Variance Decomposition Analysis Research Articles

Macroeconomic Impacts of Oil Price Shocks: Evidence from Iraq by Using Vector Autoregressive Model

Based on a VAR framework, Granger Causality Tests, Impulse Response Function and Variance Decomposition analysis on the annual data from 1970 to 2021, This study aims to demonstrate the effect of oil price fluctuations on GDP, imports, international reserves, and exports in Iraq. The Granger causality results demonstrated a unidirectional connection moving from oil price to imports, exports, international reserves, and GDP, this is an inevitable result because oil prices are determined in the international markets according to specific factors that do not include local economic variables. The variance decomposition result indicated that oil price changes are an important source of variation in the studied variables. Finally, the results of the impulse response function indicated that fluctuations in oil prices significantly impact GDP, exports, imports, and international reserves in Iraq. Therefore, we suggest that economic policymakers work seriously to develop practical plans to diversify the Iraqi economy instead of relying too heavily on oil revenues, which are characterized by sharp and continuous volatility, to avoid the negative effects of these fluctuations on the main macroeconomic variables in Iraq.

Water Resource Utilization Assessment in China Based on the Dynamic Relationship between Economic Growth and Water Use

Water scarcity has significantly hampered China’s economic, social, and environmental development. Ensuring sustainable water utilization is crucial given the mounting water stress accompanying continuous economic growth. A quantitative water resource forewarning model was constructed using the vector autoregressive (VAR) model. By analyzing the key indicators related to water systems and GDP data from 2001 to 2022, the VAR model revealed the long-term dynamic correlation between water consumption and economic growth using generalized impulse response, co-integration, and predictive variance decomposition analyses. The results revealed the presence of a long-term equilibrium between water consumption and economic growth, with a stable co-integration relationship and an optimal lag period of one year. The positive impact of water consumption on economic development increased during the 2001–2022 period, indicating a rising dependence of GDP on water resources. Water usage rose with economic development, while the water resource carrying capacity remained high and continued to grow. Based on the generalized impulse response, co-integration, and predictive variance decomposition analyses, this study predicted water-use-related indicators, providing vital early warnings for China’s water environment carrying capacity from 2023 to 2050. This enabled informed decision-making and fostered sustainable water management practices for the future.

Exploring the mechanisms affecting energy consumption in the construction industry using an integrated theoretical framework: Evidence from the Yangtze River economic Belt

Significant environmental challenges remain due to growing construction industry energy consumption (CIEC). Most existing studies focus on energy consumption at the city level, neglecting industry-level perspectives. To clearly explain the CIEC mechanism in the Yangtze River Economic Belt (YREB), this study adopts panel data from the YREB collected by the Chinese government from 2004 to 2020 and constructs an analytical model of the CIEC impact mechanism. Through Granger causality, impulse response function, and variance decomposition analyses, we found that technological innovation has an inverted U-shape and the greatest impact on CIEC, with a contribution rate gradually increasing with the cycle, finally reaching 0.4 %. The impact of urban development and foreign direct investment on CIEC shows a gradual upward trend, with a final contribution rate of 0.3 %; and the impact of urban resident population on CIEC is very small, with a contribution rate of only 0.1 %. Therefore, this study provides a new theoretical framework for future research on energy consumption and a theoretical basis for CIEC management from a government and construction industry manager perspectives.

Asymmetric Effects of Oil Price Volatility on Government Expenditure in Oil-Importing African Countries: Evidence from Zambia

This paper examines the impact of disaggregated oil price shocks the government expenditure in oil-importing African countries using the case of Zambia. The analysis utilizes a“Structural Vector Autoregressive (SVAR) Model” to evaluate the short-run effects, while a “Vector Error Correction Model (VECM) is employed to analyze the relationship over the long-run. Results of the short-run analysis reveal a notable positive influence of oil price shocks”on government expenditure that emanates from global aggregate and precautionary demand factors. The aforementioned results are clarified by the effects that increased global aggregate demand has on copper priсes, which in turn influences government revenue derived from taxes. Additionally, the transmission of “financial contagion and volatility spillovers” from the οil to the copper market clarifies the influence of precautionary demand on government expenditure. The findings obtained from the VECM indicate that global aggregate demand has positive effects on public sector spending in the long run. Additionally, the analysis demonstrates that the rate at which public sector spending recovers to its equilibrium level after a temporary deviation is both monotonic and statistically significant. Moreover, among the three categories of οil priсe hikes, Forecast Error Variance Decomposition analysis reveals that shocks related to precautionary demand have the most significant impact, accounting for 11.7% of the observed variation in public sector spending.

Inter-linkages Among Selected Stock Markets of South Asia: Revisit

The present study investigates the inter linkages among South Asian stock markets namely India, Sri Lanka and Pakistan. The topic is important for international investors because if the stock markets are found integrated with each other then they cannot reap profits from portfolio diversification or reduction in risk. Daily closing prices of the bench mark indices of three countries are taken for a period from 1 Jan. 2001 to 23th Feb. 2023. The paper used statistical tools such as descriptive statistics and correlation analysis and on the other hand, the econometric techniques namely Johansen’s Co integration test, VAR, Variance Decomposition Analysis, Impulse response function and Granger causality test and the selected stock markets are found interlinked in long run as well as in short run.

Empirical Evidence on Tourism, Geopolitical Risk and Economic Policy Uncertainty Relationships in Malaysia

Malaysia, celebrated for its rich cultural diversity, vibrant cities, and pristine natural landscapes, stands prominently on the global tourism map. However, the tourism industry is susceptible to the impact of geopolitical risk and economic policy uncertainty. These external factors, shaped by global events and policy changes, can significantly influence the tourism landscape in Malaysia. Given this significance, the study aims to empirically investigate the cointegrating relationship between international inbound tourism levels (tourist arrivals), geopolitical risk (GPR index), and economic policy uncertainty (EU index) in Malaysia. The analysis uses quarterly observations from the first quarter of 2000 to the fourth quarter of 2022. The study employs the Granger Causality test, supported by structural VAR impulse functions and variance decomposition analysis to illustrate how economic policy uncertainty responds to shocks in tourist arrivals. The significant causal relationship observed moves from tourist arrivals to economic policy uncertainty. In other words, the study implies that variations in tourist arrivals have a lasting impact on economic policy uncertainty but not necessarily on geopolitical risk. This study provides valuable implications for policy planning and decision-making. Policymakers should consider the implications of shifts in tourism patterns for economic policy uncertainty. The absence of a long-term relationship between tourist arrivals and geopolitical risk may suggest conducting separate risk assessments to manage geopolitical risks that may affect the tourism industry in Malaysia.

Empirical analysis of the relationship between economic growth and environmental pollution based on VAR model: A case study of Hubei Province China

In this paper, the industrial three wastes from 1992 to 2018 in Hubei Province were collected as environmental indicators, and the per capita GDP was used as economic indicators. The study of Kuznets curve showed that industrial wastewater gradually decreased with economic growth, showing an inverted N shape; Both industrial emissions and solid waste will increase with the growth of per capita GDP, industrial waste gas shows a linear relationship, and industrial solids show an inverted U relationship. Then through the VAR model research, it is found that the response of industrial “three wastes” to per capita GDP growth in Hubei Province is positive in the short term. The long-term response of economic development in Hubei province to industrial waste gas and industrial solid waste discharge is positive and decreasing, but the short-term response to industrial wastewater discharge is negative. The variance decomposition analysis reveals that the primary environmental determinants influencing the current economic growth trajectory of Hubei Province are industrial wastewater, industrial waste gas, and industrial solid waste. Fluctuations in economic growth within Hubei Province demonstrate a stronger correlation with variations in emissions of industrial wastewater and industrial solid waste, while exerting comparatively less influence on changes in emissions of industrial waste gas.

Price Dynamics in South African Agriculture: A Study of Cross-Commodity Spillovers between Grain and Livestock Markets

In South Africa, the agricultural sector is a crucial pillar of the economy, with the livestock and grain industries playing significant roles in ensuring food security, fostering economic growth, and providing employment opportunities, particularly in rural areas. This research addresses the relatively unexplored relationship between the livestock and grain industries in South Africa. This study employs a comprehensive approach using a VAR/VECM framework alongside VECM Granger causality tests, Toda Yamamoto causality tests, impulse response functions, and variance decomposition analysis. The main findings of this study demonstrate a long-run relationship among the study variables, with consistently low error correction terms indicating slow short-term adjustments. Significant long-run relationships were observed between grain feed prices and livestock prices, where yellow maize and soybean prices affect live weaner prices, while beef carcass prices influence yellow maize prices. Overall, the results highlight the pivotal role that yellow maize plays as a link between the South African livestock and grain markets. The study concluded that policy formulation for the South African agricultural sector must consider the interconnected nature of the grain and livestock markets to achieve sustainable and effective outcomes.

Integrating FinTech with Industry 4.0 for sustainable mineral resource management: Exploring the interplay of technology, regulation, and economic dynamics

In the field of global geology, the intricate relationship between Earth’s mineral resources and their pivotal role in fostering sustainable human development is a fundamental area of scholarly investigation. This research thoroughly explores the complex dynamics between technological progress, financial innovations, and regulatory structures in the context of sustainable management of mineral resources. Covering the period from 2000 to 2022 and including key international stakeholders in the mineral industry, the study employs a Panel Autoregressive Distributed Lag (ARDL) approach to assess the effects of Industry 4.0 technologies (IND), Financial Technology (FinTech), Regulatory Quality (RQ), Energy Efficiency (EF), and per capita Gross Domestic Product (GDP) on Mineral Resource Capital (MRC). The results reveal a substantial, positive long-term influence of Industry 4.0 on Mineral Resource Capital, underscoring the essential role of technology. Conversely, FinTech exhibits a nuanced association with Mineral Resource Capital, indicating the necessity for its careful incorporation. The analysis highlights the significance of regulatory quality in the sustainable management of resources, while the negative impact of energy efficiency points towards the need for more sustainable industrial practices. Through variance decomposition and impulse response analysis, the study delineates the responsiveness of Mineral Resource Capital to various economic fluctuations, with Granger causality tests offering additional insight into these complex interactions. This investigation contributes significantly to the existing scholarly literature and offers crucial perspectives for policy-makers and industry participants, advocating for a cohesive strategy that effectively harnesses technological and financial innovations to enhance sustainability and economic resilience in the mineral sector.

An affine term structure model with Fed chairs’ speeches

I analyze the impact of the sentiment expressed in speeches by Fed chairs on Treasury bond yields, using an affine term structure model. The speeches by Fed chairs are numerically evaluated to generate three sentiment factors: negative, neutral, and positive. The variance decomposition analysis indicates that sentiment factors account for a considerable portion of the variance in yield forecasts.

ANOVA approximation with mixed tensor product basis on scattered points

In this paper we consider an orthonormal basis, generated by a tensor product of Fourier basis functions, half period cosine basis functions, and the Chebyshev basis functions. We deal with the approximation problem in high dimensions related to this basis and design a fast algorithm to multiply with the underlying matrix, consisting of rows of the non-equidistant Fourier matrix, the non-equidistant cosine matrix and the non-equidistant Chebyshev matrix, and its transposed. Using this, we derive the ANOVA (analysis of variance) decomposition for functions with partially periodic boundary conditions through using the Fourier basis in some dimensions and the half period cosine basis or the Chebyshev basis in others. We consider sensitivity analysis in this setting, in order to find an adapted basis for the underlying approximation problem. More precisely, we find the underlying index set of the multidimensional series expansion. Additionally, we test this ANOVA approximation with mixed basis at numerical experiments, and refer to the advantage of interpretable results.

On the Sensitivity of Large-Eddy Simulations of the Atmospheric Boundary Layer Coupled with Realistic Large-Scale Dynamics

Abstract We present a new ensemble of 36 numerical experiments aimed at comprehensively gauging the sensitivity of nested large-eddy simulations (LES) driven by large-scale dynamics. Specifically, we explore 36 multiscale configurations of the Weather Research and Forecasting (WRF) Model to simulate the boundary layer flow over the complex topography at the Perdigão field site, with five nested domains discretized at horizontal resolutions ranging from 11.25 km to 30 m. Each ensemble member has a unique combination of the following input factors: (i) large-scale initial and boundary conditions, (ii) subgrid turbulence modeling in the gray zone of turbulence, (iii) subgrid-scale (SGS) models in LES, and (iv) topography and land-cover datasets. We probe their relative importance for LES calculations of velocity, temperature, and moisture fields. Variance decomposition analysis unravels large sensitivities to topography and land-use datasets and very weak sensitivity to the LES SGS model. Discrepancies within ensemble members can be as large as 2.5 m s−1 for the time-averaged near-surface wind speed on the ridge and as large as 10 m s−1 without time averaging. At specific time points, a large fraction of this sensitivity can be explained by the different turbulence models in the gray zone domains. We implement a horizontal momentum and moisture budget routine in WRF to further elucidate the mechanisms behind the observed sensitivity, paving the way for an increased understanding of the tangible effects of the gray zone of turbulence problem. Significance Statement Several science and engineering applications, including wind turbine siting and operations, weather prediction, and downscaling of climate projections, call for high-resolution numerical simulations of the lowest part of the atmosphere. Recent studies have highlighted that such high-resolution simulations, coupled with large-scale models, are challenging and require several important assumptions. With a new set of numerical experiments, we evaluate and compare the significance of different assumptions and outstanding challenges in multiscale modeling (i.e., coupling large-scale models and high-resolution atmospheric simulations). The ultimate goal of this analysis is to put each individual assumption into the wider perspective of a realistic problem and quantify its relative importance compared to other important modeling choices.

The effects of the fourth industrial revolution on employment in South Africa: Variance decomposition analysis

This study investigates the effect of the fourth industrial revolution on levels of employment in South Africa. The fourth industrial revolution is proxied by ICT development with the components of mobile telephony, fixed-line telephony and internet penetration. The data used in this study were obtained from the World Bank's World Development Indicator database spanning from 1980 to 2019, while the labour market data was obtained from Statistics South Africa for the same time period. The study employed the Johansen Cointegration test and variance decomposition techniques to determine existence of long run equilibrium and possible shocks impact respectively. The results indicated that there exists a long run relationship between employment levels and the hypothesised determinants. The ICT revealed a positive relationship with employment in both the short and long run which is an indication of importance of fourth industrial revolution to employment in modern society. Policy recommendation were provided for the South African policy-makers in line with further deployment of ICT tools that promote employment.

Policy Implications of Fiscal Deficit and Its Trends in India: An Econometric Analysis

"The paper attempted to explore the trend of fiscal deficit in India from 1993-94 to 2022-23 and also to determine whether the growth patterns of fiscal deficit have changed before and after the Housing Bubble Crisis of 2008. The study too made an effort to analyse the causality among fiscal deficit, government revenue and expenditure in India. Therefore, the paper follows a diagnostic research design, and is based on secondary data, collected from the Budget and finance documents of the Government of India. Econometrics tools including eight different regression models and CAGR (Compound Annual Growth Rate) were used. And the study revealed that the fiscal deficit follows a log-linear trend, and as per the VDA (Variance Decomposition Analysis) model in the long run the variability of fiscal deficit is explained by 66.09% of govt. expenditure, for the sample period. Thus, the study recommends to efficiently utilise the govt. expenditure to have a proper management of the fiscal deficit in India."

Local predictability of stock returns and cash flows

Motivated by the present-value framework, this article proposes a novel and flexible semiparametric long-horizon time-varying model to investigate the so-called ‘pockets of predictability’, which refer to local periods in which stock returns or cash flows are significantly predictable. A semiparametric profile method is used to estimate both time-varying and constant parameters. In the empirical studies, the predictive ability of the dividend-price ratio for dividend growth is considerably weaker than its ability to predict stock returns at both short and long horizons. Moreover, dividend smoothing only matters for dividend growth predictability at a low frequency. In addition, localized variance decomposition analysis suggests that the present-value relation is locally valid for most sample periods and that the main driver of the variation in the dividend-price ratio stems from its ability to predict stock returns. Lastly, using the earnings-price ratio produces similar results.

Sci-tech innovation, industrial structure transformation and economic development: An empirical study based on province panel data of China

Based on panel data of 31 provinces in China from 2000 to 2019, this study explores the complex dynamic interaction between scientific and technological innovation, industrial structure transformation and economic development by building PVAR model. After GMM estimation, impulse response function analysis and variance decomposition analysis, the following conclusions are drawn: Technological innovation has a significant promoting effect on economic development, and economic development can also promote technological innovation; Technological innovation has a positive impact on industrial structure transformation, while industrial structure transformation has a slight negative impact on technological innovation; Economic development can lead to industrial structure transformation, but the impact of industrial structure transformation on economic development is not significant.

The Relationship Between Stock Performance and Money Supply Based on VAR Model in the Context of E-commerce

Abstract With the development of network technology, electronic money as a payment and settlement tool based on the network has been developing at an unprecedented speed. Based on the background of e-commerce, this study uses the data from June 2012 to June 2022 to establish a vector autoregressive model to study the interaction between oil prices, stock performance, and money supply. Such a model can not only further our understanding of the complex relationship between these important variables but also shed light on future oil prices. Granger causality test, impulse response function analysis, and variance decomposition analysis have been applied to variables in the model. The main finding is that oil price responds to changes in stock performance and money supply, stock performance is affected by both oil price and money supply, and changes in money supply can be explained by stock performance fluctuations. Such a relationship can help inform traders in e-commerce and investment banking to generate better predictions of future oil prices.

Data-Driven Global Sensitivity Analysis of Variable Groups for Understanding Complex Physical Interactions in Engineering Design

Abstract In engineering design, global sensitivity analysis (GSA) is used for analyzing the effects of inputs on the system response and is commonly studied with analytical or surrogate models. However, such models fail to capture nonlinear behaviors in complex systems and involve several modeling assumptions. Besides model-focused methods, a data-driven GSA approach, rooted in interpretable machine learning, would also identify the relationships between system components. Moreover, a special need in engineering design extends beyond performing GSA for input variables individually, but instead evaluating the contributions of variable groups on the system response. In this article, we introduce a flexible, interpretable artificial neural network model to uncover individual as well as grouped global sensitivity indices for understanding complex physical interactions in engineering design problems. The proposed model allows the investigation of the main effects and second-order effects in GSA according to functional analysis of variance (FANOVA) decomposition. To draw a higher-level understanding, we further use the subset decomposition method to analyze the significance of the groups of input variables. Using the design of a programmable material system (PMS) as an example, we demonstrate the use of our approach for examining the impact of material, architecture, and stimulus variables as well as their interactions. This information lays the foundation for managing design space complexity, summarizing the relationships between system components, and deriving design guidelines for PMS development.

Calculation and Cause Analysis of Hidden Unemployment—A Case Study of the Northeast State-Owned Forest Areas in China

The Northeast State-owned Forest Areas comprise the largest region of resources in China. The transformation of the Northeast State-owned Forest Areas is due to various stimuli, such as policies, systems, and markets. In the context of ecological construction and the reform of the state-owned forest system in Northeast China, these have undergone a transformation in terms of economy, society, and ecology. However, a mismatch persists between employment and industrial structures, leading to hidden unemployment. This study employs the framework of hidden unemployment theory, utilizing the Cobb–Douglas production function to quantify the hidden unemployment rate, revealing the relationship between transformation and hidden unemployment. Through this analysis, the underlying causes of hidden unemployment in Northeast China’s state-owned forest regions are scrutinized. There is a bidirectional causal relationship between ecological transformation and hidden unemployment, while there is a unidirectional causal relationship between economic transformation and hidden unemployment. Variance decomposition analysis highlights the substantial contribution of social transformation, followed by economic transformation, with ecological transformation playing a comparatively smaller role. Based on the above research, this article proposes expediting the reforms within the forest industry groups, facilitating the separation of governmental and corporate interests. Additionally, it advocates for strategic forestry development planning to effectively absorb surplus labor, and proposes three policy directives aimed at supporting forest area employees, including provisions for job transfers and reemployment opportunities.

An adaptive data-driven subspace polynomial dimensional decomposition for high-dimensional uncertainty quantification based on maximum entropy method and sparse Bayesian learning

Polynomial dimensional decomposition (PDD) is a surrogate method originated from the ANOVA (analysis of variance) decomposition, and has shown powerful performance in uncertainty quantification (UQ) accuracy and convergence recently. However, complex high-dimensional problems result in a large number of polynomial basis functions, leading to heavy computational burden, and the probability distributions of the input random variables are indispensable for PDD modeling and UQ, which may be unavailable in practical engineering. This study establishes an adaptive data-driven subspace PDD (ADDSPDD) for high-dimensional UQ, which employs two types of data for modeling the PDD basis function and the low-dimensional subspace directly, namely, the data of input random variables and the input-response samples. Firstly, we propose a data-driven zero-entropy criterion-based maximum entropy method for reconstructing the probability density functions (PDF) of input variables. Then, with the aid of the established PDFs, a data-driven subspace PDD (DDSPDD) is proposed based on the whitening transformation. To recover the subspace of the function of interest accurately and efficiently, we put forward an approximate active subspace method (AAS) based on the Taylor expansion under some mild premises. Finally, we integrate an adaptive learning algorithm into the DDSPDD framework based on the sparse Bayesian learning theory, obtaining our ADDSPDD; thus, the real subspace and the significant PDD basis functions can be identified with limited computational budget. We validate the proposed method by using four examples, and systematically compare four existing dimension-reduction methods with the AAS. Results show that the proposed framework is effective and the AAS is a good choice when the corresponding assumptions are satisfied.