Variance Decomposition Method Research Articles

Do Terms of Trade and Its Volatility Matter? Evidence From Economic Escalation of China

This article makes a unique contribution to the literature with reference to China, being a pioneering attempt to investigate the effect of terms of trade and its volatility on economic growth of China by using annual time series data from 1980 to 2010 and by applying rigorous econometric techniques. Autoregressive distributed lag (ARDL) cointegration and Johansen and Juselius cointegration methods suggest the significant negative long-run relationship of terms of trade and volatility of terms of trade with economic growth. Four different sensitivity analyses indicate that initial results are robust. Rolling window analysis was performed to find the yearly effect of terms of trade and its volatility on the economic growth of China. Results show that the coefficient of terms of trade on economic growth remains negative from 1985 to 1988, 1994 to 1995, 1998 to 2002, and from 2006 to 2008. Variance decomposition method shows a unidirectional causal relationship between terms of trade and economic growth runs from terms of trade to economic growth and a bidirectional causal relationship between volatility of terms of trade and economic growth in China. It is concluded that less volatile terms of trade are better for economic growth. Policy makers should critically analyze the reasons for deterioration of terms of trade in the years 1985 to 1988, 1994 to 1995, 1998 to 2002 and from 2006 to 2008. Either this happened because of increases in import prices or decreases in export prices, or increases in export prices that are less than increases in import prices. This analysis will be beneficial in finding the contents of export and import by which terms of trade deteriorate and form growth-enhancing policies.

Does Speculation Matters for Wheat Price Shocks?

The purpose of this research is to study the relation between wheat price shocks and speculative movements. VAR model is developed to analyze the data. Impulse-Response functions and Variance Decomposition method are used to analyze the size of relationship among the variables. Wheat prices are effected significantly by speculative movements in the short-run. The relation loses its significance after three months. The effect of speculation on wheat prices can lead to negative reaction from the producers; that will be harmful for an economy as a whole. In order to prevent this, effective use of the government policies is needed; so that, in the long-run, not only economic but also speculative based price structure can be achieved. The disclosures of global wheat yield estimated by the authorities can be a helpful tool in order to control speculative movements and in achieving long-run market equilibrium. This study encompasses a bigger picture and provides an opportunity to have a deeper and broader look into the dynamics of wheat prices. Thus, it can be advantageous for traders as well as for policy makers.

Sensitivity and Uncertainty Analysis Approach for GIS-MCDA Based Economic Vulnerability Assessment

This research aims to employ a novel methodology for modelling uncertainty in the GIS environment. The spatially explicit sensitivity and uncertainty analysis was applied on Multicriteria Decision Analysis (MCDA) for an economic vulnerability assessment within the Salzach Basin. The main objective of this research is to demonstrate how a unified approach of uncertainty and sensitivity analysis can be applied to minimize the associated uncertainty within an economic vulnerability assessment. In order to achieve this objective, the following methodology, composed four steps, was applied: (1) computation of criteria weights using Analytic Hierarchy Process (AHP); (2) Monte Carlo Simulation was applied for computing the uncertainties associated with AHP weights; (3) the Global Sensitivity Analysis (GSA) was employed in the form of the model-independent method of output variance decomposition, in which the variability of vulnerability maps is apportioned to every criterion weight, generating one first-order (S) and one total-effect (ST) sensitivity index map per criterion weight; and (4) an Ordered Weighted Averaging method was applied for producing vulnerability maps. The results of this research demonstrated the robustness of spatially explicit GSA for minimizing the uncertainty associated with GISMCDA models. According to the achieved results, we conclude that applying the variance based GSA leads to a spatial and statistical assessment of the importance of each input factor on the outcome of the GIS-MCDA method, which allows us to introduce and recommend GIS based GSA as a useful methodology for minimizing uncertainty of GISMCDA.

Fiscal Deficits in Egypt: Is It A Macroeconomic or Politico-Institutional Problem?

This paper aims at finding and analyzing the key determinants of Egypt’s fiscal deficits for the period of 1985–2013. It hypothesizes that the political and institutional factors are more important than macroeconomic variables to explain the fiscal deficits of the country in question. In carrying out the research, the paper employs ARDL technique. Findings of the study include that the growth in interest payments, public wage, and subsidy bills are the key sources of fiscal deficits in Egypt. They separately outweigh most of the gains with tax revenues. The reason for this kind of results is found to be largely due to adverse political and institutional factors prevailing in the country. In order to estimate the individual influences of the factors, we use Variance Decomposition Method. Policy implications of the research suggest that to control the future risks of fiscal deficits in Egypt, it is necessary to restrict the growth of subsidy bill and interest payments. However, more efforts are needed to reform the entire tax revenue system and its collection procedures. In addition, policy makers should adopt massive and appropriate political and institutional reform measures.

Purchasing Power Parity: A Unit Root, Cointegration and VAR Analysis in Emerging and Advanced Countries

The purpose of this study is to investigate the validity of the absolute version of the purchasing power parity (PPP) of a sample of four advanced and four emerging countries covering the period from 1993 to 2014. To examine the existence of PPP we apply the Augmented Dickey-Fuller, DF-GLS and KPSS tests for non-stationarity, and the Johansen procedure for cointegration between exchange rates and consumer price indices. The impulse response function presents a graphical view which is consistent with impressions from the statistics of stationarity tests. We also employ the variance decomposition method to analyze the movements in the exchange rates and the price indices that are caused by their own shocks, and shocks caused by other variables. With respect to half-life estimates, the results from a shock to the real exchange rate range from 9,76 to 77,39 months. Overall, unit root tests show that absolute PPP may hold, but this depends on the country and the selected method. In contrast, the Johansen approach does not support the existence of PPP in any country.

Study on the Economic Growth and Environmental Pollution Based the VAR Model

The long term dynamic relation between economic growth and environment pollution is analyzed by using co-integration analysis and granger causality test, generalized impulse response function and variance decomposition method in Jiangsu Province based on the VAR model, and the related data of Environment pollution and GDP indices are from 1982 to 2012. The results indicate that economic growth to environmental pollution is positive and persistent, but that of environmental policy is negative and weak. This conclusion can provide a great reference value for promoting sustainable economic development and formulating relevant environmental policy for Jiangsu Province. the existence of Kuznets inverted U shaped curve for economic growth and environmental pollution depends on the data from different regions and different indexes measuring the levels of environmental pollution.

Multiple environmental changes interact to modify species dynamics and invasion rates

Multiple aspects of the environment often change at the same time, influencing populations directly by modifying their physiology, but also indirectly by influencing other interacting species. The impacts of each environmental change upon population dynamics are usually assumed be independent of the state of other aspects of the environment, despite evidence at the individual level indicating that the combined impacts are often non‐additive. The importance of indirect effects mediated through community interactions also has high uncertainty. We used experimental microcosms to determine whether environmental factors interact to affect species dynamics and the relative importance of direct and indirect effects on species dynamics.We factorially manipulated three aspects of the environment (temperature, food availability and salinity) and examined reciprocal invasions of competing protist species under each environment. Experimental observations were used to parameterize a dynamic model of the system. Using this model and a novel variance decomposition method, we examined the mechanisms by which environmental changes altered species invasion rates.The three environmental factors interacted when modifying species growth rates, intra‐ and interspecific competition, causing the impact of each environmental change on species dynamics to depend crucially on the state of other aspects of the environment. Indirect changes in the abundance of the resident competitor and its interspecific competitive ability were the main cause of environmental driven variation in invasion rates, whilst direct effects on species intrinsic growth rates were relatively unimportant. This indicates that, to understand and ultimately predict species and community responses to multiple environmental changes, we should consider their joint impacts and the mechanisms by which they interact to modify key ecological processes such as competition.

Computing derivative-based global sensitivity measures using polynomial chaos expansions

In the field of computer experiments sensitivity analysis aims at quantifying the relative importance of each input parameter (or combinations thereof) of a computational model with respect to the model output uncertainty. Variance decomposition methods leading to the well-known Sobol׳ indices are recognized as accurate techniques, at a rather high computational cost though. The use of polynomial chaos expansions (PCE) to compute Sobol׳ indices has allowed to alleviate the computational burden though. However, when dealing with large dimensional input vectors, it is good practice to first use screening methods in order to discard unimportant variables. The derivative-based global sensitivity measures (DGSMs) have been developed recently in this respect. In this paper we show how polynomial chaos expansions may be used to compute analytically DGSMs as a mere post-processing. This requires the analytical derivation of derivatives of the orthonormal polynomials which enter PC expansions. Closed-form expressions for Hermite, Legendre and Laguerre polynomial expansions are given. The efficiency of the approach is illustrated on two well-known benchmark problems in sensitivity analysis.

An Empirical Study on the Dynamic Relationship between Higher Educational Investment and Economic Growth using VAR Model

Previous research has studied the relationship between higher education and economic growth, but the results are mixed. This paper analysed higher educational investment and economic growth theoretically and studied the long‐term dynamic relationship between them by using Johansen test, Granger causality test, impulse response function, variance decomposition and other empirical methods based on vector autoregressive model. The result indicates a long‐term stable equilibrium relationship among higher educational fund investment, human capital investment and economic growth. Specifically, higher educational fund investment and human capital investment have a significant positive impact on economic growth. Both higher educational fund investment and human capital investment are Granger causes of economic growth. Fund input is Granger cause of human input. Therefore, fund input is a major and driving impetus for higher education to promote economic growth. Theoretical and practical implications are discussed. Copyright © 2014 John Wiley & Sons, Ltd.

Large‐scale estimation and uncertainty analysis of gross primary production in Tibetan alpine grasslands

AbstractGross primary production (GPP) is an important parameter for carbon cycle and climate change research. Previous estimations of GPP on the Tibetan Plateau were usually reported without quantitative uncertainty analyses. This study sought to quantify the uncertainty and its partitioning in GPP estimation across Tibetan alpine grasslands during 2003–2008 with the modified Vegetation Photosynthesis Model (VPM). Monte Carlo analysis was used to provide a quantitative assessment of the uncertainty in model simulations, and Sobol’ variance decomposition method was applied to determine the relative contribution of each source of uncertainty to the total uncertainty. The results showed that the modified VPM successfully reproduced the seasonal dynamics and magnitude of GPP of 10 flux tower sites on the plateau (R2 = 0.77 − 0.95,p < 0.001). The 6 year mean GPP in Tibetan alpine grasslands was estimated at 223.3 Tg C yr−1(312.3 g C m−2 yr−1). The mean annual GPP increased from western to eastern plateau, with the increase of annual temperature and precipitation and the decrease of elevation, while the decrease of GPP from southern to northern plateau was primarily driven by air temperature. Furthermore, the mean relative uncertainty of the annual GPP was 18.30%, with larger uncertainty occurring in regions with lower GPP. Photosynthetic active radiation, enhanced vegetation index, and the maximum light use efficiency (LUE) are the primary sources of uncertainty in GPP estimation, contributing 36.84%, 26.86%, and 21.99%, respectively. This emphasizes the importance of uncertainty in driving variables as well as that of maximum LUE in LUE model simulation.

A Long-Run Dynamic Analysis of FDI, Growth and Oil Export in GCC Countries: An Evidence from VECM Model

This study investigates a long-run dynamic relationship of GDP, crude oil export and FDI inflows in GCC countries; The United Arab Emirates, Bahrain, Saudi Arabia, Oman, Qatar and Kuwait. The methodology adopted is based on Error Correction Model (VECM) which involves 195 stationary balanced observations over the period 2000-2010. Two major objectives were tested, which are; Impulse response function and Variance Decomposition method. The empirical analysis shows that a shock of FDI inflows will cause a parallel negative influence on the oil export and GDP. As well as FDI inflows are highly linked to GDP compared to oil export.

Variance decomposition: A tool enabling strategic improvement of the precision of analytical recovery and concentration estimates associated with microorganism enumeration methods

Concentrations of particular types of microorganisms are commonly measured in various waters, yet the accuracy and precision of reported microorganism concentration values are often questioned due to the imperfect analytical recovery of quantitative microbiological methods and the considerable variation among fully replicated measurements. The random error in analytical recovery estimates and unbiased concentration estimates may be attributable to several sources, and knowing the relative contribution from each source can facilitate strategic design of experiments to yield more precise data or provide an acceptable level of information with fewer data. Herein, variance decomposition using the law of total variance is applied to previously published probabilistic models to explore the relative contributions of various sources of random error and to develop tools to aid experimental design. This work focuses upon enumeration-based methods with imperfect analytical recovery (such as enumeration of Cryptosporidium oocysts), but the results also yield insights about plating methods and microbial methods in general. Using two hypothetical analytical recovery profiles, the variance decomposition method is used to explore 1) the design of an experiment to quantify variation in analytical recovery (including the size and precision of seeding suspensions and the number of samples), and 2) the design of an experiment to estimate a single microorganism concentration (including sample volume, effects of improving analytical recovery, and replication). In one illustrative example, a strategically designed analytical recovery experiment with 6 seeded samples would provide as much information as an alternative experiment with 15 seeded samples. Several examples of diminishing returns are illustrated to show that efforts to reduce error in analytical recovery and concentration estimates can have negligible effect if they are directed at trivial error sources.

Environmental Pollution and Economic Growth

This paper utilize generalized impulse response function of the VAR model and variance decomposition method to investigate the effects of long-term dynamic characteristics between environmental pollution indicators and GDP per capita in 11 provinces of western China in the 1992-2010.Impulse response analysis showed that:on the one hand economic growth is a major cause of environmental pollution.Environmental pollution on the other hand, economic growth, there are also a reverse effect.however, this force has a certain lag effect. Variance decomposition results show that although environmental pollutionis an important variable to forecast economic growth, the economic growth has little contribution to explain various types of environmental pollution predictor.

Slow and Intraseasonal Modes of the Boreal Winter Atmospheric Circulation Simulated by CMIP5 Models

The authors evaluate the performance of models from Coupled Model Intercomparison Project Phase 5(CMIP5)in simulating the historical(1951-2000)modes of interannual variability in the seasonal mean Northern Hemisphere(NH)500 hPa geopotential height during winter(December-January-February,DJF).The analysis is done by using a variance decomposition method,which is suitable for studying patterns of interannual variability arising from intraseasonal variability and slow variability(time scales of a season or longer).Overall,compared with reanalysis data,the spatial structure and variance of the leading modes in the intraseasonal component are generally well reproduced by the CMIP5 models,with few clear differences between the models.However,there are systematic discrepancies among the models in their reproduction of the leading modes in the slow component.These modes include the dominant slow patterns,which can be seen as features of the Pacific-North American pattern,the North Atlantic Oscillation/Arctic Oscillation,and the Western Pacific pattern.An overall score is calculated to quantify how well models reproduce the three leading slow modes of variability.Ten models that reproduce the slow modes of variability relatively well are identified.

Feedforward and Feedback Control Performance Assessment for Nonlinear Systems

A performance assessment method for nonlinear feedforward and feedback control systems is proposed in this paper. First, the existence of minimum variance performance bound for two nonlinear systems with different structures is analyzed, and the closed-loop model of nonlinear system is obtained with the help of iterative orthogonal least squares identification method. Then, the technology of variance analysis is introduced to establish the variance contributions due to both disturbances and controller. A nonlinear performance index for the feedforward and feedback control systems is estimated using an ANOVA-like variance decomposition method. Finally, a meaningful example is simulated to show the effectiveness of our method.

Study on the Economic Growth and Industrial Waste Water Based on the VAR Model

Based on the VAR model, the long term dynamic relation between economic growth and industrial waste water is analyzed by using co-integration analysis and granger causality test, generalized impulse response function and variance decomposition method in Nanjing, and the related data of industrial waste and GDP indices from 1982 to 2011. The empirical results show that between economic growth and industrial waste water pollutant discharge has a two-way mechanism, but industrial waste water pollutants on the reaction mechanism of economic growth is weak, and there is a certain lag. This conclusion can provide a great reference value for promoting sustainable economic development and formulating relevant environmental policy for Nanjing.

Commodity Residential House Price Volatility Spillover Effects of Study — To Zhengzhou City Circle as an Example

With the overall urban scale and the constant improvement of the level of economic development and regional urban cluster gradually formed, the interactive relationship between urban residential market is increasingly significant.Based on building the VAR model, this paper using Johansen cointegration relationship test, Granger causality test based on VEC model and variance decomposition method of the nine cities of zhengzhou city circle commodity residential house price volatility spillover effect carries on the empirical analysis, the results showed that: each city commodity housing prices keep long-term stable equilibrium relationship between, and has obvious effect, the interaction of zhengzhou and luoyang to other urban commodity residential house price fluctuation has significant guiding role.

Interactive relationships between crude oil prices, gold prices, and the NT–US dollar exchange rate—A Taiwan study

This investigation examines the correlations of oil prices, gold prices and the NT dollar versus U.S. dollar exchange rate during 2007/09/03–2011/12/28. Johansen co-integration test, VAR model, Granger causality test, impulse response analysis, and variance decomposition method were used to clarify the interactive relationships among the three variables. These tests and models show that the oil price, gold price and exchange rate remain considerably independent from one another, which implies policymakers should consider the separation of energy and financial policies.

Uncertainties in estimating future heat wave mortality in the eastern U.S.

Aims. We estimated the future heat wave mortality in the eastern United States (~ 1,700 counties) under two Representative Concentration Pathways (RCPs) and analyzed the sources of uncertainties. Methods Using dynamically downscaled hourly temperature projections in 2057-2059, we calculated heat wave days and events based on four heat wave metrics, and estimated the excess mortality attributable to them. The sources of uncertainty in estimated excess mortality were apportioned using a variance-decomposition method. Results In the eastern U.S., the excess mortality attributable to heat waves could range from 200-7,807 with the mean of 2,379 persons/year in 2057-2059. The projected average excess mortality in RCP 4.5 and 8.5 scenarios was 1,403 and 3,556 persons/year, respectively. Excess mortality would be relatively high in the southern and eastern coastal areas. The major sources of uncertainty in the estimates are relative risk of heat wave mortality, the RCP scenario, and the heat wave definitions. Conclusions The estimated mortality risks from future heat waves are likely an order of magnitude higher than its current level and the probability of catastrophic outcomes with thousands of deaths each year is not trivial under the RCP 8.5 scenario. The substantial spatial variability in estimated county-level heat mortality suggests that effective mitigation and adaptation measures should be developed based on spatially resolved data.

Uncertainty analysis of the recovery of hollow-fiber ultrafiltration for multiple microbe classes from water: A Bayesian approach

In this study, we introduce a Bayesian approach to address uncertainty of microbial recoveries from hollow-fiber ultrafilters (HFUF) and to determine any sources of uncertainty. Microbial recoveries were measured under twenty conditions, including two types of water, two types of ultrafilters, and five types of microorganisms. The probability distributions of the recoveries were approximated using Bayesian statistics with Markov chain Monte Carlo sampling after integrating the likelihood function of the recovery data and prior information about the data. Then a variance-decomposition method was used for examining influential factors on microbial recovery by HFUF. The results revealed that HFUF efficiently recovered Escherichia coli KO11, E. coli O157:H7 and bacteriophage MS2, but recoveries for Bacillus atrophaeus spores and adenovirus 41 were markedly different between source and treated waters. The uncertainty analysis indicated that the probability distributions for recoveries had dissimilar patterns under different conditions. Among these test factors, the type of microorganisms and associated interaction effects had great impacts on the recovery. To sum up, the Bayesian approach to uncertainty analysis shows advantages in evaluating the recovery of HFUF by providing its full probability distribution.