Econometric Framework Research Articles

Incorporating Environmental Sustainability and Supply Chain Management in Contemporary Interior Design: A Study on Chinese Landscape Paintings

The primary objective of this empirical investigation was to comprehensively examine the economic, environmental, and socio-cultural factors that exert a substantial influence on the collective impact of Chinese landscape paintings and modernist interior design. The study applied a complete econometric framework, integrating unit root tests, lag selection criteria, and autoregressive distributed lag (ARDL) models. The results indicate a statistically significant and enduring association between essential factors, such as customer demand, price elasticity, and supply chain costs. The ARDL models demonstrate significant short-term and long-term effects, as seen by the substantial R-squared values reaching 84.4%. These findings confirm the variables’ strength and explanatory capability and incorporate stepwise and robust least squares models, further supportingthe obtained results. The present findings have important implications for professionals in various fields of art interior design. The results provide valuable data-driven insights that can inform the creation of sustainability and economic feasibility in these domains. This study is a crucial reference point for future academic inquiries and practical applications in the interdisciplinary fields of art, design, and supply chain management.

Modeling the impact of exchange rate fluctuations on agricultural performance: Evidence from Morocco during the period 2000-2023

The agricultural sector is a cornerstone of Morocco's economic development, contributing significantly to food security, employment, raw materials for agro-industries, and national GDP. This study investigates the impact of Real Effective Exchange Rate (REER) fluctuations on agricultural performance in Morocco from 2000 to 2023. Using an econometric framework, three models were developed: the first examines the direct effects of REER on agriculture, the second explores the implications of REER misalignments (calculated via the Behavioral Equilibrium Exchange Rate (BEER) approach), and the third analyzes the distinct impacts of REER overvaluation and undervaluation. The ARDL method was employed to estimate these models, incorporating control variables such as GDP Growth Rate, inflation, and foreign direct investment. The findings reveal that REER fluctuations significantly influence agricultural performance, with undervaluation enhancing export competitiveness and overvaluation diminishing profitability and production. REER misalignments, particularly overvaluation, have adverse long-term effects on the sector’s competitiveness. Conversely, undervaluation fosters growth through export stimulation and increased investments. The study underscores the need for a strategic exchange rate management policy aimed at stabilizing the REER and mitigating misalignments to support sustainable agricultural growth and competitiveness in Morocco. These insights provide a foundation for policymakers to devise macroeconomic strategies that enhance the agricultural sector’s resilience and contribution to national economic development.

Determinants of access to animal health care in France: evidence from a spatial econometric framework

Determinants of access to animal health care in France: evidence from a spatial econometric framework

Exploring and modeling cumulative bias and its asymmetry in student evaluations of teaching at a Polish university

Purpose This paper aims to investigate the usefulness and validity of student evaluations of teaching (SET) by estimating multiple biases and their cumulative effect, and assessing their implications for evaluating teaching effectiveness. Design/methodology/approach The study uses a rich dataset from a Polish university and applies linear and quantile regressions to estimate SET biases, including course difficulty, class size and instructor characteristics. The cumulative effect of these biases is measured, and changes during the COVID-19 pandemic are analyzed to assess their impact on SET scores. Findings The cumulative SET bias reaches more than one point on a 1–5 Likert scale, challenging the reliability of raw SET scores. Significant asymmetries exist between low and high SET scores. Poor initial evaluations of a teacher predict future low performance ratings, while top-rated teacher contests are often influenced by chance rather than teaching quality. Practical implications The findings suggest universities should discontinue using raw SET scores for faculty evaluation and instead implement adjustments for identified biases. This approach will provide a more accurate measure of teaching performance. Originality/value This paper builds on earlier studies that applied econometric frameworks to analyze SET bias predictors and offers a novel, comprehensive assessment of cumulative SET biases and their asymmetries. It is the first to evaluate the effects of multiple SET biases within a single model and the first to document how SET biases intensified during the pandemic, emphasizing the need for significant reform in teaching evaluation practices.

Developing CUSTOM framework: explore telecommuting-induced activity-travel demands with mode choice

This paper presents an econometric framework of jointly modelling daily activity scheduling (activity type, time expenditure, and location choices known as the CUSTOM system) and travel mode choice considering Random Utility Maximization (RUM) behaviour. The joint model is applied to model workers’ daily activity-travel demand with flexible work arrangement choices. The joint framework is flexible to capture workers’ activity-travel patterns under different workplace (telecommuting, not telecommuting, or hybrid) arrangement options. The model is empirically estimated using datasets collected in the Greater Toronto Area (GTA) in 2021. The analysis explores how different workplace arrangements affect activity-travel demand. The model is calibrated to simulate scenarios where the distribution of work-from-home workers varied between the levels observed from 2016 to 2021. The scenario analysis validates the behavioural predictability of the joint CUSTOM system. This highlights the potential of the agent-based activity-based modelling system to forecast the influence of disruptive events on travel behaviours.

What determines outward FDI in developing blocs? A new empirical comparative macroeconomic perspective of post 1990s

This study compares the relationships between important macroeconomic variables and outward foreign direct investment (OFDI) in the economies of the BRICS and N-11 countries between 1990 and 2019. The analysis employs a comprehensive econometric framework, with ordinary least squares (OLS) serving as the baseline model, followed by three-stage least squares (3SLS) to account for simultaneous relationships, and the generalized method of moments (GMM) for the robustness check to handle any endogeneity. The primary macroeconomic factors that are examined are GDP, financial development, trade openness, technological advancement, human capital, and inward foreign direct investment. The empirical results verify that, in both groups of economies, OFDI has a long-term positive and significant correlation with GDP, financial development, and trade openness. GDP is also identified as a critical outcome variable in the 3SLS model, which shows that higher GDP levels are associated with increased trade openness, financial development, and OFDI. There is conflicting evidence regarding the magnitude and statistical significance of these associations, and their direction and strength differ among models and country groups. These results demonstrate the intricate relationship between OFDI and macroeconomic factors, emphasizing the necessity of distinct policy strategies adapted to the institutional and structural realities of the N-11 and BRICS economies.

Stock market volatility and multi-scale positive and negative bubbles

We study whether booms and busts in the stock market of the United States (US) drives its volatility. Given this, first, we employ the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator (MS-LPPLS-CI) approach to identify both positive and negative bubbles in the short-, medium, and long-term. We successfully detect major crashes and rallies during the weekly period from January 1973 to December 2020. Second, we utilize a nonparametric causality-in-quantiles approach to analyze the predictive impact of our bubble indicators on daily data-based weekly realized volatility (RV). This econometric framework allows us to circumvent potential misspecification due to nonlinearity and instability, rendering the results of weak causal influence derived from a linear framework invalid. The MS-LPPLS-CIs reveal strong evidence of predictability for RV over its entire conditional distribution. We observe relatively stronger impacts for the positive bubbles indicators, with our findings being robust to an alternative metric of volatility, namely squared returns, and weekly realized volatilities derived from 5 (RV5)- and 10 (RV10)-minutes interval intraday data. Furthermore, we detect evidence of predictability for RV5 and RV10 of nine other developed and emerging stock markets. In addition, we also find strong evidence of causal feedbacks from RV5 and RV10 on to the MS-LPPLS-CIs of the 10 countries considered. Finally, time-varying connectedness of the RVs of the G7 stock markets is also shown to be strongly (positively) predicted by the connectedness of the six bubbles indicators. Our findings have significant implications for investors and policymakers.

Nonlinear input cost pass-through to consumer prices: a threshold approach

ABSTRACT This paper examines a possible nonlinearity in the pass-through to consumer price index (CPI) inflation of increases in firms’ input costs. Extending the existing econometric framework, we propose a threshold model with multiple cost variables and unknown thresholds. An empirical analysis shows that the proposed model is favoured over a linear one and existing threshold models in terms of in-sample model fit. The empirical results indicate that the linear model risks underestimating the degree of the pass-through of an input-cost increase that exceeds the threshold.

Modelling green knowledge production and environmental policies with semiparametric panel data regression models

AbstractThis paper introduces a novel semiparametric econometric framework for policy evaluation and estimates a green knowledge production function for a large, 30-year panel dataset of high-income countries. Due to the substantial uncertainty in the data-generating process and the potential presence of nonlinearities and latent common factors, the paper explores semiparametric panel specifications that go beyond interactive fixed effects fully parametric models. The findings suggest that (i) the semiparametric additive specification with individual time trends is the preferred model, (ii) threshold effects and nonlinearities are salient features of the data that parametric specifications fail to capture, and (iii) the impact of environmental policy is noteworthy and exhibits clear heterogeneity when modelled as a nonparametric function of specific knowledge inputs. The evidence reveals a significant nonlinear policy inducement effect stemming from R&D investments.

Investigating the complex landscape of climate finance in least developed countries (LDCs)

This study aimed to investigate the complex landscape of climate finance, assessing the adequacy, predictability, and implications for sustainable development in least developed countries (LDCs). This study is motivated by the pressing need to assess the adequacy, predictability, and implications of climate finance for sustainable development in least developed countries (LDCs). Employing an econometric framework, this study utilizes ARIMA models to analyze time series data (from 2000 to 2021) on climate finance. The analysis revealed a notable gap between the needed and actual climate funding received by LDCs. Despite an annual requirement of $93.7 billion according to the UK-based International Institute for Environment and Development (IIED), LDCs have only received an average of $14.8 billion annually since 2015. The study suggests that climate funding for LDCs lacks predictability and falls short in meeting their needs, potentially facing an 80% decrease by 2030 under certain scenarios. It advocates for a strategic revamp in climate finance mechanisms to ensure adequacy and predictability, urging policymakers and international funding bodies to adopt more robust, fair, and needs-based approaches to climate financing. This research emphasizes the responsibility of developed nations and global agencies in bridging the considerable funding gap faced by LDCs. By integrating advanced forecasting techniques with a comprehensive analysis of global economic and political factors, this study sheds light on the challenges LDCs encounter in securing stable and sufficient climate finance, stressing the urgency for systemic reforms in global climate finance policies.

Dynamic effects of energy aid in energy transition under the moderating role of the government's regulatory quality

Energy aid to Asia Pacific countries seems attractive for decarbonization and renewable energy transition. However, no clear picture has been drawn on the synergetic connections between the energy aid and the transition to renewable energy in the Sustainable Development Goal 7 (SDG7) paradigm. Therefore, this study is designed to fill this gap by examining the energy aid and its response to energy transition, measuring it with four sub-indicators of SDG7. Using a robust econometric framework, the empirical findings indicated that total energy aid has substantial potential in transitioning to renewable energy, and the link is more pronounced with energy policy aid and renewable energy aid in the Asia Pacific. The response of energy aid through the moderating effect of government regulatory quality on energy transition is also strong. These results carried similar signs and magnitude across the regional panels, estimates with multiple estimators, including asymmetric analysis, making the results robust for theoretical foundations. The novel findings from this study have scope for policymakers to design a practical policy roadmap for the transition of renewable energy to reach a carbon-neutral globe.

Does higher education matter in mitigating chronic disease Mortality? evidence from MENA countries with consideration of Globalization, economic Growth, and environmental pollution

This study investigates the relationship between higher education and mortality rates from chronic and fatal diseases in MENA countries from 2000 to 2020. Utilizing a robust panel econometric framework with Generalized Least Square, Fully Modified Ordinary Least Squares (FMOLS), and Dynamic Ordinary Least Squares (DOLS), we analyze empirical data while controlling for globalization, CO2 emissions, and GDP per capita. Our findings reveal a significant negative correlation between education and mortality rates, paralleled by similar trends for globalization and GDP per capita. Conversely, CO2 emissions are found to increase mortality rates, highlighting the detrimental impact of environmental degradation on public health. This underscores the pivotal role of education, globalization, and economic development in reducing mortality rates associated with chronic and fatal diseases. The study advocates for increased investments in education and healthcare infrastructure to address disparities and enhance public health outcomes, alongside promoting responsible globalization practices to further improve health metrics.

Spillover effects in transit networks: A parameterized weight matrix spatial lagged approach

Public Transit (PT) systems aim to provide social, economic, and environmental benefits to modern cities offering reliable transportation service to users and ultimately reducing the problems related to traffic externalities due to car dependency. With the growing trend of urbanization and the associated phenomenon of cities’ sprawl being increasingly evident, significant attention should be given to understanding PT systems’ performance and then improving their efficiency. Considering the spatial characteristics of the PT system's performance promotes the environmentally friendly transport “character” that every city endeavors. This paper aims to incorporate the spatial spillover effects of a realistic PT system by augmenting information about the service network along with socio-economic variables, in a spatial demand-supply econometric framework. In detail, geographically separated demand and supply information on bus stops and lines was analyzed by a spatial econometric model, namely, the Spatial Lagged X (SLX) model which may be formatted so that can soundly handle, spatial and –in particular- network data like those coming from the General Transit Feed Specification (GTFS) protocol widely used in PT systems. The novelty and the importance of the proposed model rely on the ability to introduce transit network structure within the framework of spatial econometric regression, fostering the explanatory statistical analysis over networked information. The developed modeling approach was applied to the urban and rural PT system of Nicosia (Cyprus), incorporating the spatial spillover effects of the system over more than 1,500 bus stops, 40 lines, and 252 different but spatially connected postcode areas. The results of the SLX model were compared with other demand models of this form typically used, namely the Ordinary Least Square model and standard Spatial Autoregressive models, providing solid evidence of the benefits of incorporating the network structure in spatial demand modeling, giving valuable input for further planning purposes.

Own or public? Later livelihoods of Ukrainian war migrant households in Poland

AbstractThis paper presents empirical research on the livelihood resources of Ukrainian households that fled to Poland due to the Russian invasion and reside there long term. Using a nationwide sample of these households and an accurate econometric framework, we investigate the factors influencing the role of income from work, other private resources and public assistance in covering their living costs in exile. Our findings confirm the importance of public assistance addressed to professional activation and childcare for households' self‐reliance. Assistance from employers, housing conditions, intentions to settle permanently in Poland and financial responsibility for relatives in Ukraine are also among the decisive factors. However, sufficient monetary aid hinders households' self‐reliance, along with health problems, language barriers and a lack of professional qualifications. Household composition is also significant, as those with children face the most adverse outcomes. Based on our results, we provide recommendations for public authorities responsible for migration policy.

Housing search activity and quantiles-based predictability of housing price movements in the USA

Purpose Recent evidence from a linear econometric framework infers that housing search activity, captured from Google Trends data, can predict housing returns for the USA at a national and regional (metropolitan statistical area [MSA]) level. Based on search theory, the authors, however, postulate that search activity can also predict housing returns volatility. This study aims to explore the possibility of using online search activity to predict both housing returns and volatility. Design/methodology/approach Using a k-th order non-parametric causality-in-quantiles test allows us to test for predictability in a robust manner over the entire conditional distribution of both housing price returns and its volatility (i.e. squared returns) by controlling for nonlinearity and structural breaks that exist in the data. Findings The analysis over the monthly period of 2004:01 to 2021:01 produces results indicating that while housing search activity continues to predict aggregate US house price returns, barring the extreme ends of the conditional distribution, volatility is relatively strongly predicted over the entire quantile range considered. The results carry over to an alternative (the generalized autoregressive conditional heteroskedasticity-based) metric of volatility, higher (weekly)-frequency data (over January 2018–March 2021) and to over 84% of the 77 MSAs considered. Originality/value To the best of the authors’ knowledge, this is the first study regarding predictability of overall and regional US housing price returns and volatility using search activity, based on a non-parametric higher-order causality-in-quantiles framework, which is insightful to investors, policymakers and academics.

Foreign direct investment on economic growth in Nepal: A sector specific analysis

This paper investigates the impact of Foreign Direct Investment (FDI) on the economic development of Nepal through a comprehensive and sector-specific analysis. With a growing interest in attracting foreign investments to foster economic growth, this study aims to provide nuanced insights into the diverse effects of FDI across various sectors in the context of Nepal. Utilizing a robust methodology that incorporates quantitative and qualitative approaches, the research examines key economic indicators and sectoral performance data over a specified period. This paper employs a sophisticated econometric framework that includes unit root tests to assess the stationarity properties of the time series data, ensuring the reliability of subsequent econometric models. The ECM and ARDL models facilitate the examination of both short-term and long-term dynamics between FDI and economic development variables, ARDL Bound test, is applied to investigate the existence of cointegration among the variables, providing insights into the long-term equilibrium relationship. Additionally, Granger causality tests are conducted to discern the directionality of causal relationships, helping to discern whether FDI leads to economic development or vice versa. The empirical results reveal compelling insights into the relationship between FDI and economic development in Nepal. Robustness checks further validate the reliability of the findings, enhancing the overall robustness and credibility of the study's empirical results. The comprehensive analysis provides valuable insights for policymakers and stakeholders aiming to formulate targeted strategies for fostering sustainable economic development in Nepal through FDI.

The impact of urbanization and economic growth on carbon dioxide emission in sub-Saharan African countries: a perspective from the spatial-temporal approach.

Sub-Saharan Africa (SSA) is seeing exceptional urbanization and economic expansion rates. Therefore, the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) parameters and the spatial econometric framework are used in this work to examine the influence of economic growth and urbanization on SSA's CO2 emissions. Likewise, to determine the spatial effect and understand how factors influence the spatial dependence of carbon emissions, the study builds a spatial Durbin model (SDM). In line with the findings, the spatial correlation test revealed the spatial correlations across various countries. This indicates that the changes in sub-Saharan African country's CO2 emissions impacted nearby countries and the countries themselves. Additionally, the findings reveal that, in the SSA's countries, urbanization, economic growth, industrial structure, trade, and population, excluding energy intensity, which failed the significant test, all positively influence CO2 outflows, in line with the spatial econometric model's findings. Thus, energy intensity shares an adverse impact on carbon emissions. As an outcome, energy intensity reduces carbon dioxide emissions in nearby nations and the entire region. Thus, the study recommends that policymakers account for the effects of spatial spillover when establishing low-carbon policies, encouraging a low-carbon lifestyle, promoting environmentally friendly technologies, and improving regional collaboration.

Hitting the jackpot: what drives casino hotel stock returns’ predictability?

ABSTRACT Forecasting casino hotel stock prices are strategically important to key stakeholders making investment decisions. However, there is a paucity of studies that have examined the impact of macro-economic variables, sector-specific variables and firm-specific variables on casino stock returns. This research fills a research gap by examining the predictability of Australian casino hotel stock returns. The forecasting of casino hotel stock prices was modelled using econometric panel predictive regression analysis through the efficacy of 26 predictors comprising macroeconomic variables, sector-specific variables, and firm-specific variables as predictors. All eight casinos listed on the Australia Stock Exchange (ASX) were included in our sample. Our econometric panel predictive regression model found macro-economic variables with the strongest return predictability relative to the sector-specific and firm-specific variables. Our study contributes to the theoretical advancement of forecasting methodology through panel predictive regression modelling by addressing the endogeneity, persistency and heteroskedasticity of stock predictors by considering a large set of macro-economic variables, sector-specific and firm-specific variables as predictors to develop an econometric framework that suits the features of hotel financial data.

Energy poverty and shadow economy: evidence from Africa

PurposeEnergy poverty is a global phenomenon, but its prevalence is enormous in most African countries, with a potential impact on quality of life. This study aims to investigate the impact of energy poverty on the shadow economy.Design/methodology/approachThe study uses panel data from 45 countries in Africa over a period of 1996–2018. Using panel cointegrating regression and panel vector auto-regression model in the generalized method of moments technique.FindingsThis study provides that energy poverty deepens the size of the shadow economy in Africa. It also documents that there is a bidirectional causality between shadow economy and energy poverty. Therefore, the two variables can predict each other.Practical implicationsThe study suggests that lack of access to clean and modern energy services contributes to the depth of the shadow economy in Africa. African authorities are advised to strengthen rural and urban electrification initiatives by providing adequate energy infrastructure so as to reduce the level of energy poverty in the region. To ensure energy sustainability delivery, the study proposes that the creation of national and local capacities would be the most effective manner to guarantee energy accessibility and affordability. Also, priorities should be given to the local capital mobilization and energy subsidies for the energy poor. Energy literacy may also contribute to the sustainability and the usage of modern energy sources in Africa.Originality/valuePrevious studies reveal that income inequality contributes to the large size of shadow economy in developing economies. However, none of these studies analyzed the role of energy poverty and its implications for underground economic operations. Inadequate access to modern energy sources is likely to deepen the prevalence of informality in developing nations. Based on this, this study provides fresh evidence on the implications of energy deprivation on the shadow economy in Africa using a heterogeneous panel econometric framework. The study contributes to the literature by advocating that the provision of affordable modern energy sources for rural and urban settlements, and the creation of good energy infrastructure for the firms in the formal economy would not only improve the quality of life but also important to discourage underground economic operations in developing economies.

Gravity Models for Global Migration Flows: A Predictive Evaluation

This study introduces a comprehensive econometric framework based on gravity equations and designed to forecast migrant flows between countries. The model’s theoretical underpinnings are validated through empirical data, and we show that the model has better out-of-sample predictive ability than alternative global models. We explore the quantitative effects of various socioeconomic, demographic, and geographic factors on migration and illustrate its use to obtain scenario-driven projections of bilateral migration, assessing the potential contributions of migration to population and GDP dynamics in Germany and Portugal for the period 2021–2025. Our projection results highlight the critical role of immigration in sustaining population levels and economic growth, particularly in the context of ageing populations and decreasing fertility rates across Europe.