have aimed to ascertain the impact of monetary policy on economic growth. However, these empirical investigations have not reached a unanimous consensus. Therefore, this study analyzes the effect of monetary policy. To address this objective, this study employed ARDL and error correction model (ECM) based on annual data from 1993 to 2022. Granger causality test was also employed to check the direction of causal effects of one variable on the other variable. All required pre and post estimation tests were performed and verified that the model was statistically viable. The study revealed that, in the short run, deposit interest rate, reserve required amount, and open market operation have positive and statistically significant effects on real GDP growth of Ethiopia; however, money supply has negative and statistically significant effects on real GDP growth of Ethiopia. In the long run, money supply has positive and statistically significant effects on real GDP growth of Ethiopia; however, deposit interest rate and reserve required have negative and statistically significant effects on real GDP growth of Ethiopia. To sum up, in the short run, contractionary monetary policy was promising, and in the long run, expansionary monetary policy was favorable for economic growth. The Granger causality test result shows that there is bidirectional causality between monetary policy and economic growth; except there was a unidirectional relationship between deposit interest rate and economic growth. Ultimately, this research suggests the following policy recommendation: for short-term economic enhancement, it is advisable for the government to opt for a contractionary monetary policy rather than an expansionary one. On the other hand, for long-term economic improvement, an expansionary monetary policy is more favorable than a contractionary one.

Cement production and CO2 emission cycles in the USA: evidence from MS-ARDL and MS-VARDL causality methods with century-long data.

The cement industry is among the top three polluters among all industries and the examination of the nonlinear and cointegration dynamics between cement production and CO2 emissions has not been explored. Focusing on this research gap, the study employs a novel Markov-switching autoregressive distributed lag (MS-ARDL) model and its generalization to vector error correction, the MS-VARDL model, for regime-dependent causality testing. The new method allows the determination of nonlinear long-run and short-run relations, regime duration, and cement-induced-CO2 emission cycles in the USA for a historically long dataset covering 1900-2021. Empirical findings point to nonlinearity in all series and nonlinear cointegration between cement production and cement-induced CO2 emissions. The phases of regimes coincide closely with NBER's official economic cycles for the USA. The second regime, characterized by expansions, lasts twice as long relative to the first, the contractionary regime, which contains severe economic recessions, as well as economic crises, the 1929 Great Depression, the 1973 Oil Crisis, the 2009 Great Recession, and the COVID-19 Shutdown and Wars, including WWI and II. In both regimes, the adverse effects of cement production on CO2 emissions cannot be rejected with varying degrees both in the long and the short run. Markov regime-switching vector autoregressive distributed lag (MS-VARDL) causality tests confirm unidirectional causality from cement production to CO2 emissions in both regimes. The traditional Granger causality test produces an over-acceptance of causality in a discussed set of cases. Industry-level policy recommendations include investments to help with the shift to green kiln technologies and energy efficiency. National-level policies on renewable energy and carbon capture are also vital considering the energy consumption of cement production.

The implications of the ecological footprint and renewable energy usage on the financial stability of South Asian countries

This study explores the complex relationships involving ecological footprints, energy use, carbon emissions, governance efficiency, economic prosperity, and financial stability in South Asian nations spanning the period from 2000 to 2022. Employing various methodologies such as cross-sectional dependence tests, co-integration analysis, and first- and second-generation unit-root tests, we use a panel Autoregressive Distributed Lag model, feasible generalized least squares, and Panel Corrected Standard Errors to ensure the robustness of our findings. We find noteworthy positive correlations between several variables, including heightened ecological consciousness, effective governance structures, increased GDP per capita, and amplified CO2 emissions. These relationships suggest potential pathways to strengthen the financial stability of the entire region; they also highlight the latent potential of embracing ecologically sustainable practices to fortify economic resilience. Our results also underscore the pivotal role of appropriate governance structures and higher income levels in bolstering financial stability in South Asian countries. Interestingly, we also find negative coefficients associated with the use of renewable energy, suggesting that escalating the adoption of renewable energy could create financial instability. This finding stresses the importance of diversification in energy strategies, cautioning policymakers to carefully consider the financial ramifications of potentially costly imports of renewable energy sources while seeking to reduce carbon emissions, emphasizing the need to strike a balance between ambitious sustainability goals and the pursuit of sustained economic robustness in the region. In considering the implications of these findings, it is crucial to consider each country’s broader socioeconomic context. Our results offer valuable insights for policymakers in developing renewable energy strategies.

Connectedness of cryptocurrency markets to crude oil and gold: an analysis of the effect of COVID-19 pandemic

The notion that investors shift to gold during economic market crises remains unverified for many cryptocurrency markets. This paper investigates the connectedness between the 10 most traded cryptocurrencies and gold as well as crude oil markets pre-COVID-19 and during COVID-19. Through the application of various statistical techniques, including cointegration tests, vector autoregressive models, vector error correction models, autoregressive distributed lag models, and Granger causality analyses, we explore the relationship between these markets and assess the safe-haven properties of gold and crude oil for cryptocurrencies. Our findings reveal that during the COVID-19 pandemic, gold is a strong safe-haven for Bitcoin, Litecoin, and Monero while demonstrating a weaker safe-haven potential for Bitcoin Cash, EOS, Chainlink, and Cardano. In contrast, gold only exhibits a strong safe-haven characteristic before the pandemic for Litecoin and Monero. Additionally, Brent crude oil emerges as a strong safe-haven for Bitcoin during COVID-19, while West Texas Intermediate and Brent crude oils demonstrate weaker safe-haven properties for Ether, Bitcoin Cash, EOS, and Monero. Furthermore, the Granger causality analysis indicates that before the COVID-19 pandemic, the causal relationship predominantly flowed from gold and crude oil toward the cryptocurrency markets; however, during the COVID-19 period, the direction of causality shifted, with cryptocurrencies exerting influence on the gold and crude oil markets. These findings provide subtle implications for policymakers, hedge fund managers, and individual or institutional cryptocurrency investors. Our results highlight the need to adapt risk exposure strategies during financial turmoil, such as the crisis precipitated by the COVID-19 pandemic.

Environmental Management Technologies, Environmental Policy Stringency, Energy Productivity and Pollution Emission: Fresh Evidence from Indonesia under STIRPAT Framework

The rapid pace of climate change and global warming has become an ever growing challenge all over the world. Global warming and carbon concentrations can be mitigated by adopting strict environmental policies and effective management of the environment. Therefore, the study aims to empirically investigate the effect of environmental policy stringency (EPS), energy productivity and environmental management technologies (EMT) on pollution emission in Indonesia- one of the most polluting countries of the world. To this end, the study collects data over the 1990 to 2020 period. The study uses Autoregressive Distributed Lag Model (ARDL) approach to carry out empirical estimation. The findings reveal that environmental management technologies, energy productivity and environmental policy stringency had negative impact on CO2 emission. However, economic growth (GDP) and urbanization had positive role in increasing CO2 emission in the long run and the short run. On the basis of the outcomes, the study recommends that Indonesia should adopt strict environmental policies and legislations to mitigate environmental deterioration. Indonesian government is recommended to promote the adoption of environmental management technologies by providing substantial incentives and investment opportunities for businesses.

Symmetric and Asymmetric Association Between Foreign Direct Investments and Macroeconomic Variables: An Ardl Approach

Objectives: The main objective of the study is to investigating the dynamic relationship between FDI and different Macro Economic Variables (MEVs) using the ARDL procedure, providing a more comprehensive understanding of the association between FDI and MEVs and to evaluate their relative importance for FDI. Methods: This study utilizes annual data from 1991 to 2021 from the World Bank (2021) and the Reserve Bank of India (2021). Data on GDP, Export, Inflation and interest rate, and FDI are collected from the World Bank. Autoregressive Distributive lag Model procedure has been used for the study in order to establish relationship between Macro economic variables and FDI. Results: Our methodological approach using ARDL model and finds: (a) a positive correlation between exports and FDI, (b) a negative impact of inflation and exchange rates on FDI in the long run. Non-linear ARDL analysis reveals the asymmetric impact of inflation and interest rate on FDI, which includes the effect of positive and negative shock of interest rate and inflation on FDI. d) 1% increase in inflation reduces FDI by 0.4% and if Inflation is reduced by 1%, FDI is increased by 0.2%. At the same time, the non-linear estimation of interest rates concludes that there is an asymmetric and significant association between interest rates and FDI. e) If Interest rate has increased by 1% FDI is decreased by 0.9% and if interest rate has reduced by 1% FDI has deceased by 1.63%. f) The causality analysis reveals that exports, GDP, and exchange rates are the significant economic variables that affect FDI. Conclusion: The study's findings have practical implications for policymakers and investors looking to attract more FDI in India. The results indicate that exports play a critical role in attracting FDI and that the government should focus on improving export performance to increase FDI inflows. Additionally, the study highlights the importance of controlling inflation and exchange rates to attract more foreign investment. The finding that interest rates have an asymmetric relationship with FDI suggests that policymakers should be cautious when implementing monetary policies that may impact interest rates. Overall, the study provides valuable insights for policymakers and investors looking to attract more FDI in India and highlights the importance of considering the country's macroeconomic conditions when making investment decisions.

Unveiling asymmetry impacts of economic policy uncertainty on climate change: fresh insights into African Countries.

This study investigates the influence of economic policy uncertainty on climate change in selected African countries within asymmetric settings. Although previous research has examined the impact of various economic factors on climate change, the asymmetric effects of economic policy uncertainty have not been thoroughly explored, particularly in African countries. We analyze annual data spanning from 1980 to 2017 by utilizing three models: Panel Pooled Mean Group-Autoregressive distributed lag model (ARDL-PMG), Panel Pooled Mean Group-non-linear autoregressive distributed lag model (NARDL-PMG), and Dumitrescu-Hurlin asymmetric causality tests. According to the results of ARDL-PMG estimation, economic policy uncertainty has a detrimental impact on climate change in the long run. However, the NARDL-PMG estimation suggests that a positive shock in economic policy uncertainty negatively affects long-term climate change mitigation. However, a negative shock has a beneficial effect on climate change in the long term. In African nations, positive and negative changes in economic policy uncertainty failed to generate any significant climate change effects in the short run. The results also reveal that both positive and negative shocks in economic policy may cause climate change in a one-way direction. Based on the findings of our study, we recommend that African policymakers implement programs aimed at reducing economic policy uncertainties to help mitigate the effects of climate change.

STOCK MARKET DEVELOPMENT AND ECONOMIC GROWTH: AN ARDL APPROACH

This research project employs the Autoregressive Distributed Lag (ARDL) approach to investigate the intricate relationship between stock market development and economic growth. The primary objective is to systematically analyze the causal links and quantify the impact of key stock market indicators on economic growth, and vice versa. The methodology encompasses a thorough literature review to establish a theoretical framework, followed by the collection and examination of relevant data over a defined timeframe. Unit root tests and cointegration analysis are applied to assess the stationarity and long-term relationships between the variables. The ARDL model is then employed to estimate the dynamic interactions, considering lagged values of the indicators. Diagnostic tests are conducted to ensure the robustness of the findings. The project aims to contribute empirical insights into both short- and long-term dynamics, shedding light on the complex interplay between stock market development and economic growth. The results are expected to offer valuable implications for policymakers and researchers, providing evidence-based guidance for fostering sustainable economic development. Ultimately, this research seeks to advance our understanding of the mechanisms driving economic growth in the context of stock market development. Keywords: Stock Market Development, Economic Growth, ARDL Approach, Causal Relationship, Lagged Variables, Unit Root Tests, Cointegration Analysis, Empirical Insights, Sustainable Economic Development, Policy Implications.

Impact of human capital and natural resources on environmental quality in South Asia

AbstractThis study examines the importance of human capital and natural resources on environmental quality in South Asia by using panel data that ranges from 1975 to 2021. Advanced cointegration approaches establish the long-term linkages between human capital, natural resources, economic growth, economic growth squared, urban population, industrial value added, and ecological footprint. To explore the long and short-run association among these variables, this study applied the panel autoregressive distributed lags (ARDL) model. The outcome designates that human capital has a negative and significant alliance with environmental quality, whereas economic growth and economic growth square affect environmental quality positively and negatively, confirming the existence of the environmental Kuznets curve (EKC) hypothesis. Urban population growth and natural resources have significant negative associations with environmental pollution, whereas industrial value added increases the pollution levels in South Asia. Lastly, the outcome of the interaction between human capital and natural resource rent on the ecological footprint has a significant positive effect. This study recommends that South Asian nations improve their human capital to promote industrial modernization, expansion of green technology, and energy-efficient habits. Utilizing natural resources also helps South Asian nations maintain a clean ecosystem. Additionally, South Asian nations ought to vigilantly track the negative consequences of industrialization on the atmosphere. Thus, ecological sustainability requires human capital and natural resources.

Transition towards natural resource rents and green technology to achieve China's COP26 success: A novel insights in the case of trade openness and environmental pollution

This study aimed to investigate the impact of various factors such as natural resource rents, trade openness, green technology innovation, oil prices, and economic growth on environmental pollution in China by covering the period from 1970 to 2021, aligning with the COP26 agenda. These factors included. To accomplish this, we utilized an advanced Quantile Autoregressive Distributed Lag model to scrutinize the short- and long-term quantile patterns for both the dependent and independent variables. The findings reveal that green technologies and natural resource rents play a negative role in reducing emissions, whereas economic growth and trade openness have positive effects on emissions levels in China in the short and long run during different quantile patterns. Additionally, we employed the Wald test to confirm the presence of asymmetric patterns in environmental pollution concerning economic growth, natural resource rents, green technological innovation, and trade openness in both the short and long terms. These results emphasize the significance of prioritizing key policy measures to align with the COP26 agenda, including enhancing green technology via natural resource rents and addressing immediate climate change challenges in China.

Achieving environmental sustainability goals through capitalizing on renewable energy channels: Role of green finance, resources productivity and geopolitical situation in the MENA region

Environmental sustainability is essential to the country's financial position and economic growth. Geopolitical situations and green activities significantly contribute to enhancing sustainable development goals. The purpose of this study is to evaluate the impact of green finance, investment in renewable energy sources and geopolitical risk on the environmental sustainability of the Middle East and North America (MENA) region over the period 2000–2021. When using a cross‐sectional dependence autoregressive distributed lag model (CS‐ARDL), it is discovered that a rise in geopolitical risk will decrease environmental performance in the form of carbon dioxide emission. At the same time, the other factors (green finance and investment in renewable energy) have a positive relationship with sustainability. This observation can be ascribed to the association between increased geopolitical risk and the inclination of crude oil consumers, who are most affected by such risk, to contemplate renewable energy as an alternative to conventional energy sources. This finding could also be attributed to the fact that clean energy is becoming more affordable. In addition, the augmented mean group model findings provide more evidence that a negative linkage is found between geopolitical risk and environmental sustainability. The findings have repercussions for regulators as well as investors who are active in the renewable energy markets.

The Impact Of Ecological Sustainability On Poverty Alleviation In Nigeria: ARDLM Co-Integration From 1981 To 2021: Policies Perspectives

The aim of the research is to examine the impact of ecological sustainability on poverty alleviation in Nigeria. Despite finding the limit to growth theoretical clarity, it is more justifiable to verify the connection that ecological sustainability does not relate to policies perspectives for improving the well-being of poor people. Employing Autoregressive Distributed Lag Model (ARDLM) to analyze the impact of social and economic sustainability on poverty reduction based on ecological policies perspectives from 1981-2021. Use the limit-to-growth theory to examine the hypothesis in poverty alleviation as well as the ecological policies perspectives such as social or urban growth, economic or empowerment sustainability. The results reveal that ecological policies perspectives correlated with social and economic sustainability in alleviating poverty. In addition, social sustainability is positive and significant, while economic sustainability is negative and insignificant in the short- and long-run on poverty alleviation. Besides, the impact of ecological policies on social sustainability on poverty alleviation is found to be positive and economic insignificant, which does not confirm the hypothesis on growth limit that creates an impact on ecological policies perspectives.

Impact of Information and Communication Technologies on Energy Consumption and Economic Growth: An Empirical Assessment for China

Regarding the association between information and communication technology (ICT) and economic growth and energy consumption, particularly in developing economies, there is a great deal of controversy and a lack of empirical study. This study applies Quantile Autoregressive Distributed Lag Model (QARDL) approach to empirically probe into the effect of ICT on energy consumption and economic growth in China. Two corresponding models for energy consumption and economic growth are formulated and the data of all of the variables spanning over 1995-2021 period is taken from different secondary sources. The findings of the study reveal significant and positive impact of ICT on economic growth at all quantiles in the short and long run. Similarly ICT has significant and positive impact on energy consumption also at all quantiles. The Quantile Granger Causality test provides evidence for bidirectional causality between ICT and economic growth and ICT and energy consumption. Wald test proved the presence of and symmetric and linear association between ICT and economic growth and ICT and energy consumption at quantiles in the long and short run both. In the light of these findings, the study recommends the Chinese government and investors to have more investment in energy efficient ICT products that consume less energy and help increase in economic growth in China.

Effects of ICT and Energy Transition on Sustainable Fiscal Growth: Empirical Evidence from China

It is crucial for sustainable growth policies and strategies, which are currently being used internationally, to balance the various aspects of development, including the economic, environmental, and social ones. There is currently broad agreement that energy transition and Information and communication technologies (ICT) offer fresh momentum for long-term sustainable fiscal growth. The main goal of the current research is to study the effects of energy transition and ICT on sustainable fiscal growth in China over 1995-2020 period. To empirically estimate the above mentioned nexus, the recently introduced Quantile Autoregressive Distributed Lag Model (QARDL) is applied. The study findings for long run indicate that energy transition impacts sustainable fiscal growth negatively over lower qauntiles and at extreme higher quantiles. However, the ICT is found to promote sustainable fiscal growth at all quantiles. The short run dynamics also indicate that current and previous values of ICT and energy transition have significant impact on sustainable fiscal growth. The Wald test of parameter constancy rejects the null hypothesis and shows that non linear and asymmetric impact is present between sustainable fiscal growth and ICT and energy transition in the short run and the long run. Test of Quantile Granger Causality reveals bidirectional causal association between study variables. The study recommends the Chinese government and policy makers to adopt efficient policies to promote ICT use among all the sectors of the economy that can help in achieving sustainable fiscal growth.

Towards Achieving Sustainable Development in China: What Role Does Digital Technologies and Green Technologies Play?

There is always discussion about how to use digitalization to promote sustainable development. While one side vehemently defends using ICT to direct and carry out sustainable development, another strongly struggles to highlight its many drawbacks. Similarly, green technologies are also under the limelight for promoting sustainable development since these technologies combine economic growth and environmental conservation. The purpose of the study is to empirically estimate the effect of digital technologies and green technologies in sustainable development in China. Time series data spanning over 1995-2021 period is analyzed using recently introduced Quantile Autoregressive Distributed Lag Model (QARDL). The findings of the study suggest that there exists positive association between digital technologies and sustainable development over (0.05-0.80) quantiles. The significant and positive association exists between green technologies and sustainable development at all quantiles (0.05-0.95). The study also finds positive and negative association of economic growth and urbanization respectively with sustainable development. The study results emphasize the limitations of conclusion of past studies and provide significant contributions by confirming that the associations among variables is quantile-dependent. The Wald test results also disprove the parameter constancy for the Chinese economy and the findings of Quantile Granger Causality prove the presence of bidirectional causal association between digital technologies, green technologies and sustainable development. The study recommends Chinese policy makers and government to adopt relevant policies to promote sustainable development by effectively utilizing digital and green technologies.

Carbon Dioxide Emissions and Its Socio-Economic Drivers Nexus: Empirical Evidence from MENA Countries

Carbon dioxide emissions is one of the main climate change causes. Nowadays is considered as one of the most important environmental degradation and air pollution in the world. The relationship between CO<sub>2</sub> emissions and their socio-economic determinants is investigated in this study. It uses empirical analysis of significant impact of the economic growth, the tourism development, the globalization, the population health, the population growth and the energy consumption on our interest variable. Thus, we're going to select an econometric model, an Autoregressive Distributed Lag model (ARDL) applied to a sample of 10 MENA countries. Our aim is to measure, long-run and short-run impact of the number of arrivals, the Foreign Direct Investment, government health expenditures, the energy consumption and the population growth rate on the CO<sub>2</sub> emissions. All variables are observed from 1995 to 2020. The methodology adopted in this work is as followed: First, the statistical indicators for the sample are calculated. Secondly, the temporal series stationarity is checked by realizing tests of first and second generation. The Pedroni ARDL and Pesaran cointegration tests are then applied. Finally, Granger causality for panel data is used. The causality analysis between variables is carried out using the Dumitrescu and Hurlin's test. Findings reveal that, in the long term, tourism sector and energy consumption have significant and positive impact on CO<sub>2</sub>. But the economic growth and population growth influenced it negatively and they are statistically significant. In the short term, the dependent variable is statistically impacted by economic growth. The degrees and magnitude of the individual effects of the various variables on the Carbone dioxide emissions vary from country to an other. There are bidirectional and unidirectional relationships causality between different variables. Our findings can be helpful for policy makers seeking to achieve the sustainable development goals in MENA region.

Asymmetric impact of climatic parameters on hemorrhagic fever with renal syndrome in Shandong using a nonlinear autoregressive distributed lag model

Hemorrhagic fever with renal syndrome (HFRS) poses a major threat in Shandong. This study aimed to investigate the long- and short-term asymmetric effects of meteorological factors on HFRS and establish an early forecasting system using autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL) models. Between 2004 and 2019, HFRS exhibited a declining trend (average annual percentage change = − 9.568%, 95% CI − 16.165 to − 2.451%) with a bimodal seasonality. A long-term asymmetric influence of aggregate precipitation (AP) (Wald long-run asymmetry [WLR] = − 2.697, P = 0.008) and aggregate sunshine hours (ASH) (WLR = 2.561, P = 0.011) on HFRS was observed. Additionally, a short-term asymmetric impact of AP (Wald short-run symmetry [WSR] = − 2.419, P = 0.017), ASH (WSR = 2.075, P = 0.04), mean wind velocity (MWV) (WSR = − 4.594, P < 0.001), and mean relative humidity (MRH) (WSR = − 2.515, P = 0.013) on HFRS was identified. Also, HFRS demonstrated notable variations in response to positive and negative changes in ∆MRH(−), ∆AP(+), ∆MWV(+), and ∆ASH(−) at 0–2 month delays over the short term. In terms of forecasting, the NARDL model demonstrated lower error rates compared to ARDL. Meteorological parameters have substantial long- and short-term asymmetric and/or symmetric impacts on HFRS. Merging NARDL model with meteorological factors can enhance early warning systems and support proactive measures to mitigate the disease's impact.