Granger Causality Research Articles

Analyzing the Dynamics of Inflation, Exchange Rates and Economic Growth through the Gini Index: Modeling VAR in Morocco

In this study, we analyze the cross-impact of inflation, exchange rates and economic growth on income inequality in Morocco between 2000 and 2022, based on the GINI index as a measure of inequality. Its main objective is to understand how these macroeconomic variables influence income disparities. Our methodology is based on a VAR model to capture dynamic interactions between variables. To validate the robustness of the model, Granger causality tests and specification tests, including tests of homoscedasticity and autocorrelation of residuals, were used. The result is that inflation has a significant positive impact on income inequality, and exchange rate fluctuations directly influence inequality. Furthermore, economic growth helps to reduce inequality, although this effect depends on the distribution of the benefits of this growth. This study contributes to the existing literature by providing empirical evidence of the importance of macroeconomic stability and educational and fiscal policies in reducing income inequality.

Exploring the Linkage between Temperature and Economic Growth in Bangladesh: An ARDL Approach

This study is investigated the nexus between temperature and GDP in Bangladesh and how GDP is affected by carbon emission, rainfall and temperature. The ARDL bound test is employed to examine the cointegration relationship among the variables and findings suggest that variables are cointegrated in both cases of dependent variable temperature and dependent variable of GDP. Results mirror that economic growth affect negatively the temperature and statistically significant for short run and long run. On the other hand, carbon emission and rainfall have statistically significant short run positive impact on GDP growth. The ECM result indicates the equilibrium converges to steady state at 78% annually when dependent variable is temperature and equilibrium also converges to steady state at 0.0891% annually, when dependent variable is GDP. Granger Causality test finds a one-way causal relationship between GDP and carbon emissions in both situation of the different dependent variables. Aftermath, results would say that carbon emission and rainfall have short run salutary impact on economic growth and the economic growth is conducive to reduce temperature in Bangladesh that moves to sustainable development. It might address underlying economic transitions such as shifts to greener industries or government interventions that promote sustainability. It will help researcher and policymakers for further study to identify environmental friendly growth projects that lead to reduce temperature in Bangladesh.

The Interplay of Foreign Direct Investment in Unravelling the Nexus of Technological Innovation and Economic Dynamics: An Integrated Study Using Vector Error Correction Model

Most lower-middle income countries prioritise innovation, with foreign direct investment (FDI) as a key funding source for sustainable development. This article studies the interplay between FDI, technological innovation (measured by patent-granted residents), gross domestic product (GDP), research and development (R&D) expenditure (RDE) and inflation of the Indian economy from 2002–2003 to 2022–2023. It uses a vector error correction model, Johansen cointegration tests, Granger causality tests, the impulse response function and variance decomposition. The study finds two cointegration relationships between the variables. GDP and RDE have a significant positive effect on technological innovation in the long term. Neither FDI nor inflation shows such an impact. FDI policies should therefore consider factors such as a conducive regulatory framework, tax breaks and grants for R&D to enhance their impact on innovation. JEL Codes: O31, O32, F21, F23, E01, O47

An Econometric Analysis of Inflation, Exchange Rate, and Interest Rate on Stock Market Performance in South Africa

The stock market is crucial for resource production, distribution, and mobilization because it provides listed firms with long-term funding. Despite the extensive research on the relationship between inflation and exchange rates, the influence of interest rates on stock market performance has received limited investigation. The goal of this study is to address this gap by examining extensive South African data spanning from the first quarter of 2010 to the fourth quarter of 2022. The study constructs a sample space of 48 quarters, utilizing the JSE All Share Price Index as a substitute for measuring stock market performance. Furthermore, it incorporates additional macroeconomic data from reputable sources like the Federal Reserve Bank of St. Louis and the South African Reserve Bank. We employ the auto-regressive distributed lag (ARDL) model to examine the relationship between the stock prices of the Johannesburg Stock Exchange (JSE) and various variables, including inflation, interest rates, and exchange rates. The unit root test results indicate that the exchange rate integrates at order zero (I(0)), while the share price index, inflation, and interest rate integrate at order one (I(1)). The model explains roughly 96.5% of the variability in the dependent variable. According to the findings, short-term interest rates have a significant and negative impact on the Johannesburg stock market’s performance. However, the study does not demonstrate the significant effects of exchange rates and inflation. Furthermore, the Granger causality test has proven that the independent factors have no ongoing impact on the dependent variable. Without establishing the generalizability of the findings outside these boundaries, the study limits its conclusions to South Africa and the studied period. Different methods, as the quantile autoregressive distributed lag (QARDL) or the non-linear autoregressive distributed lag (NARDL) methodology, could be used in future studies to look into these relationships. These studies could also look at other factors that affect stock market performance. These insights may help policymakers adopt the most effective monetary or fiscal policies and support investors in making well-informed decisions to diversify their portfolios and minimize risks.

A causality investigation into stock prices and macroeconomic indicators in the Indian stock market

Background of the study The systematic impact of macroeconomic variables on stock market returns makes it crucial to comprehend the link between macroeconomic variables and the stock market. Stock prices are closely linked to macroeconomic indicators, a crucial aspect for investors, policymakers, and researchers in emerging markets like India, influencing investment decisions and policy formulation. Methods The autoregressive distributed lag (ARDL) model was used in this study to examine the causal links between specific macroeconomic factors and Indian stock prices from April 2009 to March 2023. Results The outcomes of the research suggest that macroeconomic variables exert influence on the Indian stock market, across the short and long term. Moreover, the results of the paired Granger causality test suggest that the domestic macroeconomic variables possess predictive significance for stock prices in the Indian stock market. Conclusion The study reveals that macroeconomic variables significantly impact the Indian stock market, highlighting the need for investors and portfolio managers to monitor these conditions to optimize returns and mitigate risks. The Reserve Bank of India should maintain an optimal money supply to prevent inflation and exchange rate fluctuations, while bolstering the export sector and facilitating imports through initiatives like Atma-nirbhar Bharat Abhiyan and Make in India. Policies focusing on productivity, infrastructure, and a favourable business environment are also crucial. Therefore, it is crucial for investors and portfolio managers to consistently analyse the current macroeconomic conditions in order to maximize their profits and minimize risks. This research has extensive significance for comprehending the intricate connections between the stock market and macroeconomic issues.

The Impact of Capital Expenditure on Ethiopian Economic Growth

This study investigates the effect of disaggregated capital expenditure on economic growth in Ethiopia over the period from 1981 to 2021. An autoregressive distributed lag (ARDL) model, combined with a Granger causality test, is used for the econometric analysis. The empirical results reveal that capital expenditure on economic development (CAEE), recurrent expenditure (RCE), and the inflation rate (CPI) have significant positive impacts on economic growth in both the long run and short run. However, capital expenditure on administrative and general development (CEAG) has a significant negative effect on economic growth in the long run. The Granger causality test further indicates unidirectional causality from economic growth (GNI) to capital expenditure on economic development (CAEE). Based on these findings, the study recommends that the Ministry of Finance should increase its budget allocations for capital expenditures aimed at economic development in order to further stimulate economic growth. Additionally, the government should implement stricter follow-up and monitoring mechanisms to ensure the proper management of budget allocations, particularly regarding capital expenditures on social development and administrative and general development. These measures are crucial for sustaining long-term economic growth in Ethiopia.

A Study on the Relationship between Civil Automobile Consumption and Real Estate in China

Since the reform and opening up, China’s economy has been booming, especially the real estate industry and the automobile industry, which have become the first and second pillar industries of China’s economy in recent years. Different scholars have studied the impact of the real estate, residential and automotive sectors on other consumption and have different attitudes. However, few studies have analyzed the relationship between the two, and it is impossible to know the specific relationship between them. Therefore, this paper first puts forward a new theoretical hypothesis for the interaction between the two, then uses Eviews7.2 as the main data processing tool, and uses the cointegration test and the Granger causality test to conduct an empirical analysis of the annual number of new registered civil vehicles and the annual real estate and residential development investment amount in China from 2002 to 2022. The results show a long-term and stable equilibrium relationship between civil automobile consumption and the real estate and residential industry and that civil automobile consumption is a one-way Granger reason for the development of the real estate and residential industry, which is in line with one of the hypotheses. Finally, based on the conclusion, this paper proposes policy suggestions to promote real estate development through automobile consumption.

Oman’s Green Horizon: Steering Towards Sustainability Through Decarbonization and Energy Transition

This paper examines the determinants of CO2 emissions in Oman from 1990 to 2024, focusing on the impacts of energy consumption, economic growth, urbanization, financial development, and foreign direct investment. The analysis utilizes stepwise regression to systematically identify the most significant predictors, ensuring a parsimonious model. Robust least squares (ROLSs) are employed to account for potential outliers and heteroscedasticity in the data, providing more reliable estimates. Fully Modified Least Squares (FMOLSs) is applied to address issues of endogeneity and serial correlation, offering robust long-term coefficient estimates. Canonical cointegrating regression (CCR) further refines these estimates by handling non-stationary variables and ensuring consistency in the presence of cointegration. Cointegration tests, including the Johansen and Engle–Granger methods, confirm long-term equilibrium relationships among the variables; this study reveals several key findings. Energy use per capita (ENGY) and real GDP per capita (RGDPC) are consistently significant positive predictors of CO2 emissions. Urbanization (URB) also significantly contributes to higher emissions. Conversely, the Financial Development Index (FDX) and foreign direct investment (FDI) do not show significant effects on CO2 levels. The high R-squared values across models indicate that these variables explain a substantial portion of the variation in emissions. Cointegration tests confirm long-term equilibrium relationships among the variables, with the Johansen test identifying two cointegrating equations and the Engle–Granger test showing significant tau-statistics for FDX, ENGY, and URB. The VEC model further highlights the short-term dynamics and adjustment mechanisms. These findings underscore the importance of energy policy, economic development, and urban planning in Oman’s efforts towards sustainable development and decarbonization.

Investor attention and market activity: evidence from green cryptocurrencies

Purpose This paper aims to investigate the relationship between investor attention and market activity (return, volatility and volume) using a sample of 14 clean energy cryptocurrencies (hereafter green cryptocurrency), namely, Chia, Cardano, Stellar, Tron, Ripple, Nano, IOTA, EOS, Bitcoin Green, Alogrand, Hedara, Polkadot, FLOW and Tezos. Design/methodology/approach This paper use 26040 crypto-day observations and a range of econometric techniques, including Dynamic Granger causality, Panel vector autoregression (VAR), Impulse response function and the decomposition of forecast error variance. Findings Based on 26040 crypto-day observations, this paper finds a bidirectional Granger causal relationship between investor attention and all measures of market activity, namely, return, absolute volatility, squared volatility and volume. The panel VAR and impulse response function demonstrate that market activity in the green crypto ecosystem, especially volatility and volume, is considerably responsive to changes in investor attention proxied by Google search volume (hereafter Google search volume (GSV)). The findings also demonstrate a significant asymmetric effect of return and volume on investor attention since past negative shocks “or bad news” in return and volume are more likely to grab the investor’s attention. All in all, our study emphasizes the crucial role of investor attention in the green crypto ecosystem. Originality/value (i) The research is the first to shed light on investor attention in the green cryptocurrency market. (ii) The paper uses a wide range of green cryptocurrencies to offer a comprehensive picture of the green cryptocurrency ecosystem. (iii) This paper is the first to use the panel Granger causality to investigate investor attention in the cryptocurrency market which provides several advantages over the conventional Granger causality approach. (iv) This paper is the first to provide novel empirical evidence on the prevalent influence of investor attention in the green crypto market.

CIRCULAR ECONOMY AND ENVIRONMENT: ANALYSIS OF THE RELATIONSHIPS BETWEEN POPULATION, HOUSEHOLD AND SIMILAR WASTE AND GREENHOUSE GAS EMISSIONS IN ALGERIA (2010-2023)

Objective: The objective of this paper is to analyze the relationships between total population (POP), household and similar waste (DMA), and greenhouse gas emissions (GES) in Algeria from 2010 to 2023. The study explores trends in these variables and employs the Granger causality test to assess causal relationships between population growth, waste production, and emissions. Findings provide insights into the interdependencies among these factors. Recommendations for improving waste management through circular economy practices are also discussed. Theoretical Framework: The theoretical framework is based on the interrelationship between population growth, waste production, and greenhouse gas emissions. It posits that as population increases, household waste generation rises, which subsequently contributes to GHG emissions. This framework underpins the use of the Granger causality test to explore causal links among these variables in Algeria. Method: The study employs a descriptive analysis of the variables—total population, household waste (DMA), and greenhouse gas emissions (GES)—over the period 2010-2023. The Granger causality test is applied to examine causal relationships between total population and DMA, and between DMA and GES emissions. Data is analyzed to identify unidirectional causal links at a 5% significance level. Results and Discussion: Population growth directly impacts household waste (DMA) and greenhouse gas emissions (GES). Waste management is crucial for controlling emissions. Algeria needs to implement circular economy practices like recycling and waste sorting to mitigate environmental impact. Research Implications: Population growth in Algeria leads to increased waste production and emissions, necessitating improved waste management strategies and adoption of circular economy practices for sustainable development. Originality/Value: The study explores the link between population growth, household waste production, and greenhouse gas emissions in Algeria, offering insights for policymakers and sustainable waste management strategies.

The Impact of Financial Inclusion on Economic Growth in Nigeria: A Secondary Data Analysis

This study investigates the impact of financial inclusion on economic growth in Nigeria, a country where a significant portion of the population remains financially excluded despite ongoing efforts to enhance access to financial services. The problem addressed is the limited understanding of how various aspects of financial inclusion, particularly mobile money, banking services, and credit availability, affect Nigeria's GDP growth. The primary objectives of the study are to assess the current level of financial inclusion, analyze its relationship with GDP growth, and provide evidence-based policy recommendations to improve financial access and support economic development. To achieve these objectives, a secondary data analysis was conducted, employing multiple linear regression, Pearson correlation, and Granger causality tests. The findings reveal a marginally significant positive relationship between Mobile Money Subscriptions (MMS) and GDP growth, while access to formal banking services and credit availability showed no significant correlation with economic growth. These results highlight the importance of mobile money as a key driver of financial inclusion, contrasting with the limited impact of traditional banking services. Based on these findings, the study recommends enhancing mobile money infrastructure, strengthening financial literacy programs, promoting inclusive banking models, and fostering collaboration with fintech firms. Implementing these recommendations could significantly improve financial inclusion in Nigeria, ultimately contributing to sustained economic growth and enhanced welfare for the population.

The link between gender inequality, financial development, and economic growth in Nigeria: A spectral Granger causality approach

AbstractThe male and female genders have crucial roles to play in economic advancement. Thus, the main reason for this research is to pinpoint the causal link between gender inequality (GPIN), financial development (FIDX), inflation (INFL), and economic growth (GDPC) in Nigeria from the year 1980–2020 utilizing the Spectral Granger and Pairwise Granger causality methods and “Residual Augmented Least Square (RALS) cointegration. The RALS result show confirms the stationarity properties of the variables. The Spectral Granger causality outcomes show that GPIN granger causes GDPC in the long, medium, and short–term. In addition, the FIDX granger causes GDPC in the long and short–term, while the medium–term impact is insignificant. For INFL, causality only occurs in the long–term. On the other hand, the Pairwise Granger causality results show that GPIN and FIDX granger cause GDPC, while INFL is insignificant. Therefore, if GPIN and FIDX can predict economic expansion, policymakers need to create policies that can reduce diverse forms of inequalities, promote financial development, and reduce inflation to the barest minimum.

ANALISIS INTEGRASI PASAR SPASIAL HARGA TELUR AYAM RAS MENGGUNAKAN METODE VECTOR ERROR CORRECTION MODEL

Purebred chicken eggs are one of the staple commodities whose price development continues to be monitored by the government. To maintain price stability of purebred chicken eggs, an understanding of spatial market integration is needed. Spatial market integration can show the level of closeness between markets in a region. The spatial market integration in this study was modeled using the Vector Error Correction Model (VECM) method. The VECM method was chosen with the aim of seeing the relationship between the price movement of purebred chicken eggs both in the short and long term, seeing the interaction of relationships that occur in each region and forecasting the price of purebred chicken eggs. The research objects used include the price of purebred chicken eggs in Semarang City, Salatiga City, Demak Regency, and Pati Regency in the period January 2021 to December 2022. The data used is weekly time series data that has met the stationary test at the first differential level. Based on the Johansen cointegration test on the four regions, 3 cointegrations were obtained, indicating that in each short-term period all variables adjust to each other to achieve long-term equilibrium. Granger's causality test in this study showed that not all variables have a two-way relationship. The resulting MAPE value is less than 10%, so the resulting model is very feasible to use.

Short-Term Prediction Method for Gas Concentration in Poultry Houses Under Different Feeding Patterns

Ammonia (NH3) and carbon dioxide (CO2) are the main gases that affect indoor air quality and the health of the chicken flock. Currently, the environmental control strategy for poultry houses mainly relies on real-time temperature, resulting in lag and singleness. Indoor air quality can be improved by predicting the change in CO2 concentration and proposing an optimal control strategy. Combining the advantages of seasonal-trend decomposition using loess (STL), Granger causality (GC), long short-term memory (LSTM), and extreme gradient boosting (XGBoost), an ensemble method called the STL-GC-LSTM-XGBoost model is proposed. This model can set fast response prediction results at a lower cost and has strong generalization ability. The comparative analysis shows that the proposed STL-GC-LSTM-XGBoost model achieved high prediction accuracy, performance, and confidence in predicting CO2 levels under different environmental regulation modes and data volumes. However, its prediction accuracy for NH3 was slightly lower than that of the STL-GC-LSTM model. This may be due to the limited variability and regularity of the NH3 dataset, which likely increased model complexity and decreased predictive ability with the introduction of XGBoost. Nevertheless, in general, the proposed integrated model still provides a feasible approach for gas concentration prediction and health-related risk control in poultry houses.

A dynamic multidimensional Granger causality based on wavelet decomposition analysis of the tourism and economic growth in China

ABSTRACT This study employs an innovative multimodal approach, integrating Granger causality analysis with wavelet decomposition, to explore the intricate relationship between China’s tourism sector and its economic development spanning from 1995 to 2022. The primary aim is to uncover the causal dynamics between the advancement of tourism and the expansion of the economy across various temporal scopes and dimensions. Through rigorous panel bootstrap causality examinations and meticulous assessments for cross-sectional dependence and slope homogeneity, this research underscores a dynamic and symbiotic connection between China’s economic progress and the growth of its tourism industry, particularly emphasising the enduring significance of this relationship over the short-run, mid-run, and long-run.

MSA-GCN: Multistage Spatio-Temporal Aggregation Graph Convolutional Networks for Traffic Flow Prediction

Timely and accurate traffic flow prediction is crucial for stabilizing road conditions, reducing environmental pollution, and mitigating economic losses. While current graph convolution methods have achieved certain results, they do not fully leverage the true advantages of graph convolution. There is still room for improvement in simultaneously addressing multi-graph convolution, optimizing graphs, and simulating road conditions. Based on this, this paper proposes MSA-GCN: Multistage Spatio-Temporal Aggregation Graph Convolutional Networks for Traffic Flow Prediction. This method overcomes the aforementioned issues by dividing the process into different stages and achieves promising prediction results. In the first stage, we construct a latent similarity adjacency matrix and address the randomness interference features in similarity features through two optimizations using the proposed ConvGRU Attention Layer (CGAL module) and the Causal Similarity Capture Module (CSC module), which includes Granger causality tests. In the second stage, we mine the potential correlation between roads using the Correlation Completion Module (CC module) to create a global correlation adjacency matrix as a complement for potential correlations. In the third stage, we utilize the proposed Auto-LRU autoencoder to pre-train various weather features, encoding them into the model’s prediction process to enhance its ability to simulate the real world and improve interpretability. Finally, in the fourth stage, we fuse these features and use a Bidirectional Gated Recurrent Unit (BiGRU) to model time dependencies, outputting the prediction results through a linear layer. Our model demonstrates a performance improvement of 29.33%, 27.03%, and 23.07% on three real-world datasets (PEMSD8, LOSLOOP, and SZAREA) compared to advanced baseline methods, and various ablation experiments validate the effectiveness of each stage and module.

Unraveling the Effects of Economic Growth and Unemployment on Tobacco Use: A Panel Data Approach.

The economic situation is recognized as a significant factor influencing tobacco consumption. Accordingly, this research aims to investigate the relationship between economic growth, unemployment, and tobacco use. This investigation employs data from 1985 to 2019 to elucidate the relationship between economic growth, unemployment, and tobacco consumption in a select group of OECD countries. Employing Pedroni and Johansen-Fisher Panel Co-Integration Tests, we assess the co-integrating relationships. Furthermore, FMOLS and DOLS techniques are applied, complemented by the Dumitrescu Hurlin Panel Causality Test. FMOLS and DOLS results suggest that a 1% increase in GDP leads to a 6.46% and 7.29% rise in tobacco consumption, respectively, while GDP squared results in a 0.39% and 0.43% decrease. However, both tests failed to establish a significant relationship between tobacco consumption and unemployment. Cointegration was confirmed by Johansen-Fisher and Pedroni tests. Dumitrescu-Hurlin causality tests revealed direction of granger causality from economic growth to tobacco consumption, but not with unemployment. Tobacco consumption declined across all countries studied. Economic growth shows a positive association, while GDP squared has a negative association, indicating an inverted U-shaped relationship. No significant relationship was found with unemployment in the employed tests. In addition to the measures implemented by countries to reduce tobacco consumption, economic factors also exert a significant relation with smoking behavior. The available results suggests that economic growth initially associated with increase in tobacco consumption but reduction in later stages indicating a long-term inverse relationship between economic growth and tobacco use.

How green strategies and policies influence production‐based emissions, industrial development and sustainable development goals?

AbstractSustainable development is a global imperative; however, it is unattainable without a transformation of the production sector. Therefore, the study examines the impact of green strategies [Industrial innovation, Eco‐tech imports‐IMPT, and Green finances‐GFN] and policy interventions [Environmental policy‐EPY and Environmental taxes‐ETX] on production‐based emissions (PBE), industrial value addition (IVA), and sustainable development goals (SDG). The study covers the years 1990–2022 and focuses on 25 European nations. In addition, the pooled mean grouped (PMG)‐ARDL and mean group (MG) techniques were used to assess the short‐ and long‐term parameters. The country‐specific relationships are evaluated using the Bootstrap Granger Causality test. The results affirm that green strategies and policy interventions mitigated PBE and increased the IVA and SDG in Europe. However, globalization and energy use increase the PBE and IVA while impeding progress toward the SDGs. The mitigation technologies fueled with green energy (MPRE) came out as the ultimate solution, which requires green technologies and sustainable practices across all sectors. Therefore, it is suggested that governments implement eco‐friendly policies that boost GFN, reduce pollution, increase green output, and meet the Sustainable Development Goals.

Neuromodulation Effect According to Lesion Location After Dual-Mode Brain Stimulation in Patients with Subacute Stroke: A Preliminary Study

Dual-mode non-invasive brain stimulation using repetitive transcranial magnetic stimulation and transcranial direct current stimulation is known to help neurorehabilitation in patients with stroke. However, this neuromodulation effect may vary depending on the lesion location of patients with stroke, and the basis in lesion location for this is insufficient. This study aims to investigate the difference in neuromodulation effectiveness according to the lesion location after dual-mode brain stimulation using electroencephalography signals. Eight patients with ischemic subacute stroke and 11 healthy controls participated in this study. Brain stimulation was conducted in one session per day for a total of 10 days over the motor cortex, electroencephalography was measured for 5 min with eyes closed, and motor function was evaluated before and after dual-mode stimulation. The lesion location was divided into an infratentorial stroke (ITS) and a supratentorial stroke (STS) based on tentorium cerebelli. In addition, we focused on the mu and beta bands related to motor function. In terms of intrahemispheric connectivity, the mu weighted phase lag index over the contralesional primary motor cortex was significantly higher in only ITS before stimulation compared to healthy controls, and mu Granger causality over the ipsilesional primary motor cortex was significantly higher in both ITS and STS after stimulation compared to healthy controls. In contrast, from the perspective of interhemispheric connectivity, the laterality of beta Granger causality before stimulation in ITS was lower than that of healthy controls and significantly increased after stimulation. The effect of brain stimulation may vary depending on the lesion location of patients with stroke, and these findings provide indicative insights into effective dual-mode stimulation interventions for neurorehabilitation.

The moderating effects of environmental and regulatory quality on financial development to promote sustainable FDI inflows in Canada

Stimulating sustainable FDI through the connection of financial development and its moderating relationships with environmental quality and regulatory quality emerges as a crucial agenda nowadays. This study investigates into this relationship using quarterly data from 1990Q1 to 2022Q4 for Canada; employing ARDL bound tests, Granger Causality, and FM-OLS econometric models. Foreign direct investment is the dependent variable of this study. The findings confirm significant long-run relationships among financial development, stock market development, and the moderating effects of environmental and regulatory quality on FDI inflows in the Canadian economy. Conversely, in the short run, financial development, stock market development, and economic growth exhibit bidirectional causal links with FDI, while environmental quality, regulatory quality, and trade openness demonstrate unidirectional causal links with FDI. The error correction mechanism indicates that all variables quickly return to equilibrium except trade openness. Robustness checks further confirm that all the variables have fully modified co-integrating relationship with FDI inflows including the moderating effects of environmental and regulatory quality which is the innovation in the FDI-Growth existing literature. Thus, policymakers are urged to prioritize environmental quality and regulatory quality, alongside other significant explanatory variables identified in this study to promote sustainable FDI inflows.