Distributed Lag Research Articles

Ambient temperature exposure and rapid infant weight gain.

Childhood obesity is a major public health concern, and the global rate is rising. Rapid infant weight gain is a risk factor for later overweight. Studies have linked prenatal ambient temperature exposure to fetal growth, and preliminary evidence suggests postnatal exposure may be associated with infant weight gain. Using a population-based historical cohort study including 1100576 infants born 2011-2019, we assessed the relationship between prenatal and one-month postnatal ambient temperature exposure and rapid infant weight gain. We used a hybrid spatiotemporal model to assess temperatures at the family's recorded residence at birth. Repeated weight measurements between birth and 15 months were used to model the outcome using the SuperImposition by Translation and Rotation (SITAR) method. We employed generalized linear models and distributed lag models to estimate the association between prenatal and postnatal exposure and rapid infant weight gain, defined as the upper tertile of the SITAR growth velocity. Overall, higher ambient temperatures were associated with rapid infant weight gain. The cumulative adjusted relative risk for the highest exposure quintile during pregnancy compared with the lowest quintile was 1.33 [95% confidence interval (CI): 1.25, 1.40], and the corresponding association for the first postnatal month was 1.19 (95% CI: 1.15, 1.23). Exposure to high ambient temperature during early and mid-pregnancy, as well as the first postnatal month, was associated with rapid weight gain, while during late pregnancy, exposure to low temperatures was associated with this outcome. Prenatal and postnatal ambient temperatures are associated with rapid infant weight gain.

Finance–growth nexus and sustainable development in West Africa

PurposeWith the rising global emphasis on sustainable development (SD) and the attendant race to attain the sustainable development goals (SDGs), this study empirically examines the effect of the finance–economic growth nexus on SD in West Africa for the period 1970 to 2020.Design/methodology/approachThe study adopted the panel autoregressive distributive lag (PARDL) and rests on the estimates of the pooled mean group (PMG) model in line with the outcome of the Hausman test.FindingsThe result indicates that financial development reinforces the positive influence of economic growth on SD in addition to the direct incremental impact they wield on SD. This suggests that financial development is one of the “influencing factors” and is positioned to potentially improve the relationship between economic growth and SD in West Africa. Findings further reveal that foreign direct investment (FDI) enhances the achievement of SD in West Africa whereas carbon dioxide (CO2) emissions and natural resource rent (NRR) are found to exert a deteriorating impact.Practical implicationsSince financial development is found to enhance the growth–SD relationship, it is crucial to pursue domesticated, inclusive and self-sustaining growth policies as well as promote financial inclusion, remove bottlenecks and inefficiencies in the financial system and adopt the principles of the 3 Rs - “reduce, reuse and recycle.”Originality/valueCompared to previous studies that examined the effect of financial development and growth on SD separately, the present study interacted both to see how financial development can influence the economic growth–SD nexus.

Non-linear effects of three core mineral resources, energy uncertainty, and inclusive digitalization on economic growth: A comparative analysis of US and China

This study examines the asymmetric effects of mineral resources (oil, coal, and gas), digitalization, and energy uncertainty on economic growth in China and the USA. Using quarterly data from 2000 to 2022, we employ the nonlinear autoregressive distributed lag (NARDL) model to analyze these relationships. The study begins by conducting unit root tests, cointegration analysis, and asymmetry tests to establish robust connections between the variables. The long- and short-run results confirm the presence of asymmetry, as positive and negative shocks in oil rents, digitalization, and energy uncertainty affect economic growth differently. Notably, positive shocks in oil rents and digitalization favour economic growth, indicating that increases in oil rents and digital inclusion contribute to economic development. Conversely, positive shocks in energy uncertainty tend to reduce economic growth. Interestingly, the findings for China and the USA show similar asymmetric patterns; coal and natural gas rents positively influence economic growth, while the control variables suggest that capital and labour increase contribute to both countries' development. The error correction terms, 0.731 (73.1%) for China and 0.625 (62.5%) for the USA, indicate the annual rate at which asymmetric discrepancies are corrected. All diagnostic tests confirm the suitability of the NARDL model for this study. Finally, the study reveals significant bi-directional causality among all pairs of variables.

STRUCTURAL AND STOCHASTIC BEHAVIOR OF MEXICAN AGRICULTURAL EXPORTS

Mexican agricultural exports have grown faster than the country’s economy, suggesting a foreign exchange inflow and the use of production factors in Mexico. This work examines both the structural behavior of the agricultural exports and its stochastic behavior using data from 1993 to 2023. The structural part is modeled with three explanatory behaviors: first, the influence in exports of the U.S. economy through its Gross Domestic Product (GDP); second, the effect of the Mexican economy through its own GDP; and third, the search for a more profitable price abroad was modeled with the real exchange rate. On the other hand, the stochastic behavior was modeled with lags of the actual exports and other explanatory variables. After adjusting an autoregressive distributed lag model and verifying the existence of a long-term relationship between Mexican agricultural exports, U.S. GDP, Mexican GDP, and the real exchange rate, it was found that the behavior of the export growth is largely explained by the U.S. economy.

Influence of Health Insurance Financing on Economic Growth In Kenya

Economic growth positively impacts population health, providing access to quality healthcare, environmental protection, clean water, and better preventative behavior. However, Kenya's declining growth rate affects health financing, causing many citizens to struggle with access to care due to rising medical costs. This study sought to analyse the influence of health insurance financing on economic growth in kenya. The study was guided by the Solow-Swan Exogenous Growth Model. The study was conducted in Kenya. Data used in study was longitudinal data for twenty two years. The study period was from 2000 to 2022. A descriptive and inferential analysis performed where Auto-Regressive Distributive Lag (ARDL) model was used. Analyzed data were presented in the form of tables and discussions. The health insurance financing indicated a positive and statistically significant (b=0.2935; p = 0.0280.05), implying that one unit increase in health insurance financing could result in 0.2935 units in economic growth in Kenya. The study showed that adjusted R-squared was 0.9271, implying that the model explains approximately 92.71% of the variation in the economic growth in Kenya. The study concluded that, health insurance financing, is positively related to economic growth in the long run in Kenya. The study recommends that there is need for enhancing health insurance financing sources and gradually shifting towards sustainable domestic financing sources.

The Relationship Between Interest Rates and Agricultural Commodity Price Dynamics

ABSTRACTThe U.S. Federal Reserve has undertaken several interest rate interventions in the past decade. This study explores the relationship between U.S. corn and soybean prices and Federal Reserve monetary policy interventions, in the short and long run. We use a nonlinear autoregressive distributed lag model to capture three key features of agricultural commodity price responses to changes in interest rates. First, we observe that corn and soybean prices initially overreact to changes in interest rates, overshooting their long‐run equilibrium before gradually reverting. Second, in the short run, the relationship between soybean prices and interest rates is asymmetric; the initial price change is more pronounced for an interest rate increase than for a decrease of the same magnitude. Lastly, both corn and soybean prices display symmetry in the long run. These results help to understand how changes in interest rates affect commodity prices. Assessing the relationship between agricultural commodity prices and interest rates can assist farmers and processors in planning investments and loans based on interest rate forecasts.

How Do Greenhouse Gases and Other Factors Affect Rice Production in Bangladesh? An Insight from Autoregressive Distributed Lag Approach

Purpose: The study aims to elucidate the impact of greenhouse gases and other factors on rice production in Bangladesh. By considering the influences of greenhouse gases, technological factors, and industrial activities, the research unveils significant insights into the long-term and short-term effects on rice production. Methodology: This research employs the Autoregressive Distributed Lag (ARDL) approach to investigate the impact of carbon dioxide, nitrous oxide, agricultural land, agriculture as percentage share of GDP, and industry on rice production in Bangladesh through EViews-12 Software. Findings: Our findings suggest that CO2 emissions, industrial and agricultural share as percentage of GDP exhibit a negative impact, decreasing rice production, while N2O and agricultural land have a notable positive effect. Technical factors such as the area under rice cultivation, agriculture and industry are identified as pivotal contributors to rice production. Practical Implications: Finally, the study concludes with policy implications and recommendations based on our findings for sustainable agricultural practices, technological advancements, and policy interventions tailored to enhance rice production and secure food sustainability in the unique agricultural landscape of Bangladesh. Originality/Value: As per as our knowledge, no research has been carried out in Bangladesh to investigate the impact of greenhouse gases and selected factors on rice production. Research Limitations: Due to difficulties in data collection procedure, we could not include all variables of greenhouse gases. We expect in future, there will be more data sources which would help many researchers to contribute to rice production related researches.

The relationship of short-term exposure to meteorological factors on diabetes mellitus mortality risk in Hefei, China: a time series analysis.

The study aims to explore whether short-term exposure to meteorological factors has a potential association with the risk of diabetes mellitus (DM) mortality. During the period 2015-2018, we collected daily data on meteorological factors and deaths of diabetic patients in Hefei. A total of 1101 diabetic deaths were recorded. We used structural equation modeling to initially explore the relationships among air pollutants, meteorological variables, and mortality, and generalized additive modeling (GAM) and distributional lag nonlinear modeling (DLNM) to explore the relationship between meteorological factors and the mortality risk of DM patients. We also stratified by age and gender. The mortality risk in diabetic patients was expressed by relative risks (RR) and 95% confidence intervals (CI) for both single and cumulative days. Single-day lagged results showed a high relative humidity (RH) (75th percentile, 83.71%), a fairly high average temperature (T mean) (95th percentile, 30.32°C), and an extremely low diurnal temperature range (DTR) (5th percentile, 3.13°C) were positively related to the mortality risk of DM. Stratified results showed that high and very high levels of T mean were significantly positively linked to the mortality risk of DM among females and the elderly, while very high levels of DTR were linked to the mortality risk in men and younger populations. In conclusion, this study found that short-duration exposure to quite high T mean, high RH, and very low DTR were significantly positively related to the mortality risk of DM patients. For women and older individuals, exposure to high and very high T mean environments should be minimized. Men and young adults should be aware of daily temperature changes.

Policy implications of renewable energy technology, information and communication technology, and circular economy on employment levels

Employment level is vital in measuring an economy's overall health. Thus, economists always search for factors that can promote employment. However, most past studies have considered traditional economic factors as determinants of employment, while ignoring the novel factors such as renewable energy technology, information and communication technology (ICT), and circular economy. To plug this loophole in the literature, the current analysis intends to investigate the influence of renewable energy technology, ICT, and circular economy on employment. We employ the cross-sectionally augmented autoregressive distributed lag (CS-ARDL) framework for empirical investigation of the analysis. In the long-run, renewable energy technology, ICT, and circular economy foster the level of employment. In addition, human capital promotes the level of employment, while inflation lowers the level of employment in long run. In the short run, only human capital favorably influences employment. Policymakers must use renewable energy technology, ICT, and circular economy as policy options to promote employment levels.

Asymmetric Effects of Trade Openness, Financial Development, and Human Capital on Economic Growth in South Asia

ABSTRACT This study uses the nonlinear autoregressive distributed lag (NARDL) model to explore the relationship between trade openness and output growth in South Asia from 1980 to 2019. Findings reveal significant short- and long-term effects, with asymmetric dynamics. In linear ARDL models, Pakistan and India show strong positive correlations, while Bangladesh shows a negative one. Non-linear modeling reduces these effects, but Nepal still benefits. Sri Lanka’s linear model indicates a non-significant negative correlation, while the non-linear model reveals a positive relationship. These results highlight the complex dynamics between trade openness and economic growth in South Asia.

Has competition law enforcement been effective in South Africa?

ABSTRACT The South African Competition Act was crafted on the basis that it could be used to address distortions and inequalities in the market through enforcing competition. A question which has arisen is whether competition enforcement has been effective given that many economic sectors are still largely dominated by monopolies, duopolies or oligopolies. In this study, I assess whether competition enforcement has reduced inequality in South Africa by studying the link between market power and inequality, following a competition intervention in selected product markets. Using the nonlinear autoregressive distributed lag model, I assess whether competition enforcement in the market for bread, maize meal, and sunflower oil, has been effective in increasing consumer welfare, by lowering income inequality through lower prices. The findings of this study suggest that firms in these markets abuse their market power even post-intervention, such that they can raise prices and reduce consumer welfare, increasing inequality indirectly.

Impact of energy consumption on life expectancy in lower-middle-income West African countries

Life expectancy is a key determinant of a nation’s health and well-being. However, West African countries face several challenges in improving life expectancy, such as inadequate healthcare infrastructure, corruption, poor sanitation, and environmental quality due to environmental degradation. In addition, energy accessibility is an important determinant of health outcomes and environmental quality. The United Nations’ Sustainable Development Goals 7, 12, 13, 14, and 15 are directly linked with energy access; achieving these goals will lead to an improvement in energy access. Therefore, in this study, using data obtained from the World Bank and the Central Bank of Nigeria Statistical Bulletin, we investigated the impact of energy consumption and corruption on life expectancy in lower-middle-income West African countries from 1990 to 2021. The results of the cross-sectional auto-regressive distributed lag technique showed that renewable energy has a positive and significant impact on life expectancy in lower-middle-income West African countries in both the short and long run. However, non-renewable energy was found to have a significant and negative impact on life expectancy in the long run and a negative but non-significant impact in the short run. Corruption had a debilitating effect on life expectancy in both the short and long run. We recommend that policymakers should make a conscious and deliberate push toward transitioning to renewable energy through public–private partnerships to provide affordable and clean energy while tackling corruption.

Enhancing environmental quality and economic growth through potential effects of energy efficiency and renewable energy in Asian economies

This study examines the potential impacts of energy efficiency and renewable energy on economic growth proxies by gross domestic product and environmental quality proxies by carbon dioxide emissions across eight selected Asian countries from 2000 to 2020. This study contributes by calculating green total factor productivity and carbon total factor productivity based on the famous Solow’s residual via employing a modified extensive growth accounting model that internalized ignored factors such as energy efficiency and renewable energy. The employed panel cointegration techniques confirm that all variables are co-integrated with carbon dioxide emissions and economic growth. The pooled mean group/autoregressive distributed lag model analysis results indicate that energy efficiency is positively associated with both environmental quality and economic growth. Renewable energy hurts economic growth but has a positive effect on environmental quality which suggests the necessity of implementing an effective strategy for renewable energy alongside energy efficiency measures to enhance economic growth and environmental quality in the selected Asian countries. The findings from the fully modified ordinary least squares estimator are consistent with the environmental quality model. The average growth rate of green total factor productivity is positive despite negative contributions from energy efficiency and renewable energy. Similarly, the average growth rate of carbon total factor productivity is negative despite positive contributions from labor and capital. This discrepancy may be attributed to the beneficial effects of labor and capital as input productivity-driven. Embracing renewable energy sources can take significant steps toward improving environmental quality for future generations. Focusing on green technologies that enhance energy efficiency can substantially promote environmental quality and stimulate sustainable economic growth through innovation and climate change integration to achieve Sustainable Development Goals.

Analysis of Capital Market Development and Economic Growth in Nigeria

This study examined the analysis of capital market development and economic growth in Nigeria between 1986 and 2022. As a result, the real gross domestic product was used as the dependent variable while independent variables in the study included market capitalisation, all share index, value of transactions, total number of new issues and exchange rate. The Auto-Regressive Distributed Lag Modelling (ARDL) technique as well as the Engle Granger causality test were used to analyse the data obtained from the CBN statistical bulletin. The results emanating from the analysis reported that the total number of new issues had a positive impact on economic growth while other variables had insignificant effects on economic growth. Furthermore, the causality results revealed that all the stock market variables had a uni-directional causality running from each of them to economic growth. Therefore, it is recommended that new issues in the capital market should be seriously regulated as they can affect economic growth in a significant manner.

Study of the driving factors of the abnormal influenza A (H3N2) epidemic in 2022 and early predictions in Xiamen, China

BackgroundInfluenza outbreaks have occurred frequently these years, especially in the summer of 2022 when the number of influenza cases in southern provinces of China increased abnormally. However, the exact evidence of the driving factors involved in the prodrome period is unclear, posing great difficulties for early and accurate prediction in practical work.MethodsIn order to avoid the serious interference of strict prevention and control measures on the analysis of influenza influencing factors during the COVID-19 epidemic period, only the impact of meteorological and air quality factors on influenza A (H3N2) in Xiamen during the non coronavirus disease 2019 (COVID-19) period (2013/01/01-202/01/24) was analyzed using the distribution lag non-linear model. Phylogenetic analysis of influenza A (H3N2) during 2013–2022 was also performed. Influenza A (H3N2) was predicted through a random forest and long short-term memory (RF-LSTM) model via actual and forecasted meteorological and influenza A (H3N2) values.ResultsTwenty nine thousand four hundred thirty five influenza cases were reported in 2022, accounting for 58.54% of the total cases during 2013–2022. A (H3N2) dominated the 2022 summer epidemic season, accounting for 95.60%. The influenza cases in the summer of 2022 accounted for 83.72% of the year and 49.02% of all influenza reported from 2013 to 2022. Among them, the A (H3N2) cases in the summer of 2022 accounted for 83.90% of all A (H3N2) reported from 2013 to 2022. Daily precipitation(20–50 mm), relative humidity (70–78%), low (≤ 3 h) and high (≥ 7 h) sunshine duration, air temperature (≤ 21 °C) and O3 concentration (≤ 30 µg/m3, > 85 µg/m3) had significant cumulative effects on influenza A (H3N2) during the non-COVID-19 period. The daily values of PRE, RHU, SSD, and TEM in the prodrome period of the abnormal influenza A (H3N2) epidemic (19–22 weeks) in the summer of 2022 were significantly different from the average values of the same period from 2013 to 2019 (P < 0.05). The minimum RHU value was 70.5%, the lowest TEM value was 16.0 °C, and there was no sunlight exposure for 9 consecutive days. The highest O3 concentration reached 164 µg/m3. The range of these factors were consistent with the risk factor range of A (H3N2). The common influenza A (H3N2) variant genotype in 2022 was 3 C.2a1b.2a.1a. It was more accurate to predict influenza A (H3N2) with meteorological forecast values than with actual values only.ConclusionThe extreme weather conditions of sustained low temperature and wet rain may have been important driving factors for the abnormal influenza A (H3N2) epidemic. A low vaccination rate, new mutated strains, and insufficient immune barriers formed by natural infections may have exacerbated this epidemic. Meteorological forecast values can aid in the early prediction of influenza outbreaks. This study can help relevant departments prepare for influenza outbreaks during extreme weather, provide a scientific basis for prevention strategies and risk warnings, better adapt to climate change, and improve public health.

Critical windows for exposure to chemical composition of ambient particulate matter and human semen quality decline

BackgroundCritical windows for exposure to chemical components of particulate matter (PM <2.5 μm in diameter [PM2.5]) associated with the human semen quality decline remain unclear. ObjectivesTo address this gap, we developed a new analytical framework by integrating a Linear Mixed Model (LMM) with subject- and center-specific intercepts and a Distributed Lag Model (DLM) to fully account for correlations between finely vulnerable exposure windows based on complete profile of the spermatogenesis cycle. MethodsWe constructed a multicenter cohort involving 33,234 sperm donors with 78,952 semen samples covering 6 representative regions across China from 2014 to 2020 to investigate the week-scale critical windows for the exposure. Daily exposure to PM2.5 chemical components of donors was derived from grid data based on 1-km spatial resolution surface measurements. ResultsDecreased sperm count was significantly associated with NO3− and SO42− at 9–10 weeks (e.g., β: -0.05 %, 95%CI: [−0.10 %, −0.00 %] at the 9th week) and 0–2 weeks (e.g., β: -0.66 %, 95%CI: [−1.24 %, −0.07 %] at the 1st week), respectively. Critical windows of progressive motility decline were 0–10 weeks for BC (e.g., β: -0.07 %, 95%CI: [−0.11 %, −0.03 %] at the 5th week), Cl− at 1–4 weeks (e.g., β: -2.21 %, 95%CI: [−3.77 %, −0.66 %] at the 2nd week), 0–6 weeks and 9–10 weeks for NO3− (e.g., β: -0.05 %, 95%CI: [−0.09 %, −0.01 %] at the 4th week), 1–3 weeks and the 8th week for NH4+ (e.g., β: -0.06 %, 95%CI: [−0.11 %, −0.01 %] at the 2nd week). Total motility is significantly negatively associated with BC at entire windows, Cl− at 0–3 weeks, the 5th week and 9–10 weeks. ConclusionsThere are week-scale vulnerable windows of exposure to PM2.5 chemical components for human semen quality. This highlights the need for more targeted pollution control strategies addressing PM2.5 and its chemical components.

Do Supply Chain Pressures Affect Consumer Prices in Major Economies? New Evidence from Time-Varying Causality Analysis

This study explores the dynamic causality between the consumer price index and supply chain pressure index in two large economies. Despite extensive research into the role of supply chains at the firm level, literature on their impact on time-varying inflation and macroeconomic stability is scarce. Using data from the United States and the European Union from January 2000 to December 2021, we employ novel time-varying causality techniques to examine the evolving relationships among inflation, supply chains, real gross domestic product, interest rates, and oil prices. Our findings show that supply chains have a significant dynamic relationship with consumer prices, particularly during economic crises like the 2008 global financial crisis and the COVID-19 pandemic. Robustness tests including impulse response functions and the autoregressive distributed lag model confirm this causality. These results are critical for policymakers, emphasizing the importance of supply chains in controlling inflation and achieving global economic resilience and stability.

Effects of outdoor air pollution on hospital admissions from cardiovascular diseases in the capital city of Mongolia

Cardiovascular diseases (CVD) are one of the primary causes of morbidity worldwide, while ambient air pollution (AAP) is a major contributor to CVD. This study is the first study that aimed to compare the differences in AAP concentrations and acute effects on the risk of admissions from CVD, ischaemic heart diseases (IHD), and stroke before and after introducing the new type of coal briquette in Ulaanbaatar, Mongolia. We collected daily hospital admission records, air quality and meteorology data between January 1, 2016 and December 31, 2022. The relative risks (RRs) of cause-specific hospitalisations associated with daily concentrations of AAP were estimated by a time-stratified case-crossover analysis with conditional Poisson distributed lag models. Overall, acute exposure to the pollutants, except for O3, was positively associated with an increased risk of all admissions. For each interquartile range (IQR) increase in PM2.5, PM10, SO2, NO2 and CO, the risk of CVD admissions increased by 0.5% (RR=1.005; 95% CI: 0.998, 1.012) at lag0, 2.9% (RR=1.029; 95% CI: 1.014, 1.044), 2.5% (RR=1.025; 95% CI: 1.010, 1.040), 4.1% (RR=1.041; 95% CI: 1.016, 1.068) and 0.3% (RR=1.003; 95% CI: 0.995, 1.012) at lag01, respectively. Subgroup analyses showed greater risks of CVD admissions for women and people aged <65 years, in the cold season, and after the raw coal ban period. During the cold winter season, Ulaanbaatar’s AAP levels remained higher than the World Health Organization’s guidelines, regardless of consuming the new coal briquettes. Adopting alternative efficient methods to improve air quality and associated health outcomes is necessary.

The Tunisian Central Bank’s Efficiency Benchmarks: A Dynamic Approach

The topic of banking performance and efficiency has received considerable attention, yet there is a noticeable gap in examining the performance and efficiency of central banks. Moreover, there is a scarcity of studies on central bank efficiency. This paper aims to investigate the factors that influenced the efficiency of the Central Bank of Tunisia from 2000 to 2020, using an Autoregressive Distributed Lag Model. The appropriate econometric methodology was first established to achieve this goal, after which the model's results were presented and interpreted.&amp;nbsp;The study's findings indicate that the Tunisian Central Bank’s efficiency is influenced by several macroeconomic (inflation, public deficit, growth rate), international (exchange rate, foreign debt), and political variables (political and government instability, conflict of interest), each with a varying degree of impact. Interestingly, the transition of the Central Bank of Tunisia from a dependent to an independent institution in 2016 did not yield a notable change in efficiency.Copyright© 2024 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.

Is climate change hindering the economic progress of Nigerian economy? Insights from dynamic models

This study investigates the link between climate change and economic growth in Nigeria from 1961 to 2022. To provide a robust analysis that facilitates a nuanced examination of this dynamic relationship, this study employs state-of-the-art econometric approaches, including autoregressive distributed lag (ARDL), fully modified least squares (FMOLS), novel quantile autoregressive distributed lag (QARDL), and time-varying causality. The empirical results of this study are as follows: (1) the impact of climate change on economic growth is not felt in the short run. However, climate change negatively influences economic growth in Nigeria in the long run, (2) the elasticity of climate change increases across the conditional quantile economic growth, (3) unidirectional causality from climate change to economic growth across different time dimensions. These empirical outcomes advocate for a proactive and adaptive policy framework, emphasising the need for the Nigerian government to adopt climate-smart policies.