Variance Decomposition Analysis Research Articles

International attractiveness of cities: The drivers of urban FDI

AbstractForeign direct investment (FDI) nowadays mostly corresponds to activities in urban areas. This study provides the first global analysis of the determinants of urban FDI (UFDI). We assemble and exploit a unique database on the location choices of multinational enterprises in urban areas worldwide to investigate the relevance of local, national, and dynamic drivers of cities' international attractiveness. We find that attractive cities are large, productive, endowed with transport infrastructure able to move goods and people within and beyond their borders, abundant in educated people, and embedded in attractive national spaces. Cities combining all these attributes tend to attract service activities. Attractiveness is persistent but can evolve depending on economic dynamism. A variance decomposition analysis indicates that the location choices of UFDI are mainly driven by city‐level determinants, notably transport infrastructure.

Plankton Community Stability and Drive Factor Identification Guizhou Plateau Reservoir

In order to understand the stability of the zooplankton and phytoplankton communities in the Guizhou plateau reservoir environment, the process of reservoir water quality change affecting the stability of plankton was studied. The changes in the plankton community and water quality in three different nutrient reservoirs （Huaxi Reservoir, Goupitan Reservoir, and Hailong Reservoir） were studied from October 2020 to August 2021. The stability of the zooplankton and phytoplankton communities was studied using time-lag analysis （TLA）. Variance decomposition analysis （VPA） was used to explore the response of the two communities to environmental changes. The driving factors of plankton community changes in reservoirs were also revealed. The results showed that Huaxi Reservoir and Goupitan Reservoir were mesotrophic reservoirs, and Hailong Reservoir was a eutrophic reservoir. The average comprehensive nutrition indices of the three reservoirs were 44.07, 44.68, and 50.25. A total of 51 species of zooplankton rotifers, 39 species of rotifers, three species of copepods, and nine species of cladocera were identified. Among them, the abundance of rotifers was the highest, accounting for 85.96%. A total of seven phyla and 73 species of phytoplankton were identified, including 16 species in the phylum Cyanophyta, 32 species in the phylum Chlorophyta, 16 species in the phylum Diatoma, three species in the phylum Chlorophyta, four species in the phylum Euglenophyta, and one species each in the phyla Cryptophyta and Chrysophyta. Among them, the abundance of cyanobacteria and diatoms was the highest, accounting for 66.2% and 27.35%, respectively. The median absolute deviation （MAD） of the Bray-Curtis distance of zooplankton and phytoplankton community in the three reservoirs were 0.67 and 0.65 in Huaxi Reservoir, 0.80 and 0.69 in Goupitan Reservoir, and 0.85 and 0.47 in Hailong Reservoir, respectively. The larger the value, the greater the variation in the community. The absolute value of the slope of zooplankton was greater than that of phytoplankton in the TLA results, and the absolute values of the slopes were 0.018 and 0.004, respectively. The larger the absolute value of the slope, the faster the community variability. The zooplankton community in the three reservoirs was less stable than the phytoplankton community and more sensitive to environmental changes, and the degree of variation was greater. The higher the degree of eutrophication of the reservoir, the more obvious this phenomenon. VPA showed that the changes in plankton communities in Huaxi Reservoir and Hailong Reservoir were mainly influenced by water temperature and eutrophication factors. The changes in planktonic community in Goupitan Reservoir were mainly influenced by water temperature and chemical factors. The driving factors of Huaxi Reservoir were water temperature, TP, permanganate index, and SD. The driving factors of Goupitan Reservoir were water temperature, NO3-- N, and pH. The driving factors of Hailong Reservoir were water temperature and TP. Nutrients and water temperature were the main factors affecting the stability of plankton communities in reservoirs.

Sectoral Composition of Outward FDI and its Impact on Export Diversification in Saudi Arabia

This research explores the relationship between outward foreign direct investment (FDI) and export diversification in emerging economies, with a focus on the case study of Saudi Arabia. Utilizing a multi-equation model and vector autoregression analysis, the study investigates various factors such as the level and sectoral composition of outward FDI, income levels, institutional factors, and regional trade agreements. The findings suggest that while outward FDI can influence export diversification, its impact varies based on factors such as the sectoral distribution of investments, income levels, and institutional quality. Additionally, cointegration and vector error correction models reveal long-term relationships among key variables, highlighting the complex interplay between economic development, governance, and investment patterns. Impulse response analysis sheds light on the dynamic nature of shocks and their effects on the economy over time, emphasizing the interconnectedness of variables. Variance decomposition analysis further elucidates the significance of shocks in explaining fluctuations in model variables, underscoring the systemic interdependencies at play. Overall, the study provides valuable insights into the mechanisms through which outward FDI influences export diversification and economic development in emerging economies like Saudi Arabia and institutional reforms are essential; strengthening institutions and improving regulatory frameworks can amplify the positive effects of outward FDI on export diversification.

Research on the Linkage between RMB Interest Rate and Exchange Rate

This study uses a vector autoregressive (VAR) model to investigate the relationship between the exchange rate (EX) and RMB interest rate (I). The research starts with a review of pertinent theoretical frameworks and then conducts an empirical analysis with historical data to determine the long-term interactions between these two macroeconomic factors in the Chinese market. The empirical findings demonstrate the extent to which the I affects EX as well as the reciprocal relationship between EX and I The stability of the linkage is further explored using variance decomposition and impulse response analysis. In addition to offering policymakers a foundation for regulating monetary policy, this study offers a fresh viewpoint on the macroeconomic dynamics of China.

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.

Analisis Kebijakan Moneter melalui Jalur Harga Aset di Negara Muslim Berkembang

This research aims to analyze the contribution of variables from the interaction of monetary policy transmission variables (SBI, STOCK, INVESTMENT, GDP, INFLATION). This research uses secondary data or time series, namely from the first quarter of 2011 to the first quarter of 2016. The data analysis model in this research is Vector Autoregression (VAR) model and sharpened with Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) analysis. The results of the VAR analysis show that the past variable (t-1) contributes to the current variable both the variable itself and other variables and from the estimation results it turns out that there is a reciprocal relationship between the variables where all the variables are monetary policy transmission variables (SBI, STOCK, INVESTMENT, GDP, INFLATION) contribute to each other. The results of the IRF analysis show that the stability of the response of all variables is formed in period 5 or the medium and long term, where the response of other variables to changes in one variable shows different variations from positive to negative responses or vice versa, and there are variables whose responses remain positive or remains negative from the short term to the long term. The results of the FEVD analysis show that there are variables that have the largest contribution to the variable itself in the short, medium and long term, such as GDP and SB. Meanwhile, other variables that have the greatest influence on the variable itself in the short, medium and long term are GDP which is most influenced by INVESTMENT, Inflation is most influenced by GDP, INVESTMENT is most influenced by Inflation, SB is most influenced by INVESTMENT while it is most influenced by SB. The results of the analysis of the interaction of each monetary policy transmission variable in the path of asset prices in developing Muslim countries in the short, medium and long term show that monetary policy transmission can increase economic growth and achieve the inflation targets of developing Muslim countries.

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.