Variance Decomposition Analysis Research Articles

Development strategies for reducing infant mortality: A focus on healthcare infrastructure and policy in emerging Asian countries

The journey towards better healthcare sustainability in Asian nations demands a comprehensive investigation into the impact of urban governance, poverty, and female literacy on infant mortality rates. This study undertakes a rigorous exploration of these key factors to pave the way for evidence-based policy interventions, utilizing data from a panel of six selected Asian countries: Pakistan, China, India, Indonesia, Malaysia, and the Philippines, spanning the years 2001 to 2020. The findings reveal that adequate sanitation facilities, higher female literacy rates, and sustained economic growth contribute to a reduction in infant mortality. Conversely, increased poverty levels and limited women’s autonomy exacerbate the infant mortality rates observed in these countries. The Granger causality analysis validates the reciprocal relationship between urban sanitation (and poverty) and infant mortality rates. Furthermore, the study establishes a causal relationship where female literacy rates Granger-cause infant mortality rates, and conversely, infant mortality rates Granger-cause women’s autonomy in these countries. The variance decomposition analysis indicates that sustained economic growth, improved female literacy rates, and enhanced women’s empowerment will likely impact infant mortality rates in the coming decade. Consequently, in low-income regions where numerous children face potentially hazardous circumstances, it is imperative to allocate resources towards establishing and maintaining accessible fundamental knowledge regarding sanitation services, as this will aid in reducing infant mortality rates.

Spatial variation in soil aggregate stability and erodibility at different slope positions in four hilly regions of northeast China

The spatial heterogeneity of erosion can affect soil properties in agricultural areas, which can in turn alter the soil aggregate stability and erodibility. However, it remains elusive how aggregate stability and soil erodibility vary at different positions in the Mollisol region of northeast China, and how soil properties drive these variations. This study selected different slope positions across a latitudinal gradient in four hilly regions from cold temperate (Jiusan, JS; Keshan, KS) to middle temperate (Hailun, HL; Binxian, BX) areas in northeast China. The spatial variations of soil properties, geometric mean diameter (GMD), mean weight diameter (MWD), soil erodibility (K), and soil structural stability index (SSI) were calculated at topsoils (0–20 cm) and subsoils (20–40 cm). Total nitrogen (TN) and soil organic carbon (SOC) decreased with increasing latitudes, and gradually declined and finally rose with decreasing slope positions. MWD, GMD, and SSI were the highest in JS, and first obviously decreased from the top position to lower position, and then increased at the bottom position. The K followed an order of BX > HL > KS > JS, and decreased in the bottom position compared with that in the middle and lower positions across the four regions, which could be ascribed to the new aggregate formation promoted by fine soil particle deposition at the slope bottom. Redundancy analysis and variance decomposition analysis indicated that SOC, TN, and bulk density had higher vector values, and region (interpretation rate = 57.97 %) had the greatest influence on aggregate stability and soil erodibility. Available potassium (AK), saturated conductivity (KS), SOC, TN and soil texture were the main factors driving the variation of MWD and K. These findings highlight the spatial variations in soil aggregate stability and erodibility in Mollisol regions, providing a scientific basis for soil restoration in eroded farmland.

Does monetary policy impact income inequality?

PurposeThis article analyzes the impact of monetary policy on income inequality across 16 advanced economies. The author investigates three key points: (1) the relationship between domestic monetary policy and domestic income inequality, (2) the spillover effect of USA monetary policy (including quantitative easing) on international inequality and (3) the quantitative influence of the monetary policies of both the USA and the Eurozone on the formation of domestic income inequalities.Design/methodology/approachThe author employed the Global Vector Autoregressive (GVAR) model, which uses Vector Autoregressive with Exogenous Variables (VARXs) models of each economy to build an integrated system that enables us to evaluate individual responses to global shocks.FindingsThe author's analysis reveals that (1) contractionary monetary policy exacerbates domestic inequality and (2) USA monetary policy, including quantitative easing, affects international inequality. Furthermore, the author's variance decomposition analysis highlights that USA monetary policy is especially influential on income inequality in Norway and Sweden. Additionally, the cointegrating analysis confirms that monetary policy's impact on inequality persists in the long term.Originality/valueMost of the studies focused on investigating domestic economies as closed economies. However, the author's approach differs in that the author uses the GVAR, which treats all economies as open. This allows us to incorporate the world economy into the domestic dynamics and connect the economies using bilateral trade. Another advantage of the GVAR is that it captures spillover effects by modeling each economy and constructing the international economy.

Financial development, energy consumption, and economic growth in the ASEAN countries: evidence from the PVAR approach.

The Association of Southeast Asian Nations (ASEAN), a new global economic force, has struggled to achieve a dual objective of enhanced economic growth and improved environmental quality. Financial development is generally considered an important tool in achieving these simultaneous objectives. This paper examines the response mechanism and the inter-relationship between the ASEAN region's financial development, energy consumption, and economic growth. Unlike previous studies, the paper uses a generalized method of moments panel vector autoregression (GMM-panel VAR) framework for the 1981-2021 period. The second-generation Granger causality test is used to identify their causality relationship. Economic growth supports financial development and reduces energy consumption from fossil fuel sources in the ASEAN countries. Financial development also reduces fossil fuel energy consumption. The bi-directional relationship between economic growth and fossil fuel energy consumption exists. Energy consumption and financial development are also bi-directionally linked. However, only the unidirectional Granger causality from economic growth to financial development is found. The variance decomposition analysis results confirm that economic growth accounts for the most significant variance in fossil fuel energy consumption and financial development in the ASEAN countries. Policy implications have emerged based on these findings.

THE EFFECT OF SELECTED MACROECONOMIC VARIABLES ON THE HOUSING PRICE INDEX IN TURKEY

This study investigates how the house price is affected by macroeconomic variables such as interest rate, exchange rate, money supply and inflation rate. Housing supply and housing demand determines house price. On the one hand, housing supply dimensions covers contruction cost, existing land, housing stock. On the other hand, housing demand dimension involves interest rate, consumer price index as the indicator of inflation, income, population growth and employment rate. In order to measure the effects of macroeconomic variables more accurately, the housing price index variable determined by the Central Bank was used. Depending upon monthly data for the period 2010-2023 in Turkey, VAR models are used. In this way it is intended to find the direction and degree of the effect of selected macroeconomic variables on the house price index by performing impulse response function and variance decomposition analysis. According to this analysis findings, interest rate and exchange rate had a significant effect on the house price index. In line with the results obtained after the variance decomposition analysis, it was determined that the interest rate had a 19% effect and the exchange rate had a 14% effect on the house price index at the end of the 10th period.

MACROECONOMIC EFFECTS OF GLOBAL UNCERTAINTY IN THE TURKISH ECONOMY

Purpose- In this study, it is aimed to investigate the effects of global uncertainty shocks on the macroeconomic variables of the Turkish economy. Methodology- In the study, the global world uncertainty index (WUI) constructed by Ahir et al. (2022) is used as an indicator of global uncertainty. A structural auto regressive (SVAR) model is established, which includes five variables (global uncertainty indicator, economic growth rate, inflation rate, unemployment rate, and interest rate). In addition, three real variables representing economic activity (real consumption, real investment and real bank loans) are substituted for unemployment rate in the model, respectively. The data set covers the period 2003Q1-2020Q1. Findings- According to the findings of impulse response and variance decomposition analyses, real GDP growth, real consumption growth, real investment growth and real credit growth react negatively and significantly to an increase in global uncertainty. On the other hand, inflation rate, unemployment rate and interest rate respond positively and significantly to an increase in global uncertainty. These results prove that a global uncertainty shock creates the similar effects of a negative supply shock on the Turkish economy. In addition, the robustness of the findings is checked using the global economic policy uncertainty (GEPU) index developed by Baker et al. (2016). Conclusion- In the light of these findings, it can be suggested that the negative effects of global uncertainty shocks on domestic macroeconomic indicators should be mitigated through monetary and fiscal policies. Keywords: Global uncertainty, structural VAR, economic growth, inflation, unemployment JEL Codes: C32, E30, F40

The impact of government budget on national financial security: a case study in Vietnam

ABSTRACT This paper aims to examine the effect of the budget balance on national financial security in the linkage of macroeconomic variables by using the VAR model approach from 1995 to 2020 in Vietnam. Moreover, the methods of Impulse Response Function (IRF) and variance decomposition (FEDV) analysis show the impact of budget balance on the financial security situation in the short run. The results indicate that budget balance has a positive effect in the short term and explains about 2.04% of Vietnam’s financial security index. Several policy implications are suggested to enhance the financial stability in Vietnam during the Covid-19 pandemic.

The Relationship between the Share of Renewable Energy in Total Energy Consumption and Economic Growth: Kazakhstan and Turkiye Comparision

This study examines the relationship between the share of renewable energy sources in total energy consumption and economic growth by comparing the data of Kazakhstan and Turkiye using the annual data for the 1991-2019 period. We collected the data from the World Bank website (https://data.worldbank.org/indicator/). Impulse - Response Analysis and Variance Decomposition Analysis were applied to the data using the Vector Autoregressive (VAR) model. Research findings showed that although there is a relationship between renewable energy consumption and economic growth for Kazakhstan, this is not valid for Turkiye. The causality of this relationship, which is valid for Kazakhstan, is determined by the Granger causality analysis. According to the analysis, renewable energy consumption affects Kazakhstan's economic growth, but this effect does not translate into a causal relationship. Again, for both countries, we see that the impact of renewable energy shocks on economic growth remained limited and faded in a few periods. Another important finding is that the share of renewable energy consumption in the economic growth variance decomposition is higher for Kazakhstan.

Examining the Dynamics of Labour Force Participation, Carbon Dioxide Emission and Population Health in Sub-Saharan Africa

This study examined the dynamic causal relationship between population health, carbon dioxide (CO2) emission and labour force participation. The study employed the block exogeneity wald test after estimating a panel VAR model using data for 24 sub-Saharan African countries from 1990 to 2018. The dynamic interactions among the variables and effect of shocks was also examined using the variance decomposition analysis and impulse response functions. For robustness, the estimation was done using both aggregate labour force participation and disaggregated labour force participation inorder to investigate the differentials by gender. The results showed evidence of a feedback effect between life expectancy and total labour force participation. This also holds for male labour force participation however, a unidirectional causality was found from female labour force participation to life expectancy. A one standard deviation shock in CO2 emission had a negative impact on life expectancy. Life expectancy also had the greatest contribution to variations in CO2 emission. Male labour force participation shocks had a consistently negative impact over the ten-year period, however, shocks from female labour force participation had the least impacts on CO2 emission. Environmental reforms encouraging less polluting energy and technology use is important for CO2 emission reduction, which further increases life expectancy and ultimately labour force participation rates.

Study on the Relationship between Hang Seng Index and FTSE CHI Index in Hong Kong: Evidence from VAR Model

The stock index is very important to study the overall trend of the development of financial markets and the development of the stock industry. This paper mainly studies the interaction between stock indexes and the influence of the main fields included in the stock index on the index and related indexes. In this paper, Hong Kong Hang Seng Index and FTSE CHI index were selected for research, and impulse response analysis and variance decomposition analysis were carried out on the data through VAR model. By analyzing the results, the following conclusions can be drawn: First, the model is convergent. Second, the influence from one market to another market persist for several periods. Third, with the increase of lag period, FTSE CHI HK Index was influenced by HK Hang Seng Index and finally stabilized at about 96.1906% and HK Hang Seng Index was influenced by FTSE CHI HK Index and finally stabilized at about 93.995%.

Detection of Interaction Effects in a Nonparametric Concurrent Regression Model.

Many methods have been developed to study nonparametric function-on-function regression models. Nevertheless, there is a lack of model selection approach to the regression function as a functional function with functional covariate inputs. To study interaction effects among these functional covariates, in this article, we first construct a tensor product space of reproducing kernel Hilbert spaces and build an analysis of variance (ANOVA) decomposition of the tensor product space. We then use a model selection method with the L1 criterion to estimate the functional function with functional covariate inputs and detect interaction effects among the functional covariates. The proposed method is evaluated using simulations and stroke rehabilitation data.

Beyond climate change: Examining the role of environmental justice, agricultural mechanization, and social expenditures in alleviating rural poverty

Extreme weather events and extreme poverty are two sides of the same coin, with far-reaching consequences for emerging nations like Pakistan. Rural people are more likely to experience poverty and inequality as climate change worsens. This research aspires to close the gap between environmental ethics and justice by investigating how climate change issues contribute to poverty in Pakistan. The study used Robust Least Squares (RLS) regression to analyze the impact of water scarcity, extreme temperatures, and excessive rainfall on rural poverty in Pakistan from 1990Q1 to 2022Q4. Further, the study examines the effect of environmental justice interventions, access to healthcare and education, agricultural value-added and agricultural mechanization on the country's rural poverty. Results reveal that climate change contributes to rural poverty in Pakistan, while environmental justice initiatives, healthcare access, and agricultural automation alleviate poverty incidence. The Impulse Response Function (IRF) estimates suggested that rural poverty will be exacerbated over the next decade by water scarcity, high temperatures, and low agricultural value added but alleviated by excessive rainfall, environmental justice intervention, healthcare access, and agricultural mechanization. According to Variance Decomposition Analysis (VDA) projections, agricultural value added will substantially impact rural poverty by 2032, increasing it by 11.431%. Addressing these problems requires policymakers to prioritize the interests of the most marginalized groups by fostering fair results. Policies should cut GHG emissions and encourage sustainable development to combat climate change. Modernizing farming techniques and expanding access to healthcare are also necessary for increasing efficiency and production. It is essential to execute environmental justice interventions so that all communities have access to environmental resources and protections equitably. Promoting equitable outcomes and reducing poverty in Pakistan's climate change context may be achieved by closing the gap between environmental ethics and justice.

The Global Factors Driving Common Inflation In ASEAN

Purpose: This study investigates the comovements of inflation across ASEAN countries and identifies the global factors driving the common inflation dynamics of the region. Design/methodology/approach: The study uses a two-step approach. The dynamic latent factor model is first employed to estimate the common inflation of the region in the period between 2000Q1 and 2022Q2. In the second step, a bound test approach to autoregressive distributed lag (ARDL) methodology is employed to estimate the model of common inflation, which is constructed based on the aggregate supply - aggregate demand (AS-AD) framework. Findings: The study finds a common factor of inflation across ASEAN countries. The analysis of variance decomposition indicates the significant role of common inflation in the variations of individual countries' inflation. World economic growth, oil price change, non-fuel commodity price change, and trade openness are the main drivers of ASEAN common inflation in both short and long terms. Research limitations/implications: The study focuses mainly on supply-side factors, although some demand-side factors are involved. Future studies should pay more concerns on the trade linkages among regional countries and the similarities in goals in the region. Originality/value: The study examines the common inflation component across ASEAN countries, which has not been sufficiently investigated. It also evaluates the drivers at the global level of common inflation, which has limited empirical evidence.

Navigating Economic Uncertainty: Exploring The Impact Of Reduced Bank Credit On The Construction Industry

Purpose: This paper aims to analyze credit in the Indonesian banking construction sector empirically. Specifically, we examine whether economic conditions affect construction sector credit and how the response and diversity of construction sector credit in the face of economic shocks. Methodology/approach: This article use monthly data on Indonesian banking credit from 2004 to 2022 using the Vector Error Correction Model (VECM). Findings: The long-term effects of the economy on credit are shown by the results of the VECM research. The Impulse Response Function (IRF) results indicate that credit in the construction industry has both positive and negative effects. Forecast Error Variance Decomposition (FEVD) analysis shows that gross domestic product, crises, industrial price index, interest rates, exchange rates, and inflation affect credit diversity. Practical implications: Banking credit is an essential component in meeting company needs. Banks need to consider several things in the distribution of credit to the construction sector, especially those that impact the long term. Originality/value: This research provides a better understanding of how the construction sector credit can be affected by changing economic conditions, and how diverse credit responses and policies are in the face of such shocks.

An integrative data-driven approach for monitoring corn biomass under irrigation water and nitrogen levels based on UAV-based imagery.

Unmanned aerial vehicle (UAV)-based remote sensing has been widely considered recently in field scale crop yield estimation. In this research, the capability of 13 spectral indices in the form of 5 groups was studied under different irrigation water and N fertilizer managements in terms of corn biomass monitoring and estimation. Farm experiments were conducted at Urmia University, Iran. The research was done using a randomized complete block design at three levels of 60, 80, and 100% of irrigation water and nitrogen requirements during four replications. The aerial imagery operations were performed using a fixed-wing UAV equipped with a Sequoia sensor during three plant growth stages including stem elongation, flowering, and silking. The effect of different irrigation water and nitrogen levels on vegetation indices and crop biomass was examined using variance decomposition analysis. Then, the correlation of the vegetation indices with corn biomass was evaluated by fitting linear regression models. Based on the obtained results, the indices based on near infrared (NIR) and red-edge spectral bands showed a better performance. Thus, the MERIS terrestrial chlorophyll index (MTCI) indicated the highest accuracy at estimating corn biomass during the growing season with the R2 and RMSE values of 0.92 and 8.27 ton/ha, respectively. Finally, some Bayesian model averaging (BMA) models were proposed to estimate corn biomass based on the selected indices and different spectral bands. Results of the BMA models revealed that the accuracy of biomass estimation models could be improved using the capabilities and advantages of different vegetation indices.

Dynamic Effects Of Interaction Between Economic Growth, Investment And Savings With International Trade In Indonesia

Anticipation of economic uncertainty needs to be done to avoid the brink of crisis by maintaining the pace of economic growth, investment and savings climate as well as smooth international trade. This study aims to analyze and explore the dynamic relationship interaction between economic growth variables projected as Gross Domestic Product (GDP), investment, gross domestic savings and international trade of the Indonesian state with data in the period 1971-2020 obtained from the worldbank.org website. The research method uses the Vector Auto Regression (VAR) analysis model with the development of Impulse Response Function (IRF) and Variance Decomposition (VDC) analysis to explain the response of a variable to the shock of the variable itself and other variables. The results of the analysis revealed that the one-way relationship for the variables of investing, saving, and trading was influenced by the variable itself with a lag of one. In addition, the results of IRF analysis show that the variable response of economic growth, investment, savings and international trade is dominated by shocks to economic growth variables, and the results of VDC analysis show that economic growth variables most contribute to the variable response of economic growth itself, investment, savings and international trade. The results of this study can be used as a reference for the formulation of anticipatory policies by the government and Bank Indonesia in preventing the risk of economic crisis by maintaining macroeconomic variable stability so that the level of domestic market confidence is maintained in the outlook. Therefore, the policy stages that can be carried out are to control inflation, reduce the current account deficit, maintain fiscal balance and improve in managing foreign debt.

Green investing in China's air cargo industry: Opportunities and challenges for sustainable transportation

Aviation cargo remains vital in the economic activities to transported goods from one place to another. The developed and developing countries mainly consider the transaction routes for air transportation for safe and quickest mode. Chinese economy is attracting the global World through its exports. The country's air cargo system is mainly reliant on gasoline and petroleum-based fuels, which harms the country's green transportation agenda. The high use of fuel combustions in the aviation sector needed greenfield investment that helps to use green energy as an alternative sustainable fuel. Further, sustainable aviation insurance and financial coverage are needed to mitigate the adverse negative externalities from air cargo operations. Based on the crucial facts, the study used air cargo operations, transportation fuel combustions, private investment in the transportation and insurance coverage in the pollution damage function for the China economy using data from 1975 to 2020. The research employed a non-linear ARDL Bounds testing strategy to break down the sequence of variables into dynamic positive and negative multipliers. Positive shocks in air freight, insurance services, and greenfield investment have been shown to reduce carbon emissions immediately and over the long term. In the short term, carbon damages are exacerbated by the negative shocks resulting from the use of transportation fuel and the availability of insurance. Moreover, both the positive and negative shocks associated with transportation fuel combustions and air transportation freights contribute to a rise in carbon damage. The variance decomposition analysis validated the asymmetric correlations between the aforementioned variables in the intertemporal environment. Based on the findings, negative shocks from total fuel combustions are expected to impose the greatest carbon damages over the next decade, followed by insurance services and air freight operations. The study concludes that air cargo operations need to be sustainable transacting routes fueled by biofuel energy sources, greenfield investment, and sustainable aviation insurance coverage to achieve the ‘green is clean’ transportation agenda.

Reducing carbon emissions with Geoscience solutions: A look at the contributions of nuclear energy, technology, and green finance

Standards for Low-Carbon Energy Portfolios (LC-EPS) mandate that a certain percentage of a region's electricity generation originate from zero- or low-emissions sources. From 1995Q1 through 2020Q4, the study used the ARDL-Bounds testing technique to estimate coefficient parameters, Granger causality to draw causal inferences, and variance decomposition analysis to anticipate the factors that will have the greatest impact on carbon emissions in the US economy. Nuclear power significantly impacts carbon emissions, as seen by an inverted U-shaped environmental Kuznets curve, whereas long-term impact of innovation leads to lower emissions. On the other hand, exports of sophisticated technology reduce carbon emissions. Economic growth has a discernible effect on carbon emissions, nuclear power, innovation, and environmentally friendly financing. High-tech exports will likely impact carbon emissions most, followed by a demand for nuclear power, innovation, economic expansion, and sustainable finance for the next ten years. These results give policymakers helpful insight into how the US economy may reduce carbon emissions and fight climate change via renewable energy and green finance.

Distribution Characteristics and Influencing Factors of Abundant and Rare Planktonic Microeukaryotes in Jinsha River

Planktonic microeukaryotes are usually composed of a few abundant species and a large number of rare species, which play an important role in maintaining the health and stability of aquatic ecosystems. At present, little is known about the biogeographical distribution patterns of these two groups of microeukaryotes in large damming rivers. This study analyzed the distribution patterns of abundant and rare planktonic microeukaryotes and the dominant factors affecting their spatial distributions in the Jinsha River, one of the largest rivers in southwestern China that is strongly regulated by cascade dams. The results showed that the alpha diversity of planktonic microeukaryotes in the Jinsha River was higher in the cascade dam reach than that in the upstream natural reach, and the increase in alpha diversity of rare species was larger than that of rich species. There were significant differences in microbial community composition among different river sections, and the relative abundances of the dominant genera such as Vermamoeba shared by them were also significantly different between the two river sections. The results of distance decay analysis revealed that the geographic distribution patterns of rare and abundant taxa were jointly influenced by environmental heterogeneity and dispersal limitation, and the results of variance decomposition analysis and partial Mantel further indicated that dispersal limitation was the dominant ecological process. The results provided data support for the distribution and ecological response of microorganisms in rivers in the areas lacking data in Southwest China.

The precision of satellite-based net irrigation quantification in the Indus and Ganges basins

Abstract. Even though irrigation is the largest direct anthropogenic interference in the natural terrestrial water cycle, limited knowledge of the amount of water applied for irrigation exists. Quantification of irrigation via evapotranspiration (ET) or soil moisture residuals between remote-sensing models and hydrological models, with the latter acting as baselines without the influence of irrigation, have successfully been applied in various regions. Here, we implement a novel ensemble methodology to estimate the precision of ET-based net irrigation quantification by combining different ET and precipitation products in the Indus and Ganges basins. A multi-model calibration of 15 models independently calibrated to simulate rainfed ET was conducted before the irrigation quantification. Based on the ensemble average, the 2003–2013 net irrigation amounts to 233 mm yr−1 (74 km3 yr−1) and 101 mm yr−1 (67 km3 yr−1) in the Indus and Ganges basins, respectively. Net irrigation in the Indus Basin is evenly split between dry and wet periods, whereas 70 % of net irrigation occurs during the dry period in the Ganges Basin. We found that, although annual ET from remote-sensing models varied by 91.5 mm yr−1, net irrigation precision was within 25 mm per season during the dry period for the entire study area, which emphasizes the robustness of the applied multi-model calibration approach. Net irrigation variance was found to decrease as ET uncertainty decreased, which is related to the climatic conditions, i.e., high uncertainty under arid conditions. A variance decomposition analysis showed that ET uncertainty accounted for 73 % of the overall net irrigation variance and that the influence of precipitation uncertainty was seasonally dependent, i.e., with an increase during the monsoon season. The results underline the robustness of the framework to support large-scale sustainable water resource management of irrigated land.