Autoregressive Distributed Lag Model Research Articles

Pathways to China's Carbon Neutrality and Clean Energy Transition: Evidence from the Three Decades Long Stricter Environmental regulations

This study highlights the ongoing debate surrounding China's slow low-carbon transition despite a rapid shift towards clean energy. However, this debate is crucial for finding pathways to China's pledge to carbon neutrality. Using Auto-Regression Distributed Lag model over the time series data spanning 1990-2020, study attempts to add to the literature by exploring the nexus betwixt environmental regulations, renewable energy transition, and carbon dioxide emissions along with incumbent macroeconomic factors i.e., economic growth and trade. The findings conclude that stringent environmental regulations spur renewable energy transition and combat carbon dioxide emissions. However, energy intensive growth suggests China's priority for development targets ˗ leading to continued increase in fossil fuels despite the remarkable increase in renewable energy resources in country's total energy mix. Moreover, trade remains the crucial component of economic growth but found helping for renewable energy transition in the short-run. The two-way causality between these factors validate the regression results. Whereas the inelastic and insignificant relationship between stringent environmental regulations, renewables, and carbon dioxide emissions in the long run – highlights prospective challenges in terms of efficiency, management, and subsidy-related issues. Thus, the study suggest that trade liberalization helps the country to achieve its carbon abatement goals. However, further market based policy reforms toward stricter environmental regulations are needed for innovation driven efficiency in the energy sector.

Environmental degradation in geopolitical risk and uncertainty contexts for India: A comparison of ecological footprint, CO2 emissions, and load capacity factor

This study assesses the role of geopolitical risk and uncertainty in the degradation of the environment by forming the functions for ecological footprint, CO2 emissions, and load capacity factor for the period 1990-2019 in India. Besides, the specified function endogenizes economic growth, renewable energy consumption, and natural resource rent as the additional covariates. The use of the autoregressive distributed lag model (ARDL) confirms the long-run relationship between study variables. Further, the dynamic simulations of the autoregressive distributed lag model (DYNARDL) outcomes show that geopolitical risk improves the quality of the environment by reducing the ecological footprint and CO2 emissions. However, it degrades the environment by reducing the load capacity factor. Furthermore, the uncertainty improves the environmental quality by reducing the CO2 emissions and ecological footprint, but the reduced load capacity factor due to uncertainty implies the degradation of environmental quality in India. Given these findings, the study proposes different environmental conservation policies.

How does technological innovation moderate the environmental impacts of economic growth, natural resource rents and trade openness?

The objective of this study is to unravel the linear impacts of economic growth, technological innovation, natural resource rents and trade openness on carbon emissions in Malaysia during 1980–2021. It also unveils the moderating role of technological innovation on the impacts of economic growth, natural resource rents and trade openness on carbon emissions. It further analyses the nonlinear relationship between technological innovation and carbon emissions. It estimates the parameters with the Autoregressive Distributed Lag model technique. The results of the linear model reveal that economic growth, natural resource rents and trade openness contributes to carbon emissions while technological innovation mitigates carbon emissions. The disaggregated analysis of natural resource rents indicates that oil rents, natural gas rents and coal rents intensify carbon emissions while mineral rents and forest rents do not contribute to carbon emissions. The disaggregated analysis of trade openness shows that exports worsen carbon emissions while imports have tenuous effect. The disaggregated analysis of technological innovation indicates that innovation by non-residents mitigate carbon emissions while innovation by residents do not alleviate carbon emissions. Moreover, evidence from the interaction model reveals that technological innovation can favourably mitigate the adverse impacts of economic growth and trade openness on carbon emissions albeit it cannot alleviate the impact of natural resource rents on carbon emissions. Besides, the nonlinear model indicates a U-shaped relationship between technological innovation and carbon emissions. Unlike previous studies that typically focused on the direct impacts of these variables, this study unravels the impacts of the disaggregated components as well as provides insights into the moderating and nonlinear effects of technological innovation on carbon emissions. The implication of this study is that efforts to achieve a carbon-neutral economy should consider the direct and indirect impacts of economic growth, technological innovation, natural resource rents and trade openness. It is recommended for Malaysia to encourage technological innovation in her quest to abate the adverse environmental impacts of economic activities.

Uncertainty and Money Demand Function in Developing Countries

As monetary policy authorities influence many macroeconomic variables by determining monetary aggregates, their relationships with other macroeconomic variables are critical in setting the most appropriate monetary policy rules. Identifying the variables affecting money demand and having a stable money demand function is essential for monetary policy. This paper examines the stability of the money demand function for 12 developing countries over the sample 2006.Q1-2023.Q3. We employ the Autoregressive Distributed Lag Model and the Cross-Sectionally Autoregressive Distributed Lag Model because of the different degrees of integration of the selected variables. According to the results, there is a stable long-run relation in the money demand function for selected developing countries. The uncertainty variable, which is the study’s primary objective, affects money demand negatively in the long run; it does not temporarily affect the demand for money. The findings also indicate that the real GDP (inflation) positively (negatively) impacts demand for real monetary aggregates as expected. The real interest rate measuring the opportunity cost of holding money does not significantly affect money demand. Although the effect of the exchange rate is positive in the short run, it turns negative as time passes and in the long run. This finding supports the ‘wealth effect’ in developing countries.

Asymmetric Impact of Carbon Emission, Oil Price Shocks on Economic Growth in Nigeria (1981 – 2022)

The study examines the asymmetric impact of carbon emissions and oil prices on economic growth in Nigeria. The research is conducted using annual time series data running from 1986 to 2019. The study employs Nonlinear autoregressive distributed lag models (NARDL) as the estimation technique. The variables used in the analysis were Gross Domestic Product (GDP), Carbon Emission (CO2), and Oil Price (OP). The NARDL result shows that carbon emission has a negative and significant impact on economic growth in the long run and the oil price has a positive and significant impact on economic growth in both the short-run and long-run. Based on the empirical outcomes of the result obtained, the following recommendations were offered: The implementation of an economic diversification policy from oil reliance towards dependence on other sustainable energy types (renewable energy resources) should be sustained and strengthened especially. The private sector should also tap into the opportunities that are available in the oil sector, such as the decarbonization programme in order to reduce carbon emissions from fossil fuels.

Dynamic linkages between element input structure, global value chain, and carbon emissions: evidence from a heterogeneous panel cointegration model

ABSTRACT Industrial CO2 constitutes a dominant component of global CO2 emissions. Therefore, modifications in industrial production processes are crucial. We analyse global cross-national panel datasets from 1995 to 2018 using the panel autoregressive distributed lag (ARDL) model to provide comprehensive evidence of the combined effects of input-side element structures and production-side global value chain (GVC) divisions on output-side CO2 emissions. The results show that: (i) Renewable energy and service elements in production factors can reduce CO2 emissions, though their abatement effects vary. Both can produce sustained carbon mitigation effects in the long term, but only renewable energy is effective in the short term. (ii) The GVC division pattern can induce a CO2 emission increase in both the short and long term, though these effects are heterogeneous depending on the differences in forward-backward GVC divisions. (iii) A path dependence analysis shows that input-side element structures can change production-side GVC divisions, thereby affecting output-side emissions. This study extends our understanding of the environmental effects of various production links and helps coordinate high-quality economic development and impending environmental constraints.

Exploring the impacts of agricultural emissions from natural gas on ecological footprint

BackgroundThis study investigates the long-term effects of agricultural natural gas emissions on ecological footprints across 19 European countries from 2006 to 2020. Employing Cross-Sectional Distributed Lag and Cross-Sectional Autoregressive Distributed Lag models, the research aims to deepen the understanding of agricultural emissions’ dynamics and their impact on ecological sustainability.ResultsThe study reveals that reductions in renewable energy consumption negatively affect ecological footprints, indicating the crucial role of renewable energy adoption in environmental sustainability. The findings emphasize the need for policies that promote renewable energy and address barriers to its adoption. Additionally, the research identifies significant correlations between population growth and ecological footprints, demonstrating the influence of demographic factors on environment. The analysis highlights significant correlations between population growth and ecological footprints, underscoring the importance of demographic trends in shaping environmental policy.ConclusionsThe policy implications of this study include advocating for sustainable urban planning and incentivizing eco-friendly agricultural practices to mitigate emissions and promote environmental sustainability. By enhancing our understanding of the relationship between agricultural emissions and ecological footprints, this research provides valuable insights for evidence-based environmental policymaking in European countries.

Determinants of Inflation in Pakistan: A Comprehensive Analysis of Macroeconomic Variables (1991–2022)

This research paper investigates the determinants of inflation in Pakistan from 1991 to 2022, focusing on key macroeconomic variables such as imports, GDP growth rate, GDP per capita, military expenditure, population growth, total debt service, and unemployment. The main objective is to analyse the impact of these variables on inflation and provide policymakers with actionable insights. Utilizing annual time series data from reliable source World Bank WDI, the study employs descriptive statistics, correlation analysis, ADF unit root tests, regression analysis, and the Autoregressive Distributed Lag (ARDL) model. The findings reveal significant interdependencies among the examined variables. The correlation analysis shows strong positive relationships between inflation and imports and military expenditure, while GDP per capita and population growth negatively correlate with inflation. OLS results confirm that imports, GDP growth, and total debt service significantly raise inflation, whereas GDP per capita, military expenditure, and population reduce inflation. The ARDL model reveals that, in the long run, imports, GDP growth, and debt service positively affect inflation, while GDP per capita, population growth, and unemployment exert downward pressure. Short-run dynamics indicate that inflation quickly adjusts to economic shocks, with imports and GDP growth having an immediate impact. These findings highlight the importance of managing imports and debt, while promoting economic growth to maintain price stability. The policy implications emphasize the need for targeted monetary policies to manage imports and optimize debt service strategies, as well as policies aimed at enhancing economic growth and increasing per capita income. Effective debt management and strategic military spending are also vital for maintaining price stability. Despite its comprehensive analysis, the study identifies gaps such as the need for further exploration of structural breaks and non-linear effects in inflation dynamics, and the impact of external factors like global oil prices. The significance of this research lies in its contribution to understanding the complex economic interactions influencing inflation in Pakistan, providing a crucial resource for policymakers to develop effective strategies for economic stability in developing economies.

Analysis of Ghana—Nigeria Export Trade: Linkages, Impacts and Policy Implications

This study hypothesised that the Ghana-Nigeria export trade has a significant impact on the economy of Ghana. Time series on variables of interest were obtained from the World Development Indicator (WDI) of the World Bank and the Ghana Statistical Service (GSS). The 2018 input-output table for Ghana was obtained from the social accounting matrix of the Organisation for Economic Co-operation and Development (OECD). The data were analysed using descriptive statistics, input-output analysis, and the autoregressive distributed lag (ARDL) bound testing approach. Through input-output analysis, this study found significant total impacts (151.585 to 190.139 million Ghana Cedis) in 2017 and 2018, centred around export commodities such as food and live animals, beverages, and tobacco. The expanding field of influence (214.373 to 268.898) and the ratio of impacts to Ghana's GDP (0.058% to 0.062%, then 0.044%) substantiate their pronounced role in the economy. The ARDL model showed that a percent increase in Ghana-Nigeria exports led to a 0.1017% increase in Ghana’s real GDP on the log-run. The speed of adjustment was 42.18% in each period. By providing support to exporters, both countries can leverage their comparative advantages, strengthen economic ties, and foster sustained growth through increased export trade.

The determinants of volatility connectedness of South African equity super sectors

The paper aims to explore the determinants of total volatility connectedness of nine super sectors on the Johannesburg Stock Exchange (JSE) market from 3rd January 2006 to 31st December 2021. These sectors are Automobile and Parts, Chemical, Telecommunication, Technology, Energy, Health, Finance, Insurance, and General Industrials. The paper applied Diebold and Yilmaz connectedness matrix and the time-varying parameter – vector autoregressive (TVP-VAR) model to determine the sectorial total volatility connectedness index (STVCI). After that, the nonlinear autoregressive distributed lag model (NARDL) was used to determine the asymmetric effects and the drivers of STVCI. It was found that the partial sum decomposition of the South African volatility index (SAVI) and Economic Policy Uncertainty Index (EPU) are the key determinants of the STVCI both in the long and short run. However, domestic market return (DMR) shows no significant asymmetric effect on STVCI. The study concluded that SAVI and EPU are the key determinants of volatility connectedness among the JSE super-sectors. The results unveil important implications for sectorial investors and policymakers on potential regulations and stability of the significant determinants of spillover risk.

Inference of Some Macroeconomic Variables on Nigeria Unemployment Rate

This research investigates the impact of foreign direct investment (FDI), government expenditure (GOE), and inflation rate (IFR) on the unemployment rate (UPR) in Nigeria from 1985 to 2021 through Autoregressive Distributed Lag (ARDL) modeling approach. An initial assessment to test the significance of signals between each independent variable and UPR was performed using rolling correlation analysis. Subsequently, the bounds test methodology to examine cointegration among between the FDI, GOE, IFR, and UPR was performed. Additionally, the causal relationship between these economic variables was performed through the Error Correction Model (ECM) approach. The estimated ARDL model parameters stability was determined using the cumulative sum (CUSUM) of squares chart. The Augmented Dickey Fuller unit root test suggests that the variables are stationary at first differences (I(1)). The bounds test revealed that the variables are cointegrated at 1%, 5%, and 10% indicating a long run relationship between UPR and FDI, GOE, and IFR. The ARDL results indicates that at 5%, a unit increase in FDI at lag one have a long run significant decreasing impact on UPR by 19.96%. But in the short run, the FDI at lag one has a significant increasing effect on UPR by 7.23%. However, the CUSUM of square chart shows unstable parameter estimation based on the Akaike Information Criteria selected model, ARDL (1, 3, 0, 0). The study concludes that UPR is being influenced by FDI in reducing UPR in the long run. Recommendation based on the findings is that FDI should be considered most important when policies are drafted for tackling the issue bordering UPR in Nigeria.

Balancing Economic and Construction Growth with Environmental Sustainability in Albania’s Real Estate Sector

This study aims to examine the relationship between real estate development and environmental impact in Albania during the period of 1995–2022. This is among the first scientific studies of this nature in Albania and the region. Using an Autoregressive Distribution Lag (ARDL) model and EViews 12 software, we analyze how carbon emissions relate to economic indicators such as the issuance of building permits, Gross Domestic Product (GDP), and energy consumption patterns. Our results show a positive relationship between construction activities and increased carbon emissions, signaling a development model that currently diverges from sustainable practices. This research is important as it not only fills a critical gap by quantifying the environmental footprint of the real estate sector in Albania but also provides strong signals to support policy makers in guiding sustainable development initiatives. This study recommends that future strategies integrate and harmonize economic growth with environmental care by targeting sustainability in construction techniques and renewable energy.

An analysis of the effects of aging society on global stock markets

PurposeThe primary purpose of this study is to explore the effects of demographic transition toward aging populations on the performance of stock market indices across various economic developments. The research aims to provide valuable insights into the life-cycle hypothesis on savings patterns, investment behavior and the potential reverberations on global financial markets.Design/methodology/approachThe study adopts a comprehensive global perspective, scrutinizing the effects of aging populations on stock market indices across developed, developing and transitional economies through the panel data analysis. Using annual data spanning the period from 1991 to 2020, encompassing a sample of 10 countries from each economic development level, the study employs the panel autoregressive distributed lag (ARDL) model with fixed effect estimation.FindingsThe findings unveil a statistically significant positive impact of the elderly population proportion on global stock market indices. However, the magnitude and contours of this impact exhibit considerable heterogeneity across different country groups. Specifically, the study finds that while the aging population significantly influences stock market performance in developed nations, its effect is overshadowed by other economic factors, such as consumer price indices and interest rates, in developing countries and economies in transition.Originality/valueThe originality and value of this study lie in its comprehensive global perspective, which encompasses a diverse array of economies at varying developmental stages. The research contributes to an understanding of the effects of demographic transitions on stock market performance on a global scale. The insights derived from this study hold significant implications for policymakers, financial institutions and investors seeking to navigate the challenges and opportunities posed by aging societies in an increasingly interconnected global economy. Additionally, the findings highlight the need for specific strategies and policies that account for the unique economic characteristics and developmental stages of different nations.

Sustainable supply chain management operations: does sustainable environmental disclosure matter for banks’ financial performance in Nigeria?

In recent years, banks have incorporated sustainable development strategies to achieve competitiveness in the financial market. However, the primary concern for banks is the financial pressure to implement sustainable environmental practices. In addition, banks are obliged by different regulators to maintain standards in conducting their business and allocating funds. Previous studies examined the relationship between sustainability reporting, corporate social responsibility (CSR), and firms’ financial performance. Few empirical studies examined the impact of sustainable supply chain management (SSCM) operations on firm financial outcomes, yet the banking sector has been ignored in the analyses. To bridge this gap, we examine the dynamic effect of SSCM operations on deposit money banks’ (DMBs) financial performance. We selected seven banks in Nigeria with national, regional and international authorization and data spanning from 2005 to 2023. Based on the panel co-integration test and the Cross-sectional Dependence Autoregressive Distributed Lag model (CS-ARDL) model, we found that SSCM operations have a significant influence on the financial performance (market value and profitability) of the banks. Impliedly, banks’ SSCM operations and environmental consideration in the allocation of funds drive their corporate image and value. Integration of SSCM strategies for a sustainable environment can help enterprises/companies bridge information asymmetry among diverse stakeholders. We offer some policy suggestions in line with the empirical findings.

Asymmetric relationship between crude oil price and remittance inflows in a small island economy: Evidence from non-linear ARDL approach

Sri Lanka's economy heavily depends on remittance inflows to finance its import of essential and intermediate goods. A decline in remittances could severely limit the country's import capacity, affecting overall economic growth and development. This study investigates the impact of crude oil prices on Sri Lankan remittance inflows from 1980 to 2020, while accounting for per capita GDP, financial development, and exchange rates as additional explanatory variables. Employing a nonlinear autoregressive distributed lag (NARDL) model, the analysis reveals significant asymmetric effects of oil price shocks on remittance inflows. In the short run, positive oil price shocks lead to increased remittance inflows, whereas negative shocks reduce them. However, in the long run, both positive and negative oil price shocks have an adverse impact on remittance inflows. Furthermore, changes in per capita GDP, financial development, and exchange rates also play a crucial role in influencing remittance inflows. The findings highlight Sri Lanka's vulnerability to global oil market fluctuations and suggest policy measures to mitigate these risks. Key recommendations include diversifying labor export markets, enhancing financial infrastructure, and reducing dependence on remittance inflows to sustain import capacity and promote long-term economic resilience.

Unravelling the Dynamics of Macroeconomic Variables and Infant Mortality in India Based on ARDL Model Analysis

The well-being of mothers and children serves as a vital indicator of a nation’s prosperity and is influenced by a range of factors, including macroeconomic variables such as Gross Domestic Product (GDP), unemployment, inflation, income, education, and healthcare expenditure. Escalating inflation precipitates higher food prices, amplifies household living costs, and diminishes purchasing power, consequently exerting a substantial impact on individuals’ nutritional and physical health. This study aims to examine the correlation between macroeconomic variables, specifically GDP per capita, Consumer Price Index (CPI), unemployment, and infant mortality in the Indian context. Despite the significance of this relationship, there exists a dearth of research on the association between macroeconomic variables and maternal and child mortality, particularly within the Indian context. Prior studies consistently underscore the indispensability of macroeconomic stability in achieving improved outcomes in maternal and child health. The analysis draws upon secondary data procured from reputable sources such as the World Bank and Reserve Bank of India (RBI), encompassing time series data about both the dependent variable, namely the infant mortality rate, and the explanatory variables. To investigate the impact of macroeconomic variables on infant mortality, the study employs the Autoregressive Distributed Lag (ARDL) Error Correction Model, which accounts for the interplay between the variables over time. Empirical findings establish the existence of long-term cointegration between macroeconomic variables and the infant mortality rate. However, in the short run, some variability in cointegration arises due to a multitude of factors, including policy interventions, demographic characteristics, and socio-cultural determinants. This study substantiates the proposition that sustaining macroeconomic stability and fostering economic growth play pivotal roles in attaining health sector objectives, particularly in emerging economies like India. Consequently, while formulating health policies, equal emphasis must be placed on measures aimed at stabilizing the economy to ensure favourable outcomes of such policies.

Assessing the influence of foreign direct investment on renewable energy consumption in Somalia: an ARDL model analysis

ABSTRACT This study examines the impact of Foreign Direct Investment (FDI) on renewable energy consumption in Somalia from 1990 to 2019 using the Autoregressive Distributed Lag (ARDL) model. The findings reveal a positive, statistically significant relationship between FDI, Gross Domestic Product (GDP), trade openness, and renewable energy consumption in the long run. Specifically, a 1% increase in FDI leads to a 0.0000115% increase in renewable energy consumption. Similarly, a 1% increase in GDP leads to a 0.026041% increase in renewable energy consumption. Meanwhile, a 1% rise in trade openness enhances renewable energy consumption by 0.0000280%. Increased foreign investments and economic growth promote the adoption of renewable energy, aligning with sustainable development principles. Conversely, environmental degradation negatively impacts renewable energy consumption. Specifically, a 1% increase in environmental degradation leads to a 0.570376% decrease in renewable energy consumption. Policymakers should incentivize FDI in the renewable energy industry through tax incentives, streamlined regulations, and public-private partnerships. Strategies promoting economic growth and integrating renewable energy objectives are essential, with trade openness facilitating the importation of renewable energy technologies. A limitation of this study is the reliance on yearly data, which may not capture recent developments or short-term fluctuations. Future research should use more granular, up-to-date data to understand the dynamics in Somalia better.

Tourism’s Influence on Economic Growth and Environment in Saudi: Present and Future

Reports from the World Tourism Organization indicate that tourism activity has been increasingly booming; this sector is essential for economic growth and may affect the environment. Tourism is one of the key strategic sectors for planned growth in Saudi Arabia’s Vision 2030. This study is designed to evaluate the long-termning association between tourist arrivals, growth domestic product (GDP), and CO2 emissions in the Kingdom of Saudi Arabia (KSA). The data related to these variables were assessed for the period 2010 to 2020. The autoregressive distributed lag (ARDL) bounds results revealed that there are long-established relations between tourist arrivals and growth domestic product and tourist arrivals and CO2 emissions. The dynamic ordinary least squares (DOLS) and fully modified ordinary least squares (FMOLS) model outcomes were compatible with the outcomes of the ARDL model. In reference to the Granger causality test, tourist arrivals cause (affect) the GDP. Such a result confirms the conception that tourism encourages economic growth. No causality runs from tourist arrivals towards CO2 accumulation. This result may reflect the governmental effort to reduce CO2 accumulation and/or to perform tourism activities in a sustainable way. The results predicted that the growth rate of tourist arrivals, GDP, and CO2 accumulation equal 0.0023, 0.048, and 0.0169, respectively, during the forecast period (2021–2030), which appeared to be increasing for tourist arrivals and GDP and decreasing for CO2 accumulation. The study recommended that, to increase economic growth, tourist arrivals should be increased alongside performing tourism activities in a sustainable way. These findings point to the benefits of governance in ensuring effective policies to decarbonize the environment, and policy proposals are put forward accordingly.

Towards Environmental Sustainability: The Impact of External Debt and Government Expenditure on Carbon Emissions in Somalia

This study aims to assess the effect of external debt and government expenditure on carbon emissions in Somalia. To do so, the study employed a rigorous econometric approaches such as autoregressive distributed lag model (ARDL), Fully modified least square (FMOLS), and Dynamic least square (DOLS). Findings demonstrated that external debt (LNED) positively and significantly affects carbon emissions. Results revealed that a 1%, or a point increase of external debt, rises 0.29% into the emitted emissions. Moreover, the study found that the long-run coefficient of government expenditure (LNGE) positively correlates with emissions. However, a 1% increase in total government expenditure significantly increases CO2 emissions by 0.05% in the long run. So, regarding the positive correlation between external debt, government expenditure, and carbon emissions, the study suggests that the government should strategically use its expenditures and external debt to fund environmentally sustainable projects. This approach aims to reduce carbon emissions while promoting sustainable development and balancing economic growth with ecological conservation.

Election and Wallet: The Effect of Election Periods and Consumer Expectations on Consumer Confidence with ARDL and QARDL Model Approach

The growing significance of consumer confidence and the explanation of the factors influencing this confidence is an important issue worthy of study. This study aims to examine the effects of election periods and consumer expectations on the Consumer Confidence Index (CCI) in Turkey. To this end, the Autoregressive Distributed Lag (ARDL) and Quantile Autoregressive Distributed Lag (QARDL) models will be employed. Data on the variables in question were collected on a monthly basis from Turkish Statistical Institute (TUIK) over the period spanning 2012:1 to 2024:3. The ARDL model findings indicate that election periods exert a positive and significant influence on the TGE, both in the short run and in the long run. The intention to buy or have a house built and the intention to save have positive and significant effects on the TGE in both the short and long run. Conversely, the intention to buy a car and the cost of living index have a positive and significant effects on the TGE in the short run, but no significant effect was found in the long run. The QARDL model reveals that asymmetry is present, with all variables exhibiting disparate effects across different quartiles. The findings of this study indicate that in order to enhance consumer confidence and guarantee economic stability, it is essential to prioritise the housing market, savings patterns and reducing the cost of living. The development of policies that ensure the sustainability of the confidence and stability created during election periods is crucial. The research offers invaluable insights into ARDL and QARDL models that can inform Turkey's economic policies.