Variance Decomposition Research Articles

PENGARUH NILAI TUKAR, PDB, DAN PEMBIAYAAN BANK SYARIAH TERHADAP EKSPOR KOPI DI INDONESIA

This research aims to determine the influence of exchange rate variables, Gross Domestic Product (GDP) and Sharia Financing on coffee exports in Indonesia. The method used in this research is a quantitative method, using timeseries (secondary) data with data collection techniques using documentation studies, namely by collecting data on Exports, GDP, Exchange Rates and Financing distributed by Sharia Banking obtained from the website of the Central Bureau of Statistics (BPS). ), Bank Indonesia (BI), Financial Services Authority (OJK) and ICO (International Coffee Organization). The data was then analyzed using the VECM (Vector Error Correction Model) method using Eviews 10 software. This shows that financing has an effect on the exchange rate which is significant in the long term but has no significant effect in the short term. Apart from that, based on the results of the impulse response analysis, it caused shocks but did not cause too much volatility in Indonesian coffee exports. The exchange rate has no effect on GDP, and GDP has no effect on financing. Then, based on the results of the variance decomposition analysis, it shows that the value of financing plays the biggest role in the exchange rate of Indonesian coffee exports. And it has a significant effect in the long term but does not have a significant effect in the short term on the volatility of Indonesian coffee exports.

Spatial distribution patterns and drivers of above- and below- biomass in Chinese terrestrial ecosystems

Unraveling the dynamics of the global carbon cycle and assessing the sustainability of terrestrial ecosystems are critically dependent on a comprehensive understanding of vegetation biomass. This exploration delves into the pivotal role of biomass within vegetation communities, emphasizing its impact on ecosystem health, productivity, and community structure development. These insights are invaluable for advancing ecological science and conservation efforts. The synthesis of aboveground (AGB) and belowground (BGB) biomass data from 4485 and 3442 locations across China, respectively, collates a wide range of published sources. Integrating this extensive dataset with environmental parameters and applying advanced machine learning techniques facilitated an in-depth analysis of AGB and BGB spatial patterns within China. Techniques such as variance decomposition analysis and piecewise structural equation modeling were employed to dissect the factors contributing to the spatial variability of vegetation biomass. Significant spatial heterogeneity in biomass distribution was uncovered, with vegetation biomass in the northwest markedly lower than in the southern and northeastern regions. It was observed that AGB consistently surpassed BGB. Climatic conditions, soil characteristics, and soil nutrients were found to significantly explain 53 % and 48 % of the total variance in AGB and BGB, respectively. Specifically, solar radiation and soil total nitrogen were identified as critical factors influencing variations in AGB and BGB. The findings offer profound contributions to the understanding of the global carbon balance and the evaluation of terrestrial ecosystems sustainability. Moreover, they provide essential insights into the ecosystems' response mechanisms to global changes, serving as a fundamental reference for future studies on terrestrial ecosystem carbon cycling and carbon sequestration potentials.

How connected is the oil-bank network? Firm-level and high-frequency evidence

By introducing a new generalized forecast error variance decomposition (GFEVD) approach that splits the same into its contemporaneous and lagged components, we investigate the risk spillover effects of different order moments, derived from intraday data, for the top 10 banks and top 10 oil and gas companies in the U.S., covering the period from December 29, 2017 to December 30, 2022. The study finds that, first, the dynamic total connectedness of all order moments is heterogeneous over time driven by economic events. Second, except realized volatility spillovers, the vast majority of overall spillovers are attributable to contemporaneous spillovers, while only a tiny fraction is associated with lagged spillovers. Finally, realized skewness (crash risk) and realized kurtosis (extreme events) in banks and oil and gas companies originate mainly from intra-industry rather than inter-industry transmission.

Natural resource rents and financial inclusion nexus: Evidence from Africa

A significant gap in total natural resources wealth and financial inclusion studies on the African continent stems from their impact and variation. To address these deficiencies, this study investigates the effect of total natural resource rents on financial inclusion, employing data from 27 African economies from 2004 to 2019. This study uses the most recent panel estimators, causality test and variance decomposition analysis for accuracy and reliability. Findings reveal that total natural resources wealth and economic progress significantly reduces financial inclusion, while fossil and renewable energy promote financial inclusion. Moreover, financial globalization had an insignificant outcome on financial inclusion. The causality test revealed bi-directional causation between financial inclusion in renewable energy and financial inclusion in economic progress. Again uni-directional causation from financial inclusion to fossil energy and financial inclusion to natural resources wealth. At the same time, a neutral causal effect was found from financial inclusion to financial globalization. Regarding variations, financial inclusion had the highest effect on natural resource rents. Our findings grasp new insight into financial inclusion in Africa, and policymakers are advised to support natural resources rent by directing its impact into the economy's real sector.

Impulsive Response Analysis of Financial Markets in QUAD Economies: A VAR Modelling Approach

Financial market integration remains a key focus in academic finance, especially given how crucial it is to portfolio diversification. The purpose of this research is to investigate a crucial yet underexplored aspect like the cointegration of economies with formal or unofficial military ties, focusing on countries within the Quad strategic forum—India, Japan, Australia, and the United States. The daily closing values of benchmark stock market indices of Quad economies viz. Nikkie 225 for Japan, S&P ASX 200 for Australia, NASDAQ composite for the United States, and S&P Sensex for India were considered. Unit root tests, Johansen Cointegration tests, VAR (Vector Autoregressive) model, and the Granger Causality statistics are computed for data analysis. Additionally, the Impulse response function and variance decomposition are computed for better financial market predictions. The result reveals intriguing dynamics: while the Indian stock market initially responds positively to shocks in the Australian market, this effect diminishes over time. Also, movements in the Australian and United States stock markets significantly contribute to predicting fluctuations in the Indian market, contrasting with the limited predictive power of the Japanese stock market. These findings carry significant practical implications for international investors, offering valuable guidance for optimizing portfolio diversification strategies amidst geopolitical and economic complexities. This paper contributes to academic literature by offering empirical insights into the short-term dynamics and causal relationships of Quad countries’ stock markets, setting a foundation for future studies on long-term trends and market responses to external factors.

Analysis of dynamic relationship between agricultural products circulation service industry and circulation efficiency in less developed regions: based on data from Western China

The market circulation system for agricultural products in less-developed regions lags behind; it fails to meet the requirements for efficient circulation. Therefore, it is crucial to explore the dynamic relationship between the Agricultural Products Circulation (APC) service industry and circulation efficiency. Using panel data from 29 regions in China, the entropy method measured the Development Level (DL) of the APC service industry, while the non-oriented super-efficiency Slacks-Based Measure (SBM) model was employed to assess the APC efficiency. A Panel Vector Autoregressive (PVAR) model was built for estimation. The impulse response function and variance decomposition were utilized to analyze the differences in the DL of the APC service industry, circulation efficiency, and their interaction mechanism across the Western, Central, and Eastern regions of China. The findings indicate that the DL of the APC service industry and APC efficiency in the less-developed regions of the West have a positive driving effect on each other. However, the magnitude of this effect was asymmetric and transient. The APC service industry’s DL and APC efficiency are more dependent on their inertia, and no efficient two-way feedback mechanism has been formed. This study provides a reference for the construction and sustainable development of modernized APC systems in less-developed regions.

Comparative Analysis of Univariate SARIMA and Multivariate VAR Models for Time Series Forecasting: A Case Study of Climate Variables in Ninahvah City, Iraq

This study involves a comparison between the application of the univariate SARIMA model and the utilization of VAR methods (vector autoregressive models) for multivariate time series analysis. The analysis is conducted using three-time series variables derived from data representing the monthly average of Humidity (H), Rainfall (R), and Temperature (T) in Ninahvah City, Iraq. Both univariate and multivariate time series approaches are employed to model these series. The paper also outlines the implementation of vector autoregressive, structural vector autoregressive, and structural vector error correction models using the 'vars' package. Additionally, it provides functions for diagnostic testing, estimation of constrained models, prediction, causality analysis, impulse response analysis, and forecast error variance decomposition. Furthermore, it introduces three fundamental functions, VAR, SVAR, and SVEC, for estimating these models. The comparison between the methods is based on evaluating the mean error produced by each approach. The findings of the study indicate that univariate linear stationary methods outperform multivariate models. The analysis of the data was carried out using the R software platform. The primary objective is to assess the performance of univariate and multivariate time series models in handling the given data. The research gap lies in the need for a comparative evaluation of SARIMA and VAR methods for time series analysis in the context of monthly environmental variables. These models were chosen due to their effectiveness in capturing temporal dependencies and interactions among multiple variables in time series data, providing a comprehensive analysis of climatic patterns in Ninahvah City, Iraq. The study aims to address the research gap by comparing these models and justifying their selection based on their capabilities to analyze the specified time series data.

Marginal propensity to import and terms of trade: Panel granger causality evidence from the East African Community (EAC)

Abstract Subject and purpose of work The nexus between the terms of trade and marginal propensity to import and the consequent policy options arising from it among economic blocs is an area that has not been given due attention in the literature. The focus of the current paper is to examine the link between the marginal propensity to import and the terms of trade in the East African Community (EAC). Material and methods The authors discuss and test a panel Granger causality model which is complemented with the test of impulse response function and variance decomposition using data from five EAC countries (Uganda, Tanzania, Rwanda, Kenya and Burundi). Results The long-run result of the study indicated that causality runs from all the variables to the terms of trade. In the short-run, results reveal that both the marginal propensity to import and the export of manufactures Grangercaused terms of trade without a feedback. The results of the impulse response function revealed that the terms of trade responded positively to shocks in the marginal propensity to import only in the first period, but afterwards the response turned negative in all the other periods. The terms of trade were equally found to respond positively to shocks in inflation rate in all the periods. The variance decomposition results indicated that apart from shocks to itself which was 100% in the first period, marginal propensity to import contributed about 0.0458% of shocks to the terms of trade, and this rose continuously in all the periods. Conclusions The analysis shows that both in the long-run and the short-run, marginal propensity to import determines the terms of trade among the EAC countries.

Global Supply Chain Disruptions, Commodity Price Shocks and Inflation in Japan

In recent years, the concurrent occurrences of the COVID-19 pandemic and the Russian invasion of Ukraine have led to global disruptions in supply chains and a surge in commodity prices. Major advanced economies have experienced an increase in inflation rates and a decline in economic activity. Against this economic backdrop, this paper aims to address the strained condition of supply chains by employing the Global Supply Chain Pressure Index (GSCPI) alongside commodity price indices. Furthermore, it seeks to examine how fluctuations in these variables affect Japan’s macroeconomic landscape through local projection and structural Vector Autoregression (VAR). In the local projection analysis, it has been observed that the strain on supply chains induces sustained increases in consumer and producer prices, further contributing to long-term declines in production. Within the structural VAR model framework, the identification of structural shocks is conducted through sign restrictions, affirming the significance of supply chain shocks and energy and food price shocks as crucial factors contributing to recent price increases. This confirmation is supported by variance decomposition and historical decomposition analyses.

Objective contribution decomposition method and multi-population optimization strategy for large-scale multi-objective optimization problems

As problem complexity and data dimensionality increase, practical problems tend to have a large number of variables. Large-scale multi-objective optimization problems (LSMOPs) are a complex and difficult class of problems with numerous decision variables to be optimized. In addressing these problems using traditional evolutionary algorithms, achieving convergence is often difficult. The decomposition of decision variables and optimization is a more intuitive way to deal with this problem, which reduces the size of the problem to some extent. Thus, in this study, an objective contribution decomposition method and multi-population optimization strategy are proposed for handling LSMOPs. First, the decision variables are decomposed according to their contribution to the objectives. Further decomposition is performed based on the interactions among the decision variables. Then, multiple populations are generated to optimize the variable group and determine the information interaction among the subpopulations. In experimental results, the performance of the proposed algorithm was competitive in comparisons with six state-of-the-art algorithms.

Monetary policy spillovers between the US and African Central Banks: A time- and frequency-varying connectedness study

This study investigates time and frequency connectedness between monetary policy in the US and 7 African countries to determine the extent to which US monetary policy influences policy conduct amongst African Central Banks. We use a time-varying parameter vector autoregressive (TVP-VAR) framework with frequencies to extract the spectral representation of the forecast error variance decompositions of the TVP-VAR and form 3 bands of frequency strata corresponding to long-run, medium-run and short-run. The results of the study are as follows: firstly, the static analysis shows that the US is the dominant transmitter of systemic shocks across all frequencies and the vulnerability of African countries as recipients of these shocks varies across frequency bands. Secondly, the dynamic analysis further reveals stronger short- and medium-run systemic connectedness during the periods of the Large Scale Asset Purchase programme and forward guidance policies whereas long-run connectedness is prominent during periods of US conventional monetary policy particularly in the COVID-19 era. The findings from the country-by-country dynamic spillover specifically show that countries which are more (less) responsive to US monetary policy shocks have lower (higher) inflation rates averages since the start of the pandemic. The findings suggest that African Central Banks can be benefit from higher coordination with the US Federal Reserve and we further propose that Central Banks worldwide in setting similar inflation targets.

Do commodities offer diversification benefits during the COVID-19 pandemic crisis? Evidence from dynamic spillover approach

This paper examines the diversification benefits of commodity indices during the COVID-19 pandemic by analyzing both static and dynamic risk spillovers for the period from January 2, 1998 to September 16, 2020. Using variance decomposition forecasting, we employed static and dynamic analyses based on the estimation of 50-day moving window spillover indices. Globally, the results show significant spillovers between markets during the COVID-19 pandemic crisis. The results show that stock markets are highly interdependent with other financial markets (in both directions), and that commodity markets (except energy) and the bond market are recipients of shocks emanating from stock markets. The main contribution of this paper is to study the return and volatility spillovers between stock and commodity indices before and during the pandemic. This study of shock transmission mechanisms will enable investors to develop optimal diversification and hedging strategies during the crisis. In this context, we found that commodities and US government bonds could offer diversification benefits to investors. In addition, some of these assets may serve as hedging instruments or safe havens during the COVID-19 crisis.

Unveiling interest rates and Islamic banking activity: empirical evidence from Saudi banks

Purpose The purpose of this paper was to explore the impact of interest rate shocks on the deposits and financing of Islamic and conventional banks in Saudi Arabia. Design/methodology/approach The authors use impulse response functions (IRFs) and variance decomposition (VDC) analyses over the period 2008Q1–2020Q2. Findings The IRFs showed that increasing interest rates reduce loans and conventional deposits. For Islamic banks, the deposits are more affected by interest rate changes than the financing. The VDC analysis found that deposits contribute up to 61% of Islamic financing variations, compared to only 25% in conventional lending ones. Originality/value This research contributes to the field of Islamic economics and finance by providing empirical evidence on how interest rates likely impact Islamic and conventional deposits and financing in Saudi banking system.

Impact of Changes in Fuel Prices on Inflation and Economic Growth in Mozambique

The fuel oil is a basic input to production, so an increase in oil price leads to a rise in production costs that induces firms to lower output. In parallel, oil prices change also effects, indirectly, the consumption, through its positive relation with disposable income. However, despite the relevance of the variation in fuel prices in economy, there are no recent study conducted to analyse the impact of changes in fuel prices on GDP and CPI in Mozambique. This study aims to contribute to filling the information gap by providing the quantification of the impact of fuel prices on GDP and CPI. So, the study aim to determine the impact of changes in FUEL prices on Inflation (Consumer Price Index – CPI) and Economic Growth (Gross Domestic Product – GDP), using quarter data from 2007 to 2020. Descriptive statistics, Unit Root Test, Johansen Cointegration Test and Error Correction Model, Granger Causality Econometric Models, Impulse and Response Function and Variance Decomposition Analysis are used. The results indicate that in Mozambique there are a cointegrating long-run relationship between FUEL prices and CPI and GDP, which suggest that the impact of FUEL prices shocks to CPI and GDP in short-run and long-run path. Also, the results show that changes in GDP and FUEL prices lead to an increase in CPI by 3.0% and 1.3%, respectively, <i>coeteris paribus</i>. The previous quarter’s errors (or deviation from long-run equilibrium) are corrected for with the current quarter at a convergence speed of 3.5% to GDP, and 4.8% to CPI. Granger causality tests also indicate causality between CPI and GDP, and the changes in GDP causes changes in other variables.

Analyzing the Role of the Real Estate Sector in the Sectoral Network of the Chinese Economy

The real estate sector in China has strong connections with other sectors. By employing forecast error variance decompositions from least absolute shrinkage and selection operator vector autoregression (LASSO-VAR) model, this study explores the real estate sector's role in China's sectoral network between January 1, 2000, and June 30, 2023. Our analysis reveals that the total sectoral connectedness ranged between 80% and 90% before 2016, but fluctuated between 55% and 90% from 2016 to June 2023. We also identify that shocks primarily spread from the real estate sector to the financial sector from 2014 to 2019. However, this pattern was reversed, with shocks spreading from the financial sector to the real estate sector from early 2020 to June 2023. Furthermore, we observe that the government's long-term mechanisms for the real estate sector have a significant impact on the connection between the real estate sector and the other sectors.

Impact of Remittance on Educational Outcomes: Empirical Evidence from Nepal

The remittances are the treasure for irrigating Nepal's economy. Because emigration is increasing, per capita remittance is also increasing in Nepal. However, the question is signaling in its utilization for educational attainment. In this context, the study examines the impact of remittance on educational outcomes in Nepal. The study applied autoregressive distributed lag (ARDL) estimations, Granger causality, forecast error variance decomposition (FEVD) technique, and impulse response functions (IRFs), with time-series data spanning from 1981 to 2021. The overall findings revealed that remittance had a positive impact on educational outcomes in Nepal. In the short run, remittance was found to be crucial for secondary schooling; in the long run, remittance had a significant effect on tertiary schooling in Nepal. The FEVD confirmed that remittance might be the inevitable predictor of educational outcomes in Nepal. The Granger causality also found a unidirectional causality from remittance to educational outcomes. The policymakers and stakeholders, therefore, should focus on funneling remittance toward financing education as well as a productive sector to accelerate per capita GDP and on cropping the demographic dividend in the Nepali economy.

Plant diversity in the understory of Eucalyptus plantations on Hainan Island and its response to environmental factors

Research on understory plant diversity and its response to environmental factors helps in the sustainable development of plantation forests. We investigated the characteristics of understory plant diversity in Eucalyptus plantation forests located in Dongfang, Ding'an, Tunchang, and Lingao on Hainan Island by leveraging the plot survey method, and analyzing how the understory plant diversity in these Eucalyptus plantation forests responds to environmental factors. The results showed that a total of 124 plant species belonging to 62 families and 112 genera were recorded in the sampled plots of the Dongfang, Ding’an, Tunchang, and Lingao regional sites on Hainan Island, among which species of Fabaceae and Poaceae comprised the largest number of plants. The number of species and plant diversity indices of the shrub layer and herb layer in Eucalyptus plantation forests varied at different sites, The richest understory vegetation in Tunchang, located in the center of Hainan Island, and the highest α-diversity whether gauged by species or phylogenetically. The similarity of the understory plant community species was greatest between Ding’an and Tunchang, whereas the difference in composition was largest between Dongfang and the other three sites. Phylogenetically, the understory plant community at Ding’an had the most distant affinities among species, whereas that at Tunchang had the closest affinities among species. The results of the Mantel test and redundancy analysis revealed differing correlations between plant diversity in the shrub layer versus herb layer and various environmental factors. In particular, elevation and annual average temperature are the two main factors influencing plant diversity in the shrub layer, and soil available nitrogen and annual average sunshine duration are the two main factors influencing plant diversity in the herb layer. Variance decomposition showed that the combined effect of soil, climate, and topography factors is the main driver shaping plant diversity in the shrub layer of the understory in Eucalyptus plantation forests, while the combined effect of climate and soil factors is the main one determining plant diversity in their herb layer.

Analyzing the impact of remittance, FDI and inflation rate on GDP: A comparative study of Bangladesh, Pakistan and Sri-Lanka using VAR and BEKK-GARCH approach

This study examines the impact, conditional correlation and volatility spillover effect of remittances, foreign direct investment and inflation rate on GDP in Bangladesh, Pakistan, and Sri-Lanka, three Asian nations that are particularly vulnerable. While numerous studies have examined the relationship between remittances, FDI and IR on GDP but this paper approaches advanced econometric techniques to capture spillover effect and the dynamic interactions between the variables. For estimation purposes the study employs different econometric techniques such as Augmented Dickey-Fuller (ADF) test, VAR model, Granger causality tests, Impulse Response function, Variance Decomposition and BEKK-GARCH model. Bangladesh and Sri Lanka's REM, FDI and IR have no significant effects on GDP according to the VAR model. BEKK-GARCH demonstrates that three countries have both unidirectional and bidirectional transmissions of volatility, with the exception of Pakistan's REM and Bangladesh's FDI. We find that VAR model may not be adequate in capturing the complex dynamics between variables, which can be better captured by BEKK-GARCH model. Our comparison research shows how these variables affect GDP differently and similarly in each of the three nations, giving policymakers information they can use to create customized policies that encourage economic growth.

A hybrid forecasting architecture for air passenger demand considering search engine data and spatial effect

As the global process of digitalization accelerates and breakthroughs in internet technology emerge, governments worldwide are advocating for data-driven decision-making, aiming to enhance public service efficiency and stimulate economic growth. Against this backdrop, this study focuses on utilizing search engine data (SED) to improve air passenger demand forecasting, responding to national policies aimed at enhancing data analysis capabilities and promoting the development of intelligent transportation systems; however, the existing research is confined to the exploration of the temporal dependency between SED and air passenger demand variables with ignoring the spatial dependency. In order to eliminate this blind spot and catch from various parts of tourist attention, this study proposes a novel SED-driven hybrid forecasting architecture inspired by the theory of spatial effect between adjacent tourist destinations. The architecture includes three main steps: (1) construction of spatial-temporal SED variables, based on two-stage data preprocessing method; (2) variable decomposition and reconstruction, based on TVF-EMD algorithm; (3) prediction of different components of air passenger demand, employed ARIMA model and IHGS-KELM based multi-model fusion strategy respectively, where the IHGS algorithm integrates the circle chaos initialization strategy and the nonlinear convergence factor strategy. To confirm the practical applicability of this hybrid architecture, five comparative experiments based on the actual dataset are designed. The principal results are concluded as follows: (1) spatial-temporal SED is conducive to a fairly accurate air passenger demand forecasting; (2) the multi-model fusion strategy can integrate the fortes of various types of prediction models to obtain better prediction accuracy; (3) the adaptive ensemble method based on IHGS-KELM can contribute to the upgradation of prediction performance of air passenger demand.

A Variational Mode Decomposition Analysis and Prediction Simulation of DO in the Water Environment of the Chengdu Area, China

DO is an important index to characterize environmental water quality. The time series fluctuation of DO can be analyzed via frequency band decomposition, which is very valuable for water quality simulations. In this paper, DO in the Chengdu area of China was studied using variational mode decomposition with daily meteorological data and water quality data from 2020 to 2022. After variable decomposition, the DO data were first decomposed into different frequency band signals named IMF1, IMF2, IMF3, IMF4, and IMF5. IMF1 represented the low-frequency signal with long-term trend characteristics of the data. IMF2 to IMF5 represented the high-frequency signal with short-term mutation characteristics of the data. By combining the variable decomposition results with the correlation analysis, it was found that the long-term trend characteristics of DO are affected by the superposition of meteorological factors, hydrological factors, and water pollution factors but have a weak correlation with any single determining factor. The air temperature, water temperature, phosphorus, air pressure, pH value, chemical oxygen demand, and nitrogen were relatively strongly correlated with the long-term trend characteristics of DO. The short-term mutation characteristics of DO were mainly determined using the characteristics of the water body itself, while the influence of the meteorological factors could basically be ignored. The water temperature, pH value, and eutrophication were the biggest influencing factors. Then, a predictive framework combining frequency division with a deep learning model or a machine learning model was constructed to predict DO. The predicted results of GRU, random forest, and XGBoost with and without the framework were compared. It was shown that, after removing the interference factors with correlations less than 0.3, the predicted value of DO was much closer to the actual value. The XGBoost and random forest models with decomposed signals had a high degree of simulation fitting and could be used to predict DO in the Chengdu area. The above research approach can be applied to further explore the prediction of various pollution factors in different areas of China.