Variance Decomposition Analysis Research Articles

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.

Examining the impact of zakah and Islamic finance on national economic growth

Purpose – This study aims to analyze the effect of Islamic financial instruments, including social funds zakah, infak, and sadaqah (ZIS), Islamic financing, Sharia stocks, and corporate sukuk, on economic growth.Methodology – This study uses monthly data from 2013 to 2022 to examine the relationship between ZIS distribution, Islamic bank financing, Islamic stock capitalization, corporate sukuk issuance, and economic growth proxied by the production industry index (IPI). The Vector Error Correction Model (VECM) was utilized.Findings – According to the short-term estimation results, sukuk significantly hinders economic growth, whereas only the ZIS variable has a positive and significant impact. Long-term economic growth is positively impacted by the ZIS and Islamic financing variables, negatively impacted by sukuk, and not significantly impacted by Sharia stock. The Impulse Response Function and Variance Decomposition analysis results also demonstrate that the variables of the Islamic financial sector have a shock effect on Indonesia's economic growth, with ZIS funds making up the majority of the contribution and Sharia stocks the least.Implications – This study will help policymakers, industry, and academia accelerate the Islamic finance sector in Indonesia and strengthen its role in supporting and advancing Indonesia's economic recovery.Originality – Islamic finance, commonly known as Islamic banking–extends beyond Indonesian banks. Non-bank Islamic institutions, including capital markets and social finance, play a vital role in optimizing Islamic financial instruments for national economic growth.

The hedonic price model for the wine market: A systematic and comparative review of the literature

AbstractThis paper carries out a thorough review of the literature on the estimation of hedonic price functions in the wine market, compiling and carefully documenting all research work on the subject. The review analyses the main methodological decisions taken by the different authors, as well as the typology of the available databases: identification of the relevant market, specification of the price function, sources and types of prices, econometric methodology, and type of publication. The variance decomposition analysis of the Adjusted‐R‐squared values from the estimated hedonic price functions suggests that attribute selection, the definition of the product market, the characteristics of information sources, and the implemented econometric procedures are the most relevant factors in explaining the models’ explanatory power.

The effects of policy rate announcements on the exchange rates

Purpose- Exchange rate is the value of a country's national currency against foreign national currencies. In this context, the exchange rate is considered an important macroeconomic indicator in evaluating the country's economy. The failure to control the exchange rate may damage economy significantly. It is possible to understand this from the 2001 crisis in Turkey, known as 'Black Wednesday', and the foreign exchange crisis that started in Thailand in 1997 and affected many East Asian countries. Interest rate is one of the critical determinants affecting the exchange rates. Therefore, changes in interest rates are expected to affect the level of exchange rates. When there is an increase in interest rates, foreign capital flow is expected for that particular country. Hence, a decrease in exchange rates is expected for the excess capital flows. This study aims to analyze the relationship between exchange rates and interest rates, considering the last 10 announcements of the interest policy of the Central Bank of the Republic of Turkiye. These announcements are between January 19, 2023 and October 26, 2023. The study used the TL/USD exchange rates and 10-year government bond interest rates to measure the relationship in between these two variables. Methodology-The aim of this study is to analyze the relationship between the dollar exchange rate and government bond interest rates for Turkiye. For this purpose, data is collected for the days when the last 10 policy rates published by the CBRT were announced. Data is obtained investing.com. Vector Autoregression (VAR) is used to measure the relationship in between two variables. The VAR system is based on empirical regularities embedded in the data. The VAR model may be viewed as a system of reduced form equations in which each of the endogenous variables is regressed on its own lagged values and the lagged values of all other variables in the system. Vector Autoregressive models are widely used in time series research to examine the dynamic relationships exist in between variables that interact with one another. In addition, VAR models are viable forecasting tools used often by macroeconomic or policy-making institutions. . In this study first, the stationary levels of the variables are determined by using Unit Root Test. Second, pre-tests of autocorrelation, heteroscedasticity and normality are conducted for the validity of the VAR model. Third, the short-term relationship between variables is tested by using VAR Granger Causality Test. Fourth, VAR analysis is utilized by applying Impulse-Response Analysis and Variance Decomposition Analysis . And finally, the long-term relationship between variables is tested by using Johansen Cointegration Test. Vector Autoregressionmodel is employed in this study. Findings- According to the results of Granger Causality test, government bond interest rates strongly affect the changes of exchange rate. However, there is no causality from exhange rates to interest rates. Therefore, the changes of interest rates are the main determinants of the changes of exchange rates in this short period. The results of Impulse-Response Test show that an unexpected shock (an unexpected increase) in government bond interest rates affects the exchange rates and increases it significantly. More, an unexpected increase in the exchange rates causes the interest rates on government bond to increase. The results of the variance decomposition test show that 50% of the change in the variance of the exchange rates in the first period is explained by changes in bond interest while 30% of the change in the variance of bond interest rates is explained by the changes in exchange rates. The results of Johansen cointegration test support that there is a stable long-term relationship between dollar exchange rates and government bond interest rates. Conclusion-This study focuses on the relationship between government bond interest rates and the dollar exchange rates in Turkiye for the last 10 policy interest rates announcements by Cenral Bank of Turkiye. In summary, the changes in interest rates on bonds affect the changes in exchange rates more. Data for the days that the CBRT issued the last ten policy rates is gathered for this purpose. The association between two variables is measured using Vector Autoregression (VAR). According to overall results, the changes in interest rates on bonds affect the changes in exchange rates more. Keywords: Policy rate, exchange rate, interest rate, Turkiye, Granger Causality, VAR model JEL Codes: E40, E50, C10, C58

Who moved my candy (sugar)? Happiness or money

This study examines the interlinkages among happiness, income and sugar consumption using a short-balanced panel data of 129 countries for the period 2016-2020. Previous studies are mostly country-wise and do not consider their relationships simultaneously. This study generally finds that the direction of causality is from income to happiness, and to sugar consumption. Similar finding is observed for the developing economies. Meanwhile, income does cause happiness for the developed economies. For the economies in transition, it is from happiness to income and to sugar consumption. Both the impulse response and variance decomposition analyses complements these findings. This study is relevant for policy implications especially, to increase the society’s income.

THE EFFECT OF TERMS OF TRADE SHOCKS ON AGRICULTURAL PRODUCT IN IRAQ FOR THE PERIOD (1990 – 2022)

This research was aimed to study and analyze the impact of the shocks of the agricultural net terms of trade (NBTT) on agricultural product (AP) in Iraq during the period (1990 – 2021), which are among the shocks affecting the agricultural sector and foreign trade, the research used quantitative approaches in estimating the levels of impact, including VAR model, variance decomposition analysis (VDA) and impulse response function (IRF). The results showed that VDA of AP was weak in respect of agricultural NBTT and vice versa. While IRF of NBTT was negative in respect of Ap, and the same result was for IRF of AP. The study recommended the need to consider strategies to support smallholder farmers, such as providing access to credit, soft loans, technical assistance and markets for their products, in addition of support the technological advancements for their role in shaping the relationship between agricultural trade and agricultural production.

Interaction analysis of sharia financial institution assets, sharia investment and exports on gross domestic product in Indonesia

IntroductionIn macroeconomics, one of the key indicators influencing a country's economic success is its economic growth. An essential factor in determining a nation's economic growth is the Gross Domestic Product (GDP).ObjectivesThis research aims to analyze the level of interaction between Islamic financial institutions, Sharia investments, and export activities in Indonesia, examining their impact on the Gross Domestic Product (GDP) over the short and long term, spanning the period 2017-2022.MethodThe study employs a quantitative research approach, specifically utilizing the Vector Error Correction Model (VECM) method.ResultsThe results of the Vector Error Correction Model (VECM) reveal two significant variables in the long term. The causality test results indicate a significant relationship between Indonesian exports and Sharia investment. The Variance Decomposition analysis reveals that Sharia Financial Institution Assets contribute 2.7 percent, followed by Sharia Investment at 0.3 percent, and Indonesian Exports at 0.03 percent to the variation in GDP.ImplicationsThis suggests that Indonesian exports significantly impact Sharia investment, and vice versa. However, concerning Gross Domestic Product (GDP), none of the variables exhibit significance. The development of income across various institutions and economic activities in Indonesia indicates a significant increase in the last three years.Originality/NoveltyThese findings provide valuable insights into the economic dynamics of Indonesia, offering a basis for further research and policymaking.

Steering the Intangible Wheel: Chief Executive Officer Effect on Corporate Cultural Change

We provide large-scale empirical evidence of how much chief executive officers (CEOs) change corporate culture. To do this, we use employee reviews to measure corporate cultural change in S&P 1500 firms. In a variance decomposition analysis, we find a modest effect of CEOs on corporate cultural change. The effect of CEOs on cultural change is larger than industry effect but smaller than firm effect. Regression analysis in the context of CEO succession further shows consistent evidence of a modest effect of CEO succession on corporate cultural change. In addition, the relationship between CEOs and cultural change is not likely to be fully explained by time trend, reverse causality, sample selection bias, and omitted variable bias. An investigation into the contextual contingencies of the CEO-cultural change relationship suggests that succession characteristics, such as predecessor influence and turnaround situation, weaken postsuccession cultural change, but industry task environment has a weak moderation effect. Overall, our study contributes to the literature on strategic leadership and corporate culture. Funding: X. Li acknowledges financial support from the Ian Potter (‘93D) PhD Award. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.17210 .

FinTech revolution in mineral management: Exploring the nexus between technology adoption and sustainable Resource utilization in an industry 4.0 context

This study investigates the impact of FinTech adoption on sustainable mineral management policies in Australia within the context of Industry 4.0, using quarterly data from 1990Q1 to 2022Q4. Employing the ARDL-Bounds testing approach, Granger causality analysis, and innovation accounting matrix, the research finds a short-term positive association between FinTech adoption, technological readiness, and green mineral extraction. However, both in the short and long run, investment in sustainable mining technologies, government support for FinTech in mining, and environmental compliance exhibit a negative relationship with resource management. Bidirectional causality is observed between regulatory support for mining FinTech, technological finance solutions, and environmentally conscious mineral practices, while unidirectional causality exists from FinTech adoption to sustainable mining practices. Impulse response functions offer insights into the future impact of variables on eco-conscious mining policies, indicating positive influences from FinTech adoption, government support for FinTech in mining, and technological adaptability over the next decade. Conversely, eco-friendly mining investments, environmental conformity, and social license to operate will impact sustainable mineral utilization. Variance decomposition analysis highlights the most significant shocks on eco-friendly resource management over the next ten years, emphasizing the role of sustainable mining technologies, FinTech adoption, and public support for mining endeavours. In the transition to Industry 4.0, this research provides crucial insights for responsibly utilizing Australia's natural resources by leveraging financial technology and technological readiness.

Impact of Oil Price Shocks and Economic Growth in Nigeria: Evidence from 1990-2021

This research explores the impact of oil price shocks on the Nigerian economy from 1990 to 2021, focusing on Real Gross Domestic Product (RGDP), Exchange Rate (EXR), and Balance of Payments (BOP). Utilising a Vector Autoregressive (VAR) model, the study reveals a positive impact of oil price shocks on RGDP, indication economic expansion during global crude oil price and contraction during decline, such as observed in the 2015 recession. Variance decomposition analysis demonstrate the variability contributions of oil price, exchange rate, and BOP to RGDP, emphasizing the significant role of the oil sector in Nigeria’s economic dynamics Additionally, Granger Causality/Block Exogeneity Tests reveals the directional causality between oil price and the key variables, emphasizing their substantial role in shaping economic and trade dynamics. accelerated economic diversification, investment in other sectors beyond oil, fostering export-led growth, building foreign exchange reserves to reduce the economic vulnerability to fluctuation in global oil prices.

Demographic change effect on ecological footprint: A tripartite study of urbanization, aging population, and environmental mitigation technology

This study examines the impact of disaggregated demographic changes on Russia's sustainable environment improvement and management. With expanded energy resources, high urbanization levels, accelerated industrialization, and tourism growth, Russia has become one of the world's most dynamic economies. Urbanization has accelerated deforestation in Russia and contributed to numerous environmental challenges. Urbanization explains demographic changes in the cities with respect to the increase in population sizes and structures due to rural-urban migration. On this note, this study adopts a population-environment-technology and development model to examine the sustainable environment of Russia. The model is extended to include environmental mitigation technology and financial development to buttress how to decarburise Russia's economy and environment and believes technological innovation and financial facilities via financial inclusion may help reduce environmental stress. Russia's data from 2000 to 2021 with different scientific approaches such as FMOLS, DOLS, CCR, and ARDL are employed to investigate the robust outcomes. Under the basic model, real income, urbanization, FDI and the rule of law significantly contribute to environmental damage. However, environmental mitigation technology (EMT) and financial inclusion perform well to mitigate environmental degradation. Due to the surprising role of EMT, this study also investigates EMT's mediating role on RI, urbanization and foreign direct investment. Consequently, the mediating role significantly contributes to the Russian economy's sustainability. In an extended form, other demographic features (population+15 and population+65) proved detrimental to the environmental development of Russia when analyzed with FMOLS. This is not far from the active engagement of the population in the bracket of the working population on the economic performance and the dependency ratio of the old population on the active and young population for survival. However, this study also employs the granger causality test, IPR and variance decomposition analysis for further validation. Following the outcomes, this study suggests some imperative implications to attain the desired sustainable level.

On the effectiveness of the interest rate channel within inflation targeting in Ukraine: a VAR approach

Assessing the effectiveness of the inflation targeting framework via the interest rate channel remains crucial in the current monetary policy debate. For Ukraine, the relevance of this discussion is enhanced by the adoption by the National Bank of a rigid inflation targeting policy since 2016, as well as by the challenges of price stability during war. The aim of the study is to identify how the discount rate affects the money market rates and how this affects inflation in Ukraine. Employing a VAR model on monthly data spanning 2016 – Q1 2022, the analysis demonstrates weak empirical evidence for the interest rate channel effectiveness. The impulse response indicates that the discount rate’s initial effect does not provide long-term inflation dynamics control. Variance decomposition analysis highlights the minimal influence of the NBU’s discount rate, primarily evident in the refinancing rate, followed by its impact on the rate of term deposits made by individuals, followed by the inflation, followed by the rate of new loans granted to residents, and finally the rate of government bond yields. Addressing the limitations of a rigid inflation targeting approach, the study recommends adopting a balanced approach, considering both price stability supported by exchange rate control measures and fostering economic growth. Additionally, a viable strategy for deepening the financial sector should be developed.