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Examining multilevel effects of financial and social performance of microfinance institutions (MFIs), the authors aim to investigate microfinance mission drift from the trend effect. The authors also seek to move the literature forward by decomposing the performance variance at different levels and examining whether and how much each level of analysis matters.

Growth curve modeling and variance decomposition analysis were conducted using a dataset consisting of 17,953 observations of 2,902 microfinance institutions in 122 countries from 1999 to 2017.

The study's result shows no evidence of mission drift in the microfinance industry. While MFIs improve their economic returns, they also increase the depth of outreach. In addition, firm-level heterogeneity is the dominant effect which explains 44% of the variance in microfinance financial performance (ROA) and 39% of the variance in social performance (Depth of outreach). The country-level is more critical in explaining financial performance (ROA) than social performance (Depth of outreach), accounting for 11 and 32% of the total variance, respectively. In particular, the interplay between the country-level and organizational-category level accounts for 9 and 11% of the total variance in financial performance (ROA) and social performance (Depth of outreach), respectively.

This study’s multilevel analysis of microfinance performances moves the literature forward by responding to the debate on microfinance mission drift and providing a comprehensive overview of both social and financial performance. By focusing on the trend effect, the result of our models shows that MFIs improve both financial and social performance to fulfill dual missions. The microfinance business model becomes sustainable over time. The study's results of country effect and its interaction effect with different organizational categories reveal the prominence of a good policy design on MFI's mission fulfillment.

The purpose of this paper is to investigate the interrelationships amongst the sector‐specific indices of the Qatar Exchange (QE) (i.e. Banking and Financial Institutions (BFI), Industrial (IND), Insurance (INS), and Services (SER)). More specifically, three key issues are explored in this study. First, the long‐run relationships amongst the sectors. Second, the short‐run causal relationships amongst them; and third, the relative degree of endogeneity/exogeneity of each sector.

To address the issues of interest, the author employs the econometric analyses of Johansen's multivariate cointegration, Granger's causality, and generalized forecast error variance decomposition. This battery of techniques gives the opportunity to examine the nature of both long‐ and short‐run intersectoral relationships in the QE. To augment the robustness of the empirical analysis, daily as well as weekly closing stock price indices for the four sectors of the Qatar Exchange are used, spanning the period from January 2, 2008 up to April 7, 2011.

Based on daily and weekly data, the results of Johansen's multivariate cointegration analysis suggest that the four sector indices of the QE share a long‐term equilibrium relationship. The Granger's causality analysis based on daily and weekly datasets provides clear evidence that the BFI sector seems to be a significant causal factor in regard to the price predictability of the remaining sectors in the short run, and that the SER sector surprisingly seems to have the least influential role. Finally, the results of the generalized forecast error variance decomposition analysis using daily data show that the IND and BFI appear to be the most exogenous sectors, whereas the SER and INS are the most endogenous ones. The results based on weekly data confirm the relative exogeneity of the BFI sector and the relative endogeneity of the SER sector.

The findings of this study hold practical implications for individual and institutional investors alike. The potential gains derived from cross‐sector diversification could be rather limited, given the significant degree of interrelationships found amongst the sector indices of the QE. Moreover, the composition of domestic portfolios based on sector‐level investments should be revisited, particularly after major events. The findings also bring some important insights for policymakers. Given the influential role played by the BFI sector in the Qatari economy, policymakers should design appropriate strategies that curb the spread of unanticipated shocks originating from this sector to its counterparts. Besides, due to the considerable degree of endogeneity of the SER sector, it is essential for policymakers to set up precautionary regulations, with the aim of minimizing its vulnerability to common shocks in turbulent times.

Building upon the extant research and focusing on a relatively unexplored market, the paper represents a pioneer attempt to provide empirical evidence on the interdependence structure amongst the sector‐specific indices of the Qatar Exchange.

The purpose of this paper is to examine the effects of several structural shocks in oil prices on the Vietnamese economy and answer three key research questions: Is there a relationship between oil price shocks and macroeconomic indicators in Vietnam? How do different types of oil price impulses affect Vietnamese inflation and economic performance? To what extent do structural shocks in oil prices explain variations in Vietnam’s macroeconomic indicators?

Lower triangular Cholesky decomposition is performed on a short-term impact matrix in a two-block structural vector autoregressive model. The data set is defined monthly, from January 2000 to December 2021. The contributions of structural shocks in oil prices to the domestic variances are analysed using variance decomposition methods. In this study, both forecast error variance decomposition and historical decomposition are used.

The consequences of oil price fluctuations on Vietnamese output and inflation depend on different sources of oil price shocks. In comparison, oil supply shocks have an insignificant effect on both domestic industrial output and consumer price index inflation; however, positive shocks in aggregate and precautionary oil demands increase these domestic indicators substantially and sustainably. An analysis of variance decompositions reveals that supply-side oil shocks have very limited explanatory power for variations in domestic variables. Nevertheless, the contributions of unanticipated demand-side booms to domestic variations in the past and projected forecasts are considerable.

The findings from this research uncover potential risks for Vietnam’s economic prospects if the consequences of oil price shocks are not managed effectively.

Given the lack of economic sensitivity to supply-side oil shocks and the strong response to shifts in oil demands, greater pressure on the domestic economy is likely when Vietnam increases its dependence on oil imports.

The purpose of this paper is to investigate the influence of the real interest rates, inflation and risk premium on the time deposit rates of banks in the dual banking system in Malaysia.

The data consists of 1-, 6- and 12-month average time deposit rates of conventional and Islamic banks over the period of January 2000 to June 2017. The cointegration methodologies are used to explore links between the time deposit rates, real rates, inflation and risk premium. The causality tests to test causality linkages between pairs of variables are also applied. The generalised forecast error variance decomposition based on the error correction model is conducted to analyse the impact of variables variation on the deposit rates.

The results show the presence of two cointegration vectors in the deposit rates, real rates, inflation and risk premium, for both conventional and Islamic bank rates. Causality tests reveal that deposit rates are caused by inflation and risk premium in a one-way causality. The results of variance decomposition highlight the importance of inflation and risk premium in explaining the variations in the bank deposit rates. For the conventional bank, inflation shocks play the most important role in explaining the movements of the deposit rates. In Islamic banks, the major determinant’s largest influence is the risk premium. Between the two bank rates, Islamic bank rates receive more influence from the explanatory variables in the long-run compared to conventional bank rates. The real rates have no noticeable effect on the variance of time deposit rates for both banks.

This study presents new evidence on the relationship between time deposit rates and the three explanatory variables, which are the real interest rates, inflation and risk premium, for both conventional and Islamic banks in Malaysia. The dual banking system allows exploring the similarities and differences between conventional and Islamic banks in Malaysia in terms of the linkages between the variables.

The purpose of this paper is to analyse the interdependencies of the house price growth rates in Australian capital cities.

A vector autoregression model and variance decomposition are introduced to estimate and interpret the interdependences among the growth rates of regional house prices in Australia.

The results suggest the eight capital cities can be divided into three groups: Sydney and Melbourne; Canberra, Adelaide and Brisbane; and Hobart, Perth and Darwin.

Based on the structural vector autoregression model, this research develops an innovative interdependence analysis approach of regional house prices based on a variance decomposition method.

In the backdrop of growing global concern on escalating CO₂ emission leading to climate disorder and controversy between economic growth and environment, this study undertakes a decomposition analysis of the top 20 emitters of the world during 1992–2016 with two objectives: to identify the relative contribution of the major driving factors in CO₂ emission and to comprehend the role and performance of sectoral energy consumption pattern in changing the emission level.

The paper uses variance analysis method to perform two stage decomposition: first, it decomposes emission into the major driving factors, and, secondly, it also decomposes fossil fuel intensity of different sectors into fuel mix and energy intensity effects, which are new in the literature.

The results indicate that aggressive pursuit for economic growth, particularly by developing countries, is the major reason behind unprecedented emission growth, with income effect, fossil fuel intensity effect and population effect having substantial roles. Considerable decline in dependence on fossil fuel, coupled with rising emissions, signifies that emission intensity is still to be harnessed. Sectoral decomposition shows that while fossil fuel intensity has declined in residential sector, it has remarkably shot up in industry, transport and commercial sectors. On the other hand, sectoral energy intensity has declined, particularly due to favourable performances of transport and commercial sectors.

The detailed country-wise sectoral analysis identifies the sectors with favourable contribution in curtailing emission and lends a direction to other countries for policy making.

This study contributes by incorporating multi-country sectoral segregation in decomposition analysis. It focuses not only on energy intensity, but on the effect of energy substitution in each sector as well. It identifies the sectors that have lowered their dependence on fossil fuel to highlight that emission can effectively be dealt with through a prudent choice of fuel mix.

Literature, spanning industrial organization and strategic management disciplines, uses variance decomposition to understand the relative importance of firm, industry and business group effects in shaping profitability variations. Some literature analyzes firm profitability under transition to liberalization. Previous research has taken a static before-and-after view on institutional change. This paper aims to focus on the dynamic process of liberalization in India, analyzing how different institutional regime changes alter firm behavior leading to changes in profitability patterns.

Based on a panel data set of several thousand Indian firms, spanning the 26-year period between 1980-1981 and 2005-2006, the authors determine the relative importance of firm, industry and business group effects in explaining manufacturing firms’ profitability variances across different institutional phases. The authors evaluate three propositions that help assess transition dynamics between phases. They determine the quantum of catch-up or falling behind by firms.

Different industries emerge as profitability leaders, as the economy progresses through different liberalization phases. Business groups that have been more effective in resource appropriation, rent-seeking, politician management and non-market activities in a controlled regime are replaced as profit leaders by those that, in a free-market economy, can be capable of intra-business resource allocation tasks and leveraging corporate capabilities.

The approach demonstrates how to analyze the underlying detailed structure of firm-level data, and performance outcomes, to derive nuanced interpretation of factors giving rise to the effects that explain profitability variances, and how to assess the way these effects behave over time. The dynamic evidence-based approach highlights what factors matter, where, when and why, in influencing profitability variances, which are a key dimension of industrial and economic performance.

The demand for money is an important function of stabilization policies where such policies depend on the ability to manipulate the size of money supply in order to insulate real output from monetary disturbances. This paper investigates whether foreign money in Egypt should be included in transactions oriented measures of money supply. Variance decompositions analysis of demand functions for domestic money reveals that deviation of the expected rate of return on foreign money from that on domestic money is more influential than expected depreciation in accounting for quarterly forecast error variance in domestic real balances. This result suggests that portfolio rather than transactions considerations is the dominant factor behind holding foreign money in Egypt. The main policy implication contained in these results implies that foreign money should not be included in transactions oriented measures of money supply that are used as targets when implementing a monetary policy.

This paper aims to quantify the dependence relationship of bat algorithm’s (BA) behaviour on the factors that could possibly affect the outputs, and rank the importance of the various uncertain factors thus suggesting research priorities.

This paper conducts a sensitivity analysis based on variance decomposition of factors in both of original and improved BA. The data sets for sensitivity analysis are generated by optimal Latin hyper sampling in the design of experiment. The optimal factor sets are screened by stochastic error bar measures for the effective and robust implementation of BA.

The paper reveals the inner dependent relationship between factors and output in both of original and improved BA. It figures out the weakness in original BA and improves that. It suggests that uncertainty brought about by factors are mainly caused by the interaction effect and all the higher-order term in sensitivity indices for both of original and improved BA. It ranks the main effect and the total effect of factors and screens out some optimal factor sets for BA.

This paper quantifies the dependence relationship of BA’s behaviour on the factors that could affect outputs using sensitivity analysis based on variance decomposition.

The purpose of this paper is to determine the sources and factors affecting farm revenue variation on crop and livestock farms in the Northern Great Plains.

A two method approach is used. Variance decomposition analysis is completed on an 18-year balanced panel data set of North Dakota producers to determine the sources of farm revenue variation. The second component of this research uses a random effects estimator to determine the effect of farm characteristics on farm revenue variation measured by coefficient of variation.

Crop revenue is the largest source of farm revenue variation, with crop insurance being the largest source of revenue variation diversification. Small market crops and corn were found to increase revenue variation compared to those operations that received the largest sum of their revenue from wheat. Government payments and insurance payments were also found to increase farm revenue variation indicating they may provide an incentive to plant more risky crops.

This analysis examined specific enterprises that affect farm revenue variation, which has not been examined in earlier work. This distinction allows for focus on potential policy implications of small market crops and new crops in “transitional planting zones”.