Variance Decomposition Research Articles

Analisis Tren Ekonomi Digital terhadap Kebijakan Moneter di Indonesia

This study aims to determine the effect of fintech, the amount of money in circulation, interest rates and economic growth on the analysis of digital economic trends on monetary policy in Indonesia. There are four variables in this study, namely fintech, the amount of money in circulation, interest rates and economic growth. The analysis method used is Vector Autoregression with the Impluse Response Function test or abbreviated as IRF and the Forecast Error Variance Decomposition test commonly abbreviated as FEVD, stationarity test, cointegration test, lag structure stability test and optimal lag length test. There is a contribution from each variable to the variable itself and other variables, according to the results of the Vector Autoregression study with a lag basis of 2. In addition, the results of the Vector Autoregression analysis show that the past variable (t-1) contributes to the current variable both to the variable itself and to other variables. The results of the analysis show that there is a reciprocal relationship between the variables. By using response function analysis, we can see if there is a response from other variables to changes in one variable in the short, medium, or long term. In addition, we know that the stability of all variables is formed in the short, medium, and long term. According to the Variance Decomposition Analysis, factors such as Fintech and Money Supply contribute the most to the variable itself. However, other variables that have the greatest influence on the variable itself and are supported by other variables in the short, medium, and long term are economic growth and interest rates are most influenced by Fintech.

Understanding the interconnectedness of house prices between cities and regions during COVID-19: evidence from Australia

ABSTRACT While several studies analyse house price linkages within cities or regions, none estimates the impact of the COVID-19 pandemic on housing price dynamics between cities and regions. We fill this gap in the literature by examining a network of core and regional cities in Australia using Diebold–Yilmaz’s network variance decomposition approach of 2012. Compared with the pre-pandemic period, the pandemic caused significant change, with core cities significantly linked to regional cities and driving volatility in housing prices. The findings of our study have important implications for real estate investors as well as urban and regional planners.

The Impact of Exchange Rate Volatility on The Inflation Rate: Evidence from Turkey

Exchange rate volatility can directly impact the general price level by causing changes in the costs of imported goods and services. In developing economies such as Turkey, sensitivity to exchange rate movements can be more pronounced due to a high dependence on imports and external financing sources. In this study, the relationship between variables was analyzed within the framework of the VAR model using the logarithmic values of monthly data spanning from January 2003 to May 2024. The cointegration test revealed that the variables are cointegrated in the long run and do not move independently of each other. The Granger causality test found a one-way causality at the 1% significance level, where M1 money supply affects both the exchange rate and inflation, and inflation affects the exchange rate. Based on the results obtained from the VAR models, variance decomposition analysis indicates that in Turkey, the primary driver of inflation is changes in the M1 money supply. Impulse response functions reveal that the effect of inflation on the exchange rate lasts for eight periods and ceases after the eighth period. In contrast, the response of inflation to M1 money supply persists for ten periods, exhibiting a slight increasing and decreasing trend after the seventh period. Therefore, in the formulation of economic policies, the effects of exchange rate and M1 money supply on inflation should be carefully analyzed and appropriate strategies should be determined.

Effects of Different Fertilization Treatments on Bacterial Community and Citrus Quality in Yellow Soils of the Yunnan-Guizhou Plateau

To investigate the intrinsic driving mechanism of citrus yield and quality enhancement under different fertilizer applications, a field experiment was conducted to study the effects of biochar （SW）, organic fertilizer （YJ）, farmyard manure （NJ）, chemical fertilizer （HF）, and no fertilizer as the control （CK） on soil physical and chemical properties, bacterial community characteristics, and citrus quality of citrus orchards in the yellow soil area of the Yunnan-Guizhou Plateau. The results showed that compared with those in the CK treatment, the yield, single fruit weight, edible rate, juice rate, vitamin C content, and soluble solids of citrus increased under the different fertilization treatments. In contrast, the titratable acid content of citrus decreased, resulting in an increase in the solid-acid ratio of citrus. Among the different fertilization treatments, the most significant effective treatment was SW. The SW and YJ treatments significantly increased the soil pH value, while the HF treatment decreased soil pH. Different fertilization treatments could increase the contents of soil organic matter, hydrolyzable nitrogen, available phosphorus, and available potassium. The SW treatment exhibited the most obvious effect on the contents of soil hydrolyzable nitrogen, available phosphorus, and available potassium. The YJ treatment exhibited the most obvious effect on the soil organic matter content. The different fertilization treatments significantly affected soil bacterial community diversity and community structure, among which the SW treatment significantly increased the soil Chao1 index, observed species index, and Shannon index； optimized soil bacterial community structure； and made the bacterial symbiotic network simple and stable. By contrast, the HF treatment significantly decreased the soil Chao1 index, observed species index, and Simpson index and had less effect on bacterial community structure. Redundancy analysis showed that soil available potassium （96.47%）, Chao1 index （73.80%）, and Chujaibacter （55.92%） were the key factors in improving citrus yield and quality. Variance decomposition analysis indicated that the soil bacterial community structure was the largest contributor （17.00%） to the improvement in citrus yield and quality. In conclusion, fertilization can effectively improve soil physical and chemical properties, increase soil nutrient supply level, optimize bacterial community structure, and improve citrus yield and quality. Of these, biochar can be used as a priority for the fertilization and improvement of the soil in citrus orchards in the yellow soil area of the Yunnan-Guizhou Plateau.

Inferring flow energy, space scales, and timescales: freely drifting vs. fixed-point observations

Abstract. A novel method for the inference of spatiotemporal decomposition of oceanic surface flow variability is presented and its performance assessed in a synthetic idealized configuration with horizontally divergentless flow. Inference methodology is designed for observations of surface velocity. The ability of networks of surface drifters and moorings to infer the spatiotemporal scales of surface ocean flow variability is quantified. The sensitivity of inference performance for both types of platforms to the number of observations, geometrical configurations, and flow regimes is presented. As drifters simultaneously sample spatial and temporal variability, they are shown to be able to capture both spatial and temporal flow properties even when deployed in isolation. Moorings are particularly adept for the characterization of the flow's temporal variability and may also capture spatial scales provided they are deployed as arrays. In particular, we show that our method correctly identifies whether drifters are preferentially sampling spatial vs. temporal variability. Pending further developments, this method opens novel avenues for the analysis of existing datasets as well as the design of future experimental campaigns targeting the characterization of small-scale (e.g., <100 km) ocean variability.

Rainfall and Soil Moisture Jointly Drive Differences in Plant Community Composition in Desert Riparian Forests of Northwest China

Extreme rainfall and soil moisture play important roles in the survival, community composition, and ecosystem function of desert plants. This study focused on arid desert riparian forests ecosystems in the Ebinur Lake Basin of Xinjiang, China. We analyzed the effects of rainfall and soil moisture on species composition, indicator species, β diversity, species turnover, and nestedness using three consecutive years of community surveys. A zero-model combined with a Bayesian framework was used to explore the response of species turnover and nestedness to soil moisture and rainfall, and variance decomposition was used to quantify the relative importance of spatial distance, rainfall, and soil factors in determining species composition. The results indicated the following: (1) when rainfall was high, the richness and abundance of annual herbaceous plants increased. The proportion of the community based on richness (32%) and abundance (58.1%) of annual herbaceous plants in 2016 was higher than that in 2015 and 2017. The Jaccard, Bray–Curtis, and Chao indexes of the community in years with higher rainfall were significantly higher than in years with lower rainfall; however, a lag effect was also observed. (2) Soil factors explained 5% of the changes in community composition, rainfall explained 12% of the changes in community composition, and spatial distance, soil factors, and rainfall jointly explained 32% of the changes in community composition. (3) We also showed that high soil moisture leads to greater β diversity than low soil moisture. Rainfall had the greatest explanatory power on the measured values of β diversity (19.6%) and species turnover (38%), and the factor with the greatest explanatory power for species nestedness was the interaction between rainfall and soil moisture (26.2%). Our findings indicate that drought and rainfall drive differences in plant community composition, with rainfall playing a dominant role. These results provide a basis for understanding the impact of extreme rainfall events on arid ecosystem functions.

Infrastructure Development And External Financing Nexus In Sub-Saharan Africa: Panel Vecm Analysis

Abstract Research background It is evident that relying solely on domestic financing cannot bridge the infrastructure gap in the Sub-Saharan African (SSA) region. Therefore, exploring alternative sources of financing becomes crucial to complement domestic efforts and address the infrastructure needs effectively. Purpose This study investigates the relationship between infrastructure development and external financing factors (foreign direct investment – FDI, private participation in infrastructure – PPI, and official development assistance – ODA) by considering the short- and long-run equilibrium and their causal direction. Research methodology These objectives are achieved by employing the panel vector error correction mechanism (PVECM), impulse response functions (IRF), variance decomposition analysis (VDCs), and VAR causality on a dataset covering 43 SSA countries from 2000 to 2021. Results The results reveal the presence of feedback causal relationships between infrastructure development and external financing factors, and PVECM results demonstrate that only private participation in infrastructure has a positive influence on infrastructure development both in the long and short-run, while ODA and FDI only influence infrastructure development in the long- run. Novelty The current study contributes to the existing literature by examining the impact of external financing factors on infrastructure development, with a specific focus on SSA, by considering the three major external financing factors (PPI, FDI, and ODA) as the determinants of infrastructure.

Global Transmission Shocks of U.S. Monetary Policy on China's Housing Prices

The paper demonstrates that the monetary policy of the United States has important spillover effects to China. Based on the monthly data from January 2016 to April 2024, this paper establishes a VAR model to analyze the influence of U.S. monetary policy on China's real estate prices and its transmission channels, including interest rate, exchange rate, consumer confidence, and asset price. The results show that the impact of the Federal Funds Effective Rate on housing prices is very quick, reaching a maximum in the first month and continuing to have a positive impact over the long term. The Federal Funds Effective Rate and China's Loan Prime Rate have a positive impact on housing prices in the early stage, but then there is a change in impact direction. The Exchange Rate, Consumer Confidence Index and CSI 300 index all had positive spillover effects on housing prices in the early days. The variance decomposition analysis shows that the exchange rate channel plays the most significant role in causing China’s real estate price fluctuations.

The Path to Sustainable Stability: Can ESG Investing Mitigate the Spillover Effects of Risk in China’s Financial Markets?

In the context of a low-carbon economic transition and escalating uncertainties in financial markets, understanding the relationship between the long-term benefits of ESG (Environmental, Social, and Governance) investments and the stability of China’s financial markets emerges as a critical issue. This paper analyzes the risk contagion mechanisms within China’s financial system from the perspective of volatility spillovers associated with ESG investments. Initially, the study employs the Time-Varying Parameter Vector Autoregression (TVP-VAR) model to calculate the variance decomposition spillover index, contrasting the dynamics and risk transmission mechanisms of market volatility between portfolios composed of ESG and conventional stocks. Building upon the analysis of risk spillover relations among financial sub-markets, the study utilizes the generalized forecast error variance decomposition method to construct a complex network of financial system risk spillovers, investigating the risk contagion characteristics within both financial systems through network topology. Empirical findings indicate a significant reduction in the risk and net spillover effects of China’s financial system when ESG stock indices replace conventional stock indices, with a notable mutation in the volatility spillover network structure during extreme risk events and even more substantial changes during the COVID-19 pandemic. Furthermore, based on volatility spillover analysis, the study computes optimal weights and hedging strategies for portfolios incorporating the ESG volatility index and other market volatility indices. The conclusions of this research are instrumental for regulatory authorities in establishing early warning mechanisms and for investors in avoiding financial investment risks.

Do spillovers between international economic policy uncertainty be time-varying and asymmetric?

This study uses generalized impulse response, generalized variance decomposition, and quantile VAR model to analyze the spillover effects of EPUs among the United States, Japan, mainland China, and Hong Kong, China. This study analyzes for the first time the correlation between developed and emerging economies under different EPU conditions, and the results show that the correlation is stronger in the extreme case than in the normal case. This study finds that the international spillover effect between EPUs is significant and asymmetric. This asymmetric relationship is not only between pairwise countries (regions), but also between the left and right tails of EPUs. When the EPUs of other countries (regions) are at the right tail quantiles, the spillover effect is significantly stronger in both the U.S. and Japan than at the left tail quantiles and the median, and they are net exporters of EPU risk, while HK and mainland China are net receivers. Both financial shocks and major public health emergencies lead to increased EPU connectedness among countries (regions).

Measuring the connectedness of the Nigerian banking network and its implications for systemic risk

This study examines fifteen major banks’ network connectedness in the Nigerian banking system via its stock returns. The paper studies both the static and dynamic network connectedness of banks built on the generalized forecast error variance decomposition, using daily data from January 4, 2005, to June 28, 2019, of publicly traded banks. This study finds a substantial total connectedness, with a high pairwise connectedness among the system’s large banks. The dynamic evolution of connectedness in the network reveals that banks’ connectivity increases in response to certain economic episodes. The evolution of the global network's topological properties reveals that it is mainly susceptible to shocks threatening its stability. Additionally, the study computes a composite index of systemic importance for the Nigerian banking system by combining several network centrality metrics using the principal component analysis. The outcome shows that large banks are more centralized in the network, and the larger the scale of assets a bank has, the more systemically relevant the bank is in the network. Since systemic risk emanates from connectedness, frequent assessment of the banking system's connectedness and systemic importance will aid policy decisions. The proposed measure of systemic importance can be incorporated into the CBN’s stress testing mechanism for fast-tracking risk potential banks.

The role of socioeconomic and behavioral factors in HIV-related deaths

In today’s increasingly interconnected globe, there continues to be a pressing need for thorough research that can shed light on the causes of HIV-related mortality and the primary factors at play in this epidemic. By using an annual dataset, this study analyzed the impact of behavioral risk factors, income, and human capital on HIV-related deaths (HIVD) across all countries and regions. The empirical findings of the panel regression demonstrate that drug use and unsafe sex have a statistically significant and positive effect on HIVD. On the other hand, mean years spent in school and gross national income per capita both have a statistically significant and negative influence on HIVD. The findings of the variance decomposition suggest that drug use should be confronted in wealthy nations to lower HIVD, but both human capital and economic development have a greater influence on the battle against HIVD in underdeveloped and developing countries. In addition, the findings of the panel Granger causality test demonstrate that factors other than unsafe sex can be used as leading indicators in the process of forecasting HIVD. According to these findings, economic variables, as well as behavioral risk factors in emerging nations, and in industrialized countries, should be taken into account when formulating strategies for preventing HIV/AIDS.

Geopolitical Risk and Food Price Inflation: A GVAR Analysis of BRICS and the G7

ABSTRACT We investigate the influence of geopolitical risk on domestic food prices in 12 economies (BRICS and the G7) using a global vector autoregressive (GVAR) approach from 2003M1 to 2022M9. The results show that geopolitical risk shocks cause increases in international prices (oil, agricultural, and general commodity prices). We found that these shocks also provoke fluctuations in domestic food prices. The variance decomposition indicated that international prices and geopolitical risk contribute to the formation of domestic food prices. Finally, the estimates suggest relevant differences between the formation of domestic food prices in BRICS and G7 countries.

Forecasting the Contribution of Renewable Energy to the Indonesian Economy: A Vector Error Correction Model Approach

This study aims to evaluate the causal relationship between Renewable Energy Consumption, Non-Renewable Energy Consumption, and Economic Growth. This study uses quantitative methods and analyzes data using a Granger Causality Test, Vector Error Correction Model (VECM), Impulse Response Function (IRF), and Variance Decomposition (VDC). The study was conducted in Indonesia, with data ranging from 1993 to 2022 obtained from the World Bank database and Our World in Data. The results of the study based on the Granger test show that renewable energy has a significantly positive impact on economic growth in Indonesia. According to the VECM test, renewable energy consumption contributes to increasing economic growth as a result of construction and manufacturing sector activities in the development of renewable energy in Indonesia. The government should focus on energy availability, with an emphasis on increasing the production of renewable natural resources.

Analysis of Price Transmission Chili at the Level from Producer to Consumer in Tapin Regency

The fluctuating prices of chili peppers can significantly impact farmers positively and negatively. Farmers cultivating bird’s eye chili can reap substantial profits with high prices. However, they earn less when bird’s eye chili prices are low. Currently, market integration refers to the evolution of costs across different market sectors and the extent to which market sectors in various geographical locations are informed about the supply and demand occurring in a market. This study aims to: 1) analyze the price developments of bird’s eye chili at the producer, wholesale, and consumer levels in Tapin Regency; 2) examine the price transmission of bird’s eye chili from the producer level to the consumer level through prices at the wholesale level and the production of bird’s eye chili in Tapin Regency. The data used in this research are secondary in the form of monthly time series data. The study utilized the documentation method, with data collected from January 2017 to December 2022, totaling 72 data points. The data analysis method employed is the Vector Error Correction Model (VECM), with tests including the Stationarity Test, Optimal Lag Length Test, Stability Test, Cointegration Test, VECM Estimation, Impulse Response Function (IRF), and Forecast Error Variance Decomposition (FEVD). The findings indicate that the prices of bird’s eye chili exhibit monthly fluctuations. The higher price fluctuations at the producer level indicate that farmers have weak bargaining power in setting the prices of bird’s eye chili. The stationarity test shows that the prices of bird’s eye chili at the consumer (HK), wholesale (HG), and producer (HP) levels, along with bird’s eye chili production (PC), are stationary at the first difference. The optimal lag test, with the smallest Akaike Information Criterion (AIC), is lag 2. The cointegration test shows that the trace statistic and max-eigen values are greater than the critical value, thus accepting H1, indicating a long-term relationship. In the short term, the increase in bird’s eye chili prices is influenced by the two previous periods. The analysis results demonstrate asymmetric price transmission from the producer level to the consumer price level.

SPATIAL MARKET INTEGRATION AND PRICE TRANSMISSION OF COWPEA (Vigna unguiculata) IN NIGERIA

The study investigated spatial market integration and price transmission of cowpea in Nigeria. Secondary data on monthly basis for wholesale cowpea price per kilogram were used for the study. Augmented Dicky Fuller Test, Johansen Co-integration model, Granger Causality Test and Variance Decomposition of Vecto Autoregressive Model and Error Correction Model were used as analytical tools. Augmented Dickey Fuller test revealed that first difference all the price series were stationary and lag one (1) is the optimum lag length appropriate for the specified variables. Cointegration test results of the markets proved that despite that these markets were spatially separated geographically, they were connected in terms of price transmission across them, both in the long and short run but the level of integration is weak. It was observed that price changes are temporary and would converge to an equilibrium within a given time span. Granger causality test results indicate that one pair showed bi-directional causation between Bauchi and Enugu States. Thirty two (32) links showed unidirectional and three (3) pairs such as Niger→Abia, Niger→Lagos and Sokoto→Cross River States pairs showed no causal relationship between them. The study showed that Gombe and River States cowpea price had a faster speed of adjustment to equilibrium than other states. It also revealed that that 1% increase in Jigawa State will result to 20.5% increase in Enugu state and 1 % increase in River State will lead to 40.6% and 64.8% increase in Gombe and Nasarawa States in the short run respectively. The study concluded that, there is integration between the cowpea prices of source and destination states, but the degree of integration is weak. It is recommended that market participants should adopt standardize trading, settlement practice and invest in technology (electronic trading platform) to reduce operational inefficiencies in different markets to support integration.

The Effect of Food Price Stabilization on Inflation in West Nusa Tenggara 2019-2023

One of the primary contributors to the inflation rate in West Nusa Tenggara Province is the fluctuation of prices for food commodities. The objective of this study is to examine the extent to which fluctuations in food commodity prices, including those of rice, red chili, and shallots, contribute to inflationary trends in West Nusa Tenggara. The data used is monthly data on inflation and food commodity prices from 2019-2023 obtained from BPS (Central Bureau of Statistics), DKP (Food Security Agency), and the Agriculture Office of West Nusa Tenggara Province. The analysis method used is vector autoregression (VAR), which includes stationarity test, VAR stability test, optimal lag test, cointegration test, VAR estimation, impulse response function (IRF), and variance decomposition. The results of this study indicate that in the short term premium rice prices, curly red chili prices, and shallots do not affect inflation. Conversely, in the long run, the variable price of shallots does not affect inflation, while the variable price of premium rice and curly red chili prices exert an influence on inflationary pressures in West Nusa Tenggara Province. The Variance Decomposition Test indicates that the price of curly red chili contributes 37.24 percent to inflation in the fifth period, while premium rice has an effect of 8.66 percent on inflation in the second period and shallots have an effect of 7.93 percent on inflation in the fifth period.

Effects of Global Supply Chain Pressure on Sentiment, Expectation, and Uncertainty: A VAR Approach

This paper studies the relationship of global supply chain pressure with consumer sentiment, inflation expectation, and monetary policy uncertainty in the United States. A sample from January 1998 to January 2024 is used, and this paper uses a Vector Autoregression (VAR) approach based on the method proposed by Toda and Yamamoto (1995). The Granger causality test suggests that the predictions of inflation expectation based on its own past values and the past values of the global supply chain pressure are better predictions of inflation expectation than just using the past observations of inflation expectation. In contrast, Impulse Response Functions suggest that surprise increases in global supply chain pressure lead to increased inflation expectation and monetary policy uncertainty; this shock lasts up to two years. Meanwhile, the Impulse Response Functions suggest that surprise increases in the global supply chain pressure decrease consumer sentiment (confidence), lasting up to two and a half years. Afterward, the impact converges back to zero. Additionally, the Variance Decomposition results suggest that by the final period, the impulses of the global supply chain pressure explain over 22%, 7%, and 44% of the variation of consumer sentiment, monetary policy uncertainty, and inflation expectation, respectively.

The Effect of PAD, DAU, DAK, BHSDA, Investment, and Economic Growth on Poverty in North Maluku Province

This study examines the relationship between various economic factors—regional own-source revenue (PAD), general allocation funds (DAU), special allocation funds (DAK), natural resource revenue sharing (BHSDA), investment, and economic growth (PDRB)—and poverty levels in North Maluku Province. While economic growth is commonly associated with improvements in community welfare, North Maluku presents a paradox: despite increasing economic growth, poverty rates have also risen. The objective of this research is to assess the impact of these factors on poverty alleviation in the region. Using variance decomposition analysis, the study finds that the variables of PAD, DAU, DAK, BHSDA, investment, and PDRB collectively have a minimal influence on poverty reduction in North Maluku. This suggests that other factors, not captured by these variables, may play a more significant role in shaping poverty outcomes. The findings highlight the complexity of poverty dynamics in the region and imply that additional policy measures beyond economic growth and fiscal transfers may be necessary to effectively reduce poverty in North Maluku.

Does high volatility increase connectedness? A study of Asian equity markets

Financial market integration metrics based upon variance decompositions from vector autoregression (VAR) models have become quite popular, and prior literature shows how these metrics change over time. However, only a few studies explain what is driving connectedness to change. Using stock market volatilities from eight Asian equity markets, we postulate that volatility may be a driving force behind increased connectedness. We use a two-step VAR estimation procedure to analyze the dynamics between equity market volatility and connectedness. The first step estimates the VAR model over rolling windows; this yields a sequence of connectedness-measuring spillover indices. The second step re-estimates the VAR with the spillover index sequence as an endogenous variable. Impulse response analysis from the two-step procedure confirms that volatility shocks increase connectedness. The two-step procedure indicates that volatility shocks from Singapore, Hong Kong, and China affect connectedness the most, whereas Malaysia affects it the least. China’s direct effect on the volatilities of the other markets is small. However, since China has a considerable impact on connectedness, China may have an important indirect effect on the other markets by increasing the speed and ease with which volatility spreads from market to market.