Structural Vector Research Articles

Enhanced Diagnosis of Skin Cancer from Dermoscopic Images Using Alignment Optimized Convolutional Neural Networks and Grey Wolf Optimization

Skin cancer (SC) is a highly serious kind of cancer that, if not addressed swiftly, might result in the patient’s demise. Early detection of this condition allows for more effective therapy and prevents disease development. Deep Learning (DL) approaches may be used as an effective and efficient tool for SC detection (SCD). Several DL-based algorithms for automated SCD have been reported. However, more efficient models are needed to improve accuracy. As a result, this paper introduces a new strategy for SCD based on Grey Wolf optimization (GWO) methodologies and CNN. The proposed methodology has four stages: preprocessing, segmentation, feature extraction, and classification. The proposed method utilizes a Convolutional Neural Network (CNN) to extract features from Regions of Interest (ROIs). CNN is employed for feature categorization, whereas the GWO approach enhances accuracy by refining edge detection and segmentation. This technique utilizes a probabilistic model to accelerate the convergence of the GWO algorithm. Employing the GWO model to optimize the structure and weight vectors of CNNs can enhance diagnostic accuracy by a minimum of 5%, based on evaluation outcomes. The application of the proposed strategy and its performance comparison with other methods indicate that the proposed method with GWO predicted SC with an average accuracy of 95.11% and without GWO an Accuracy of 92.66%, respectively, enhancing accuracy by a minimum of 2.5% when we train our model with GWO.

Broadband spectropolarimetry based on single-shot intensity images of polychromatic structured vector beams

Broadband spectropolarimetry based on single-shot intensity images of polychromatic structured vector beams

Who Takes the Cake? The Heterogeneous Effect of European Central Bank Accommodative Monetary Policy across Income Classes

AbstractThis work provides evidence of the heterogeneous effects of the ECB's monetary policy across income classes. In particular, this investigation focuses on the labor market channel. Based on EU‐SILC data, we estimate country‐specific structural vector autoregressions (SVAR) models to analyze the impact of the expansionary monetary policy shocks over the 2006–2019 period. The results suggest that monetary easing helped decrease unemployment rates for lower‐ and middle‐income classes, to a larger extent for the former. This differential impact is accounted for a stronger improvement in job finding rates for classes located at the bottom of the income distribution. Conversely, the employment status of the upper class remained largely unaffected. The analysis identifies a positive impact of expansionary monetary policy on real labor income, which seems to have mostly benefitted the upper class. Overall, our results suggest that expansionary monetary policy helped decrease labor income inequality by exerting a stronger positive impact on lower‐income households.

Do economic shocks matter to U.S. farm income?

ABSTRACT Analysing the effect of economic shocks on farm profitability is crucial for determining the U.S. farm economy’s vulnerability to macroeconomic changes. Using a structural vector autoregressive model and over five decades of data, we examine the impact and importance of external economic shocks to the U.S. farm economy. Specifically, we measure the short-term and long-term volatility spillover effects on U.S. farm income from shocks in energy markets, exchange rates, and agricultural markets. We find that volatility in exchange rates significantly affects US net farm income fluctuations in the short term. Results also suggest that shocks in the exchange rate are more important than other economic shocks, explaining about 47% of the short-run fluctuation in U.S. net farm income. These results highlight the importance of accounting for macroeconomic shocks to enhance the stability of the U.S. agricultural sector and to improve the accuracy of USDA short-term forecasts and long-term projections of farm income components.

Nonparametric Time Varying IV-SVARs: Estimation and Inference

This paper studies the estimation and inference of time-varying impulse response functions in structural vector autoregressions (SVARs) identified with external instruments. Building on kernel estimators that allow for nonparametric time variation, we derive the asymptotic distributions of the relevant quantities. Our estimators are simple and computationally trivial and allow for potentially weak instruments. Simulations suggest satisfactory empirical coverage even in relatively small samples as long as the underlying parameter instabilities are sufficiently smooth. We illustrate the methods by studying the time-varying effects of global oil supply news shocks on US industrial production.

Does financial stability spur or retard output and price stability in South Africa?

Following the 2007-2009 global financial crisis, the South African Reserve Bank (SARB) prioritised financial stability. This study assesses the impact of financial stability on South Africa's output and price stability, which is crucial for framing its monetary policy. Utilising a Structural Vector Autoregression (SVAR) approach, the influence of the Financial Conditions Index (FCI) on the Gross Domestic Product (GDP) and Consumer Price Index (CPI) between 1960Q1 and 2022Q4 was analysed. Results revealed a strong connection between these objectives. GDP drops after an FCI disturbance, whereas the CPI trends upwards when the SARB counters financial imbalances. This vital finding indicates a positive relation between enhanced financial conditions and output stabilisation but reveals a trade-off between price and financial stability. After a GDP shock, financial conditions improve, but they deteriorate following a CPI shock. If the SARB reacts by "mopping up" post-crisis, the policy rate typically drops, pointing to potential financial vulnerabilities. Both GDP and CPI react positively to FCI disturbances when SARB is passive. The evidence suggests that while financial stability bolsters output stability, it might endanger price stability. Thus, amongst other recommendations, South Africa's monetary policy authorities must lean against financial imbalances instead of mopping after a crisis

INVESTIGATION OF CHANGES IN THE LEVEL OF NETWORK SECURITY BASED ON A COGNITIVE APPROACH

A study was conducted on the impact of the most significant threats on the level of network security based on the examination of impulse processes on a fuzzy cognitive map. A topological analysis of the structure of the cognitive map was performed, simplicial complexes were constructed, and their structural vectors were determined. Based on the obtained data, a set of control and target concepts of the fuzzy cognitive map was formed, and the relationships between these concepts within the simplicial complexes were established. Taking this information into account, a study was conducted on the change in the level of computer network security using the propagation of impulses introduced into the control concepts of the fuzzy cognitive map. The results obtained enable an increase in the level of network security by considering the impact of the most significant threats through timely managerial decisions and the implementation of necessary software and technical measures.

Weather Shocks in The Gambia: Macroeconomic Effects and Households Vulnerability

Abstract Climate change is increasing the frequency of weather shocks, which poses a significant challenge for agriculture-dependent economies such as those of sub-Saharan African countries. In this study, we present an innovative framework that combines empirical and theoretical procedures to investigate the propagation mechanism of weather shocks in The Gambia. We first document the propagation mechanism of a weather shock using a Structural Vector Auto-regressive (SVAR) model on the Gambia data. We construct and estimate a Dynamic Stochastic General Equilibrium (DSGE) model to elucidate this mechanism. This model is enhanced to encapsulate the weather-dependent agricultural sector, the heterogeneity of households in terms of access to financial markets and the inflow of remittances. Our findings reveal that weather shocks in The Gambia (i) account for approximately 10% of GDP and agricultural output in the short run, (ii) significantly contribute to long-term consumption volatility,(iii) critically increase the volatility of Ricardian households’ consumption and (iv) stimulate an increase in the direct inflow of remittances to vulnerable households.

An Empirical Analysis of the Impact of Financialisation on the Rate of Profit for the US (1955–2019)

ABSTRACTThe purpose of this article is to examine the relationship between financialisation and functional income distribution. To this end, we empirically analyse the relationship between financialisation, the real wage level and the rate of profit in the US (1955–2019) using structural vector autoregressive modelling. According to our results, while an increase in financialisation leads to changes in the rate of profit with ambiguous signs and unclear statistical significance, financialisation has a clear, negative and persistent effect on the real wage level. We conclude with a reconsideration of the role of financialisation in shaping functional income distribution, as it appears to contribute directly to restraining real wage levels rather than directly impacting profitability.

Prestress state, parametric analysis study and genetic algorithm-based tentative design of a novel pentagonal cable dome structure with tri-strut layout

To improve the structural performance and economy of the large-span roof structure from the structural system level, a novel structural form of pentagonal cable dome with tri-strut layout and large central opening is proposed, which can be applied to the large-span stadium ring canopy structure. Different from Fuller's traditional conception of the tensioning whole, this type of system has three struts intersecting at the same chord node, which reduces the amount of ring and diagonal cables, facilitates tensioning construction, and improves overall stability. For this large-opening cable dome, a general formula for calculating the internal force of prestressed state rods was derived based on the nodal equilibrium equations, and the effects of several parameters on the distribution of pre-tension in the cable dome were analyzed and studied to understand the distribution pattern and characteristics of the cable dome pre-stress. A large-opening stadium canopy with a span of 100m is taken as an example. The parameter sensitivity and correlation analysis of the structural performance reveals that the main influence of the economy of this cable dome is the pre-tension level. In contrast, the geometric parameter produces a much lower effect on the structural economy. The optimal design and trade-off analysis of this cable dome structure based on two genetic algorithms show that appropriately increasing the structural vector height and decreasing the thickness while satisfying the stability improves the structural stiffness and economy. The analysis results show that this new type of cable dome has superior technical and economic indicators, and the research in this paper provides a new form and new ideas for the analysis and design, optimization research, and modeling of the cable dome.

The Drivers of Sustainable Development in South African Economy: The Role of Female Advancement

Contemporary societies have challenges in achieving sustainable development, while African economies remain quite distant from this ideal course for sustainable growth. Likewise, official statistics in South Africa show a wide gap between male and female involvement in economic activities. Given this, the study investigates the connection between three dimensions of sustainable development and female advancement in South Africa using auto-regressive disturbed lags (ARDL), Structural Vector autoregressive (SVAR) and Ganger non-causality approach. The three dimensions of sustainable development considered in this study are economic, environmental and social, proxies by adjusted net national savings, carbon emissions and adjusted net national income per capita, respectively. The female advancement indicators are proxies by employment, wages and salaries, and secondary school enrolment. The result from the ARDL shows that female advancement indicators promote short (SR) and long-run (LR) economic sustainability. Also, female wages and salaries have a positive relationship with carbon emissions in the SR but not significantly in the LR. At the same time, female employment and secondary school enrolment have a positive relationship with carbon emissions in the SR but are negative in the LR. The primary causes affecting long-term environmental sustainability are GDP per capita, financial development, population density, trade openness and urbanisation. Also, female advancement indicators promote social sustainability, especially employment, wages and salaries. More so, the SVAR results show that the forecasting error shock of GDP has minimal effect on carbon emission with notable variation in the population density. The causality result shows a unidirectional relationship between economic sustainability and female secondary school enrolment, environmental sustainability and female secondary school enrolment, and social sustainability and female secondary school enrolment, all flowing to female secondary school enrolment. Based on the findings, the study recommended improving female educational standards and increasing involvement in economic participation.

Do disaggregated oil price shocks drive the Chinese non-ferrous metal market across different time horizons and quantiles?

ABSTRACT This study uses a wavelet-based quantile approach to document the effects of disaggregated oil shocks on the Chinese non-ferrous metal market under various time horizons and quantiles. Oil shocks are decomposed into supply, aggregate demand, and oil-specific demand shocks based on a structural vector autoregressive model. Empirical results suggest that oil shocks have notable impacts on the Chinese non-ferrous metal market in the middle and long term, whereas the impacts in the short term are not significant. The impacts remain remarkably heterogeneous across quantile levels in the long term. Oil supply shocks significantly affect the non-ferrous metal market positively, but only at higher quantiles. The positive effect of aggregate demand shocks and the negative effect of oil-specific demand shocks are significant at lower quantiles. Moreover, copper is susceptible to oil shocks, compared with aluminium. These findings provide several implications for policymakers and investors.

Macroeconomic responses to financial stress shocks: Evidence from the US and the Eurozone

This paper investigates the macroeconomic effects of an unexpected financial stress shock in the United States and the Eurozone, focusing mainly on the effect on unemployment and the response of monetary policy. First, we estimate Impulse Response Functions (IRFs) based on a Structural Vector Autoregression (SVAR) model with short-run restrictions according to economic theory. Next, we perform panel data analysis shedding light on the factors that affect unemployment in the two regions. Our findings indicate a significant impact of financial stress shocks on the macroeconomic environment, but with different policy responses from the Federal Reserve (Fed) and the European Central Bank (ECB). The impact of higher financial stress on economic activity and employment is negative in both regions, but the duration of the impact on unemployment is shorter in the US, suggesting a quicker recovery in the US labor market compared to the Eurozone. The faster recovery of the US labor market is primarily due to the superior institutional and regulatory performance in the US. These findings provide policymakers with valuable lessons about the importance of continuous monitoring and quick action to mitigate the negative effects of financial instability on economic activity.

Uneven effects of monetary policy: Sectoral disparities in credit card spending

This paper investigates the effects of monetary policy on the credit card spending on different sectors. The investigation is based on a structural vector autoregression model, where sector-specific real credit card spending data (adjusted for inflation) representing an overall country, Türkiye, are used. The empirical results (in the long run) suggest that a positive shock to the monetary policy rate reduces real credit card spending in cars, health, insurance, and shopping in a statistically significant way, whereas it increases real credit card spending on airlines and travel. Monetary policy shocks contribute to the volatility of credit card spending by up to 36% for insurance, 26% for markets and shopping centers, and 22% for travel sectors, whereas this contribution is only about 3% for contractor services and about 4% for car rentals, jewelry, and casino sectors. It is implied that there are uneven effects of monetary policy across sector-specific credit card spendings. These results are robust to the consideration of changes in unemployment rate, inflation rate, nominal effective exchange rate, and the number of credit card transactions as well as alternative model specifications with different numbers of lags, different variables, and different estimation strategies. Important suggestions follow for monetary, fiscal, and macroprudential policies to mitigate the uneven effects of monetary policy across sectors.

Modeling Inflation Expectations in Forward-Looking Interest Rate and Money Growth Rules

We propose a novel approach that directly embeds rational expectations (RE) into a low-dimensional structural vector autoregression (SVAR) without the need for any mapping to a dynamic stochastic general equilibrium (DSGE) model. Beginning from a fully specified “consensus” structural model, we establish an instrumental variable procedure internal to the SVAR to obtain RE-consistent structural responses to identified monetary policy shocks. Our RE-SVAR framework facilitates a comparison across two alternative monetary policy indicators that accommodate long horizons in the formation of inflation expectations in the policy rule. We construct clouds of responses of inflation and economic activity to monetary policy shocks. We find large regions of puzzling responses to innovations in the federal funds rate. This suggests that indicator often requires being augmented with more information in standard VAR settings. A money growth rule characterization—with Divisia M4 as a policy indicator—exhibits comparatively larger regions of sensible responses within a low-dimensional textbook model of the economy.

Value Added Tax Collection Efficiency and Economic Activity

Objective: Revenue enhancement through improving the tax efficiency generates more income for the government without increasing the tax burden, and sustain economic growth. The objective of this study is to investigate the relationship between value added tax collection efficiency and growth. Theoretical Framework: Endogenous economic growth model allows for investigating the effects of fiscal policy on growth. In tax literature, indirect taxes are considered close to growth because they do not discourage saving and investment. The study decomposes the VAT revenues and use the VAT C efficiency to estimate the impact on growth. Method: The methodology adopted for this research is based on structural vector autoregressive SVAR. SVAR allows us to impose structural restrictions according to economic theory and to analyze the impact that an individual shock has on other variables included in the model. Results and Discussion: The study found that value added C -efficiency has a small and positive impact on growth, only in the short term. The maximum effect is in the third quarter, when one percentage point improvement in collection efficiency, increase the real per capita GDP, about 0.3 percent. Further improvements in collection efficiency are needed to generate more tax revenues and sustain growth. The study also found that total tax to GDP has a positive impact on growth, that is significant only in the short time. Government expenses have supported economic growth. An increase in government expenses as a percentage of GDP, is followed by an improvement in real per capita GDP with about 1.05 percent, average impact. Research Implications: The present research uses four fiscal variables, and only inflation rate based on GDP deflator as a control variable. Further research could be extended by including other control variables. Originality/Value: This study contributes to the tax literature in developing countries by estimating the impact of VAT on growth using C -efficiency ratio.

Employment Shift in Response to a Technology Shock: An Analysis of Two Rigidities and Two Agents

This paper examines the relationship between a technology shock and employment, considering price, wage rigidities, and heterogeneous agents. To explore this relationship, we utilized a Dynamic Stochastic General Equilibrium (DSGE) model, incorporating households with varying savings rates. For empirical validation, we conducted a Structural Vector Autoregression (SVAR) analysis using data from two economies with distinct savings patterns—the United States and China. This approach allowed us to assess the impact of technology shocks on employment dynamics across different savings environments. Under these conditions, we observe that the effect of technology on aggregate employment is initially positive. Still, it gradually decreases in the mid-term, eventually switching to a negative impact before slowly recovering to equilibrium. The reason for this phenomenon depends on (i) the magnitude of fluctuations in price and wage, precisely, which variable’s fluctuations have a greater magnitude, and (ii) which effect, between income effect and substitute effect, is preferred by restricted and unrestricted households. Due to (i), real wages change, and because of (ii), households make different labor supply decisions, leading to fluctuations in employment in response to technology shocks.

Oil price shocks and bond risk premia: Evidence from a Panel of 15 Countries

We study the effect of oil price shocks on bond risk premia. Based on Baumeister and Hamilton (2019), we identify the different sources of oil price shocks using a structural vector autoregressive (SVAR) model of the global market for crude oil. These structural factors are then used as unspanned factors in an affine term structure model based on the representation of Joslin et al. (2014). This is done for a total of 15 countries. Unspanned factors are responsible for most of the variability in bond risk premia for short holding periods, while spanned factors dominate the variance decomposition for longer holding periods. In both cases, global oil supply and global economic activity are clearly the most important unspanned shocks. A historical decomposition around the outbreak of the COVID-19 crisis shows the clear influence of global economic activity shocks during the months of February and March 2020, increasing bond risk premia significantly.

주식시장 수익률과 개인 투자자의 매매 성향

This study examines the relationship between individual investors and the KOSPI stock market using long-term data spanning 25 years from 1999 to 2023. However, this study analyzes the relationship between individual investors' net buys and KOSPI returns by dividing the entire period into five sun-periods of five years and applying two-sided regression and structural vector autoregressive (SVAR) models. The main findings are as follows: First, compared to foreign investors, both institutional and individual investors exhibit relatively higher turnover ratios. Second, cross-sectional analysis shows that KOSPI returns decrease monotonically with the individuals' net buys. Third, the results of the two-sided regressions indicate a Granger causality, where past returns negatively influence individual investors' net buys. In addition, today's individual net buys negatively Granger cause future returns. Fourth, the SVAR analysis also shows that KOSPI returns have a strong negative effect on individual net buys. Finally, according to the results of SVAR impulse response function, net buys response negatively to KOSPI shocks, and this response dissipates almost entirely by the third day. In summary, individual investors tend to engage in net selling in response to positive news in the KOSPI market, and this tendency may be a key reason for their lower performance.

The European energy crisis and the US natural gas market dynamics: a structural VAR investigation

The Russian invasion of Ukraine triggered severe disruptions in the European energy market, causing also significant shifts in global natural gas flows. We investigate how this European shock has affected the dynamics and altered the estimates of the elasticities on the US natural gas market. We use the Bayesian Structural Vector Autoregression framework proposed by Baumeister and Hamilton (Am Econ Rev 109(5):1873–1910, 2019, BH) for the crude oil market and applied by Rubaszek et al. (Energy Econ 103:105526, 2021, RSU) to analyze the dynamics of the US natural gas market. By extending the RSU model for natural gas exports and imports and deriving model’s posterior using most recent data, we approximate the impact of the European energy crisis on the US natural gas market. We show that the estimates of the US natural gas market elasticities change due to our modification, while simply updating the same prior beliefs with most recent data impacts the posterior estimates to a very limited extent. We also find that a shock even as major as the European energy crisis has only marginally affected the US natural gas market, thus confirming the results from the literature that the EU and US natural gas markets evolve independently.