Variance Decomposition Research Articles

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.

Rootstock influences on growth and bearing precocity in juvenile cocoa

Within the cocoa germplasm used for variety development in West Africa, knowledge of varieties that are suitable as rootstocks to promote growth and precocity of clones is lacking. Six cocoa varieties were used to generate 36 clones for evaluation. For each of the six scions selected as ortets from previous progeny trials, the corresponding seed-derived family was regenerated through manual pollinations. The scions were grafted in a full diallel fashion on each of the six seed-derived family, and field evaluated in four replications following a randomized complete block design. Also, seed-derived varieties were evaluated in the nursery for vigour characteristics, and under similar conditions as the 36 clones in the field. Genotype heterozygosity at 72 SNP loci was significantly correlated with vigour characteristics of the six varieties. Seed-derived varieties of high vigour benefited clone performance for vigour, and dry bean yield when used as rootstocks but not as scion. The maximum photochemical efficiency of photosystem II (Fv/Fm) was different among seed-derived varieties and clones in the dry season but not the rainy season. Among clones, higher Fv/Fm values were associated with higher-vigour rootstock varieties. Diallel grafting model enabled partitioning of total variance into several sub-components. The variance due to specific compatibility ability was not significant for any of the measured traits. Decomposition of the reciprocal variance revealed that significant reciprocal effects were mainly due to significant maternal (rootstock) effects. The key finding is that, for any scion, the most precocious clone is obtained by grafting on high vigour rootstock varieties.

External financial and monetary policy shocks: Do they matter for Korean macroeconomy?

Given Korea's status as a small, open economy, it exhibits a pronounced sensitivity to external shocks. Consequently, this article seeks to elucidate the impact of external financial and monetary policy shocks on the fluctuation of critical macroeconomic variables within Korea. Employing Bayesian estimation alongside the impulse response function for empirical analysis, the findings reveal that external financial and monetary policy shocks precipitate declines in exports, output, employment, real wages, consumption, investment, and imports. Conversely, these shocks are associated with increases in both the price level and inflation, highlighting the multifaceted effects of external pressures on the domestic economic landscape. Further, through forecast error variance decomposition, this study demonstrates that, relative to shocks stemming from productivity, terms of trade, and real exchange rate variations, external financial and monetary policy shocks exert a considerably milder impact on the fluctuations of Korea's key macroeconomic variables. This insight suggests a potential area for enhancement in the existing Korean literature on this topic, advocating for the integration of these findings to enrich understanding and analysis. In summary, by delving into the nuanced effects of external shocks on Korea's economy, this article contributes valuable perspectives to the discourse, suggesting avenues for further research and policy formulation. The integration of these results into the broader body of Korean economic literature could significantly augment current understandings and interpretations of Korea's economic dynamics in the face of global financial and monetary turbulence.

Surface water quality index forecasting using multivariate complementing approach reinforced with locally weighted linear regression model.

River water quality management and monitoring are essential responsibilities for communities near rivers. Government decision-makers should monitor important quality factors like temperature, dissolved oxygen (DO), pH, and biochemical oxygen demand (BOD). Among water quality parameters, the BOD throughout 5 days is an important index that must be detected by devoting a significant amount of time and effort, which is a source of significant concern in both academic and commercial settings. The traditional experimental and statistical methods cannot give enough accuracy or solve the problem for a long time to detect something. This study used a unique hybrid model called MVMD-LWLR, which introduced an innovative method for forecasting BOD in the Klang River, Malaysia. The hybrid model combines a locally weighted linear regression (LWLR) model with a wavelet-based kernel function, along with multivariate variational mode decomposition (MVMD) for the decomposition of input variables. In addition, categorical boosting (Catboost) feature selection was used to discover and extract significant input variables. This combination of MVMD-LWLR and Catboost is the first use of such a complete model for predicting BOD levels in the given river environment. In addition, an optimization process was used to improve the performance of the model. This process utilized the gradient-based optimization (GBO) approach to fine-tune the parameters and better the overall accuracy of predicting BOD levels. To assess the robustness of the proposed method, we compared it to other popular models such as kernel ridge (KRidge) regression, LASSO, elastic net, and gaussian process regression (GPR). Several metrics, comprising root-mean-square error (RMSE), R (correlation coefficient), U95% (uncertainty coefficient at 95% level), and NSE (Nash-Sutcliffe efficiency), as well as visual interpretation, were used to evaluate the predictive efficacy of hybrid models. Extensive testing revealed that, in forecasting the BOD parameter, the MVMD-LWLR model outperformed its competitors. Consequently, for BOD forecasting, the suggested MVMD-LWLR optimized with the GBO algorithm yields encouraging and reliable results, with increased forecasting accuracy and minimal error.

Economies of scale in Saudi Arabia's refining sector: An application of modern econometric models

This paper aims to reveal how the refining industry's inputs in Saudi Arabia affect its output and to forecast refining industry dynamics. The variables used in this paper are the refined petroleum products representing the dependent variable, with natural gas liquids, crude oil, labor, and capital acting as explanatory variables covering the period 1990–2020. The long run cointegration of the variables was observed. An error correction model utilizing the Cobb–Douglas production function framework was performed. Furthermore, this study applied the vector autoregressive model (VAR) and its diagnosis tests, including forecast-error variance decomposition (FEVD) and impulse response functions (IRFs).The results indicate that natural gas liquids and crude oil have a significant influence on the refining industry's output. Although capital and labor are significant determinants of output, they do not contribute significantly to output creation in the refining industry. This might be related to some parts of the capital and human resources being directed toward supporting activities, such as administration, technical support, maintenance, transportation, logistics and assigning third-party contractors to perform the main duties related to the production process. Additionally, the petroleum refining industry requires substantial capital resources for construction and maintenance. Thus, the actual measurement of capital input's influence on output was observed in the long run. The results reveal that the refining industry's variation is influenced by both its own characteristics and that natural gas liquid, crude oil, capital, and labor factors have a significant impact on the accuracy of industry forecasts.This study concludes that Saudi Arabia's petroleum refining industry operates under decreasing returns to scale, while the shocks in the refining industry are influenced and caused by external factors.

Estimating Posterior Sensitivities with Application to Structural Analysis of Bayesian Vector Autoregressions

The inherent feature of Bayesian empirical analysis is the dependence of posterior inference on prior parameters, which researchers typically specify. However, quantifying the magnitude of this dependence remains difficult. This article extends Infinitesimal Perturbation Analysis, widely used in classical simulation, to compute asymptotically unbiased and consistent sensitivities of posterior statistics with respect to prior parameters from Markov chain Monte Carlo inference via Gibbs sampling. The method demonstrates the possibility of efficiently computing the complete set of prior sensitivities for a wide range of posterior statistics, alongside the estimation algorithm using Automatic Differentiation. The method’s application is exemplified in Bayesian Vector Autoregression analysis of fiscal policy in U.S. macroeconomic time series data. The analysis assesses the sensitivities of posterior estimates, including the Impulse response functions and Forecast error variance decompositions, to prior parameters under common Minnesota shrinkage priors. The findings illuminate the significant and intricate influence of prior specification on the posterior distribution. This effect is particularly notable in crucial posterior statistics, such as the substantial absolute eigenvalue of the companion matrix, ultimately shaping the structural analysis.

A new unsymmetrical decomposition of the damage variable

This work focuses on the dissection of the damage variable within solid materials. The underlying assumption is that damage within a solid primarily stems from the presence of various defects. The conventional approach to breaking down the damage variable into two parts – one attributed to the first defect type and the other to the second defect type – is both explored and expanded in a coherent mathematical manner. Within this context, a novel and asymmetric dissection of the damage variable is formulated. This fresh asymmetrical approach presents an alternative to the traditional symmetric dissection of the damage variable. Initially, the dissection considerations are carried out in a one-dimensional context using scalar values. However, this methodology is subsequently generalized employing tensors. In the end, an illustrative example is demonstrated.

Contribution of climatic variables and their interactions to reference evapotranspiration changes considering freeze-thaw cycles in the Tibetan Plateau during 1960–2022

While the direct impact of climate change on reference evapotranspiration (ET0) has been extensively studied, there is limited research on the indirect impact resulting from the interaction between climatic variables. This gap hinders a comprehensive understanding of climate change effects on ET0. Additionally, there is scarce exploration into the quantitative effect of freeze-thaw cycles on ET0 variation. In this study, we employed path analysis and dependent variable variance decomposition methods to discern the direct and interactive effects of climatic variables on ET0 in the Tibetan Plateau from 1960 to 2022. Annual ET0 exhibited variation across basins, with the coefficient of variability during the thawed period smaller than that during the non-thawed period. On an annual scale, the largest contribution to ET0 variation came from water vapor pressure deficit (VPD) at 47.7%. This contribution was amplified by its coupled interaction with temperature (T) at 47.1%, although the contribution was partially offset by the interactive effects of VPD with downward shortwave radiation and wind speed at −2.4% and − 27.6%, respectively. During different freezing-thawing periods, VPD primarily controlled ET0 variation, with its interaction with other climatic variables enhancing its impact. Furthermore, soil moisture, influenced by freeze-thaw cycles, exhibited a strong correlation with T and VPD, indicating the significant effect of freeze-thaw cycles on ET0 variation. The weak correlation between ET0 and NDVI suggested that vegetation growth had a limited regulatory effect on ET0. These findings provide valuable insights into the impact of interactions between climatic variables on hydrological processes, enhancing our understanding of the interactive roles of hydrometeorological variables.

Empirical analysis of the development relationship between Shanghai shipping industry and primary industry based on VAR model

This study employs the Vector Autoregression (VAR) model to conduct an in-depth empirical analysis of the dynamic relationship between Shanghai’s shipping industry and the development of the primary sector from 2002 to 2022. The findings reveal that, as a globally significant maritime hub, the development of Shanghai’s shipping industry has a notable positive impact on the growth of the primary sector, particularly in agriculture and fisheries. Through ADF unit root tests, cointegration tests, and Granger causality tests, a stable long-term relationship was identified between shipping industry indicators, such as container throughput and total waterborne cargo transport volume, and the output value of the primary sector. Impulse response analysis and variance decomposition further reveal the critical roles and mechanisms through which the shipping industry influences the development of the primary sector. Based on these insights, the paper proposes policy recommendations aimed at strengthening the integrated development of shipping and the primary sector, promoting technological innovation in the shipping industry, optimizing the policy environment, enhancing environmental protection measures, and bolstering international cooperation. These strategies are intended to foster synergistic development between the two sectors and drive sustainable economic growth.

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.

Variance Decomposition and Cryptocurrency Return Prediction

Abstract This article examines how realized variances predict cryptocurrency returns in the cross section using intraday data. We find that cryptocurrencies with higher variances exhibit lower returns in subsequent weeks. Decomposing total variances into signed jump and jump-robust variances reveals that the negative predictability is attributable to positive jump and jump-robust variances. The negative pricing effect is more pronounced for smaller cryptocurrencies with lower prices, less liquidity, more retail trading activities, and more positive sentiment. Our results suggest that cryptocurrency markets are unique because retail investors and preferences for lottery-like payoffs play important roles in the partial variance effects.

Study on advanced partial least squares for quality-related fault detection

The issue of quality-related fault detection in the industrial process has attracted much attention in recent years. The partial least squares (PLS) is considered an efficient tool for predicting and monitoring. The modified partial least squares (MPLS) is an extended algorithm for solving the oblique decomposition of PLS, however, the study indicated that the loss of quality variable information may affect the prediction of quality information in the decomposition process of the MPLS algorithm. Furthermore, the detection rate of traditional statistics and static control limit is low, and the existing dynamic control limit has certain limitations. Therefore, a new PLS space-decomposition algorithm called advanced partial least squares (APLS) is proposed. APLS avoids the loss of quality information by orthogonal decomposition of process variables according to their relationship with quality. APLS has a more accurate prediction of quality when process variables contain more noise; the fault false alarm rates (FAR) of quality-related faults are reduced by using the new statistics and thresholds combined with local information increment technology in the process variable principal component subspace. Finally, the effectiveness of the proposed approach is verified by a numerical example and an industrial benchmark problem.

Climate change and crop production nexus: assessing the role of technological development for sustainable agriculture in Vietnam

PurposeThis study aims to examine the effects of climate change and agricultural technologies on crop production in Vietnam for the period 1990–2018.Design/methodology/approachSeveral econometric techniques – such as the augmented Dickey–Fuller, Phillips–Perron, the autoregressive distributed lag (ARDL) bounds test, variance decomposition method (VDM) and impulse response function (IRF) are used for the empirical analysis.FindingsThe results of the ARDL bounds test confirm the significant dynamic relationship among the variables under consideration, with a significance level of 1%. The primary findings indicate that the average annual temperature exerts a negative influence on crop yield, both in the short term and in the long term. The utilization of fertilizer has been found to augment crop productivity, whereas the application of pesticides has demonstrated the potential to raise crop production in the short term. Moreover, both the expansion of cultivated land and the utilization of energy resources have played significant roles in enhancing agricultural output across both in the short term and in the long term. Furthermore, the robustness outcomes also validate the statistical importance of the factors examined in the context of Vietnam.Research limitations/implicationsThis study provides persuasive evidence for policymakers to emphasize advancements in intensive agriculture as a means to mitigate the impacts of climate change. In the research, the authors use average annual temperature as a surrogate measure for climate change, while using fertilizer and pesticide usage as surrogate indicators for agricultural technologies. Future research can concentrate on the impact of ICT, climate change (specifically pertaining to maximum temperature, minimum temperature and precipitation), and agricultural technological improvements that have an impact on cereal production.Originality/valueTo the best of the authors’ knowledge, this study is the first to examine how climate change and technology effect crop output in Vietnam from 1990 to 2018. Various econometrics tools, such as ARDL modeling, VDM and IRF, are used for estimation.

Exploring determinants impacting foreign direct investment in the real estate sector: a study on the Indian economy

PurposeForeign direct investment in the real estate (FDIRE) sector is required to bridge the gap between investment needed and domestic funds. Further, foreign direct investment is gaining importance because other sources of raising finance such as External Commercial Borrowing and foreign currency convertible bonds have been banned in the Indian real estate sector. Therefore, the objective of the study is to explore the determinants attracting foreign direct investment in real estate and to assess the impact of those variables on foreign direct investments in real estate.Design/methodology/approachJohansen cointegration test, vector error correction model along with variance decomposition and impulse response function are employed to understand the nexus of the relationship between various macroeconomic variables and foreign direct investment in real estate.FindingsThe results indicate that infrastructure, GDP and tourism act as drivers of foreign direct investment in real estate. However, interest rates act as a barrier.Originality/valueThis article aimed at exploring factors attracting FDIRE along with estimating the impact of identified variables on FDI in real estate. Unlike other studies, this study considers FDI in real estate instead of foreign real estate investments.

Financial connectedness in BRICS: Quantile effects and BRICS SUMMIT impacts

This study examines the financial connectedness among BRICS using a two-step framework. In the first step, financial indices for each BRICS country are estimated by dynamic factor models in which three market characteristics and six sub-financial markets are considered. The estimated financial indices provide more comprehensive pictures of the financial markets in BRICS countries. Then we use the quantile variance decomposition model to discover the financial connectedness in BRICS. The study finds significant heterogeneity in financial connectedness among BRICS countries across quantile levels, with the tail spillover effects being stronger than the mean spillover effects. Russia is a risk transmitter while India is a recipient of risk during the crisis. We also find that the BRICS SUMMIT significantly strengthens the financial connectedness in the BRICS. The study provides international investors with new portfolio strategies and a risk-hedging perspective.

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.

An analysis of long-term and short-term impact of climate change on rice production in India

PurposeClimate change-related weather events significantly affect rice production. In this paper, we investigate the impact of and interrelationships between agriculture inputs, climate change factors and financial variables on rice production in India from 1970–2021.Design/methodology/approachThis study is based on the time series analysis; the unit root test has been employed to unveil the integration order. Further, the study used various econometric techniques, including vector autoregression estimates (VAR), cointegration test, autoregressive distributed lag (ARDL) model and diagnostic test for ARDL, fully modified least squares (FMOLS), canonical cointegrating regression (CCR), impulse response functions (IRF) and the variance decomposition method (VDM) to validate the long- and short-term impacts of climate change on rice production in India of the scrutinized variables.FindingsThe study's findings revealed that the rice area, precipitation and maximum temperature have a significant and positive impact on rice production in the short run. In the long run, rice area (ß = 1.162), pesticide consumption (ß = 0.089) and domestic credit to private sector (ß = 0.068) have a positive and significant impact on rice production. The results show that minimum temperature and direct institutional credit for agriculture have a significant but negative impact on rice production in the short run. Minimum temperature, pesticide consumption, domestic credit to the private sector and direct institutional credit for agriculture have a negative and significant impact on rice production in the long run.Originality/valueThe present study makes valuable and original contributions to the literature by examining the short- and long-term impacts of climate change on rice production in India over 1970–2021. To the best of the authors’ knowledge, The majority of the studies examined the impact of climate change on rice production with the consideration of only “mean temperature” as one of the climatic variables, while in the present study, the authors have considered both minimum as well as maximum temperature. Furthermore, the authors also considered the financial variables in the model.

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.

The relationship between the inflation rate and the unemployment rate in Poland and their long-term associations with selected macroeconomic variables

The purpose of this paper is to examine the relationship between the inflation and unemployment rates in Poland and to analyze their long-term relationships with selected macroeconomic variables: the WIG20 index, consisting of the 20 largest Polish companies noted on Warsaw Stock Exchange, the USD/PLN exchange rate, the Brent crude oil index, and the interest rate on 10-year government bond yields. The main objective of the study is to determine the relationship between inflation and unemployment. In this study, a vector error correction model (VECM) was used to study long-run dependence. The impulse response function and forecast error variance decomposition were also used to examine the interactions between variables. There is one long-run relationship between the factors studied. Both the values of the VECM model parameters and the results of the impulse response function indicate that there is a negative relationship between inflation and unemployment in the short term. In the long term, there is a positive relationship, resulting in the stagflation phenomenon.