Structural Vector Research Articles

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.

Skill bias in the labour market: Evidence from Iran

Purpose ― Most global economies are dealing with the issue of skill bias. In developing and underdeveloped countries, skill bias poses a problem by preventing the educated from participating in the economy's production function, especially in the long run. This paper expands on the skill-wage relationship and investigates this issue in the case of Iran from 1981 to 2021.Methods- Applying Impulse Responses from VECM and the Structural VAR model separates the relationship between skill and wage into short- and long-term effects. The structural wage model was estimated using the structural vector auto-regression model.Findings ― The results show that skill played a significant role in wage determination only for three periods in the short run, and the effect was neutral in the long run. This means that skill accumulation through advancement in graduate and postgraduate study is unlikely to increase wages in the long run.Implication ― According to the findings, skill bias implies that education attainment in the Iranian labour market can only improve wages to a minimum extent. This also proves that factors other than education determine wage growth in the economy. Originality ― The skill-wage relationship has not been a focus of studies in education outcome fields. Moreover, in the case of Iran, this investigation is novel, and there is a lack of studies on the relationship between compensation and skill.

Fiscal Policy, International Spillovers, and Endogenous Productivity

AbstractThe paper presents empirical evidence on the international effects of U.S. fiscal policy from structural vector autoregressions identified through external instruments in a panel setting for the G7 countries. An exogenous increase in U.S. government spending is estimated to produce sizable positive responses of output and consumption in the rest of the G7 countries, both about half as large as their domestic U.S. counterparts, while strongly depreciating the U.S. terms of trade and lowering the U.S. trade balance and short‐run real interest rates. Moreover, fiscal shocks are estimated to have a strongly positive impact on hourly labor productivity in the private sector. We solve a two‐country New Keynesian model in closed form and show that a low cost elasticity of varying technology utilization can simultaneously explain the positive productivity, consumption, and international spillover effects as well as the real depreciation resulting from expansionary U.S. government spending shocks.

This is going to hurt: Weather anomalies, supply chain pressures and inflation

As climate change accelerates, the frequency and severity of extreme weather events are expected to worsen and have greater adverse consequences for ecosystems, physical infrastructure, and economic activity across the world. This paper investigates how weather anomalies affect global supply chains and inflation dynamics. Using monthly data for six large and well-diversified economies (China, the Euro area, Japan, Korea, the United Kingdom, and the United States) over the period 1997–2021, we implement a structural vector autoregressive model and document that weather anomalies could disrupt supply chains and subsequently lead to inflationary pressures. Our results—based on the monthly data and robust to alternative estimation methodologies—show that these effects vary across countries, depending on the severity of weather shocks and vulnerability to supply chain disruptions. The impact of weather shocks on supply chains and inflation dynamics is likely to become more pronounced with accelerating climate change that can have heterogeneous effects. These findings have important policy implications. Central bankers should consider the impact of weather anomalies on supply chains and inflation dynamics to prevent entrenching second-round effects and de-anchoring of inflation expectations. More directly, however, governments can invest more for climate change adaptation to strengthen critical infrastructure and thereby minimize supply chain disruptions.

Determinants of Household and Professional Forecasters’ Inflation Expectations in India

ABSTRACTThis study analyses and compares the determinants of inflation expectations among households and professional forecasters in India. To this end, we utilize quarterly survey data from the Reserve Bank of India (RBI), specifically the Inflation Expectations Survey of Households (IESH) and the Professional Forecasters’ Survey (SPF). The analysis employs a non‐recursive structural vector autoregressive (SVAR) model to explore the relationships between variables. The impulse response function and variance decomposition analysis shed light on the response of inflation expectations to macroeconomic shocks. Our findings indicate that apart from conventional macroeconomic factors like realized inflation and monetary policy, exogenous factors such as oil prices significantly shape inflation expectations. Comparing IESH and SPF, we found out that household expectations are more sensitive to shocks related to food prices, reflecting the influence of food expenses on household decision‐making. Conversely, professional forecasters’ expectations are more influenced by core inflation. Finally, we observed that inflation expectations adjust more quickly than realized inflation in response to macroeconomic shocks, suggesting a leading role. This study is novel with the contribution to the existing literature as it provides a comparative analysis of determinants of inflation expectations between households and professional forecasters in India, while also considering inflation targeting as a significant factor.

TRAF4 promotes lung cancer development by activating tyrosine kinase of EGFR

Objective: To explore the role of tumor necrosis factor receptor-associated factor 4 (TRAF4) in promoting the abnormal activation of epidermal growth factor receptor (EGFR) and its effect on lung cancer cell proliferation, migration and invasion. Methods: Tumor tissues from patients who underwent lung adenocarcinoma resection at The First Affiliated Hospital of Second Military Medical University, from January 2015 to May 2017 were collected, and the expressions of TRAF4 and Ki-67 in lung cancer tissues were detected by immunohistochemistry, the mRNA levels of Cyclin D and Vimentin were detected by real-time fluorescence quantitative PCR (qRT-PCR). The effect of TRAF4 on the tumor growth ability of lung cancer A549 cells was investigated by the xenograft model, the effect of TRAF4 or EGFR on the tumor proliferation ability was detected by using cell counting kit 8 (CCK8) and BrdU assay, and the migration and invasion abilities of tumor cells were detected by Transwell assay. Different structural domain deletion expression vectors of TRAF4 and EGFR were constructed to transfect cells, and the interaction mode of TRAF4 and EGFR was investigated by immunoprecipitation assay. Results: The expression of TRAF4 in non-small cell lung cancer (NSCLC) tissues was positively correlated with the expressions of Ki-67, cyclin D, and vimentin (r2: 0.438, 0.695, and 0.736, respectively, all P＜0.01). Immunohistochemical assay of tumor tissues from NSCLC patients showed that tissues with high expression of TRAF4 were also high in Ki-67. Patients with high TRAF4 expression (TRAF4 positivity >30%) had a shorter progression-free survival (PFS) time than that of patients with low TRAF4 expression (TRAF4 positivity ≤30%) (median PFS of 12 and 19 months, respectively; P=0.034). Traf4-/- cells had a weakened proliferative capacity than traf4+/+ cells and formed tumors with smaller size (P＜0.05). The expression level of Ki-67 in the tumor tissues formed by traf4-/- cells [(45.6±8.7)%] was lower than that in the tumor tissues formed by traf4+/+ cells [(62.3±10.3)%, P=0.015], the mRNA levels of cyclin D (1.01±0.15) and vimentin (1.01±0.12) in the traf4-/- cells were lower than those of the traf4+/+ cells (3.41±0.32 and 3.12±0.18, respectively, both P＜0.05).The western blot results showed that, with the elevated intracellular expression level of TRAF4, phosphorylation level of EGFR was significantly increased in both wild-type EGFR and activation mutant EGFR-expression cells. The capacities of proliferation, migration and invasion of A549 cells was weakened after EGFR knockdown (all P＜0.01). Immunoprecipitation experiments showed that TRAF4 binds to the peptide segment of the near-membrane region of EGFR through the TRAF structural domain, and the mutual binding between EGFR molecules was enhanced under TRAF4 overexpression conditions. Increasing TRAF4 expression promoted EGFR molecular phosphorylation and activation of downstream signaling. Conclusions: TRAF4 expression is elevated in NSCLC tissues and tumor cells, which promotes tumor proliferation, migration and invasion. TRAF4 directly binds to EGFR molecules, enhances its own phosphorylation and activates the downstream signaling pathway by promoting the interaction between EGFR molecules.

CoFFEE: a codec-based forensic feature extraction and evaluation software for H.264 videos

The forensic analysis of digital videos is becoming increasingly relevant to deal with forensic cases, propaganda, and fake news. The research community has developed numerous forensic tools to address various challenges, such as integrity verification, manipulation detection, and source characterization. Each tool exploits characteristic traces to reconstruct the video life-cycle. Among these traces, a significant source of information is provided by the specific way in which the video has been encoded. While several tools are available to analyze codec-related information for images, a similar approach has been overlooked for videos, since video codecs are extremely complex and involve the analysis of a huge amount of data. In this paper, we present a new tool designed for extracting and parsing a plethora of video compression information from H.264 encoded files, including macroblocks structure, prediction residuals, and motion vectors. We demonstrate how the extracted features can be effectively exploited to address various forensic tasks, such as social network identification, source characterization, and double compression detection. We provide a detailed description of the developed software, which is released free of charge to enable its use by the research community to create new tools for forensic analysis of video files.

Financial and market risks of bitcoin adoption as legal tender: evidence from El Salvador

Bitcoin adoption as a legal tender threatens a financial crisis because of the lack of regulatory frameworks and systems for exchanging Bitcoin into local currencies. This study analyzes monthly data from 2010 to 2022 using a structural vector autoregressive model, estimating Bitcoin’s pass-through into remittance, money multiplier, the US Dollar index, and gold price. The results show that Bitcoin prices decrease the money multiplier and gold prices in both the short and long run, while remittances moderately increase in the long run. The implication of these results suggests the potential for international business opportunities to stimulate the credit, savings, and investment monetary policy channel. The results are robust to alternative SVAR identification strategies.

Learning‐Based Vectorial Reconstruction of Orthogonal Polarization Components in a Structured Vector Optical Field Passing Through Scattering Media

AbstractOptical imaging through scattering media has become important due to its fundamental physics interest and various applications. The reconstruction of a structured optical field with various states of polarization passing through a scattering medium with a speckle pattern behind the scattering medium remains challenging since existing restoring techniques only reconstruct the speckle in a single‐polarization state (scalar optical field). This work proposes a novel approach to simultaneously restore the initial orthogonally polarized components from a speckle pattern behind a scattering medium. The neural network Polarization‐DenseUnet (P‐DenseUnet) based on the vector transfer matrix is constructed to restore the two orthogonally linear (or circular) polarization components of a structured vector optical field from a speckle pattern behind the scattering medium. The generalization and effectiveness of this proposed method are tested for high fidelity with different phase distributions such as vortex, digits, and Fashion‐mnist.

Impact of fiscal policy shocks on health outcomes in Sub-Saharan Africa: A panel structural VAR approach

This study investigates the impact of fiscal policy shocks on health outcomes in Sub-Saharan Africa (SSA). It is motivated by health systems’ increasing reliance on public funding despite growing macroeconomic uncertainties. This study adopts a regional analysis, sampling five countries with the highest per capita public health expenditure from the Central, Eastern, Southern, and Western African regions. The study employs the panel structural vector autoregression technique and expresses health outcomes as a function of fiscal policy and private health expenditure shocks. The results suggest that fiscal policy shock significantly and positively affects health outcomes in Central and Western Africa. However, health outcomes are resilient to fiscal policy shock in Eastern and Southern regions and to private health expenditure shock in all regions. The study indicates that achieving sustainable improvement in health outcomes in SSA would require policies designed to expand health financing options beyond the traditional public and private funding arrangements.

A Structural Vector Autoregression Exploration of South Africa’s Monetary and Macroprudential Policy Interactions

Interactions between monetary and macroprudential policy are crucial in safeguarding price and financial stability. This study investigates the macroeconomic and financial impacts of monetary and macroprudential policy interactions in South Africa, a leading African economy in developing macroprudential frameworks. The existing literature largely focuses on the effectiveness of these policies independently, leaving a gap in understanding how their interaction affects their overall efficacy. Employing a Structural Vector Autoregression (SVAR) model and utilizing data from 1980 to 2023, this study uniquely incorporates the financial cycle to represent financial developments. The results reveal significant effects of both policies on key variables such as output, the financial cycle, and the price level. Specifically, policy contractions reduce output and the financial cycle but increase the price level, illustrating the ‘price puzzle’. This study further identifies an endogenous response between the two policies: monetary policy reacts by rising to reduce price levels following a macroprudential shock, while macroprudential policy rises to stimulate financial activity after a monetary shock. These findings underscore the importance of using both policies in conjunction but in opposite directions to balance their effects and achieve price and financial stability. This study suggests that an optimal combination of monetary and macroprudential policies is critical for maintaining macroeconomic equilibrium.

Innovation and Demand as Drivers of Labour Productivity: An Integrated Analysis for OECD Countries

ABSTRACT The article seeks to empirically reconcile two strands of research on the determinants of labour productivity growth: on one hand, the Kaldorian literature emphasises the role of autonomous demand; on the other hand, the Schumpeterian literature underscores the role of innovative investments. Specifically, we test two hypotheses. Firstly, we investigate whether demand shocks, private research intensity, and public R&D expenditure influence labour productivity when these variables are considered simultaneously. Secondly, we examine whether private research intensity is affected by demand shocks and public investments in innovation. We make use of a panel of 12 OECD countries spanning the period from 1986 to 2017. We combine the Local Projection approach with structural shocks estimated through Structural Vector Autoregressive modelling. Findings show a positive impact of government R&D investment, demand shocks, and private research intensity on labour productivity. Additionally, when examining the effects of demand shocks and innovative activities by the public sector on research intensity, the results affirm a positive impact of these variables, thus supporting the presence of the Schmooklerian demand-pull hypothesis. These results remain robust across various model specifications and different measures of labour productivity.

Verbal attacks on terrorist groups increase violence against civilians

Abstract Terrorists and other transnational extremist groups are responsible for thousands of civilian deaths. In confronting extremists, governments have relied heavily on threats, demands, denunciations, and other forms of verbal conflict. Do these efforts at verbal coercion have any effect on terrorist behavior? This analysis focuses on the Islamic State in Iraq and Syria (ISIS), which continues to be the world's deadliest terrorist group and was responsible for recent high-profile attacks in Baghdad, Vienna, Kabul, and Russia. We use Bayesian structural vector autoregression models to analyze daily event data on interactions between ISIS and foreign governments for the 2014–2020 period. We find that verbal conflict initiated by governments not only failed to deter ISIS but in fact increased the frequency of ISIS's attacks on civilians. Additional empirical analyses, combined with evidence from ISIS's publications and public statements, suggest that this effect resulted from a perceived credibility deficit. Extremists use terror attacks to signal that they have the capabilities and willingness to inflict pain and suffering on civilian targets. Government attempts to coerce extremist groups verbally, rather than militarily, reflect an underestimation of the group's capabilities and resolve. In an effort to solidify their reputations, extremists engage in further violence toward civilians, thus leading to worse humanitarian consequences. We extend the analysis to Al-Qaeda in Iraq and Boko Haram and find similar results.

Knowledge Representation Learning Method Based on Semantic Enhancement of External Information

Background: Knowledge representation learning aims at mapping entity and relational data in knowledge graphs to a low-dimensional space in the form of vectors. The existing work has mainly focused on structured information representation of triples or introducing only one additional kind of information, which has large limitations and reduces the representation efficiency. Objective: This study aims to combine entity description information and textual relationship description information with triadic structure information, and then use the linear mapping method to linearly transform the structure vector and text vector to obtain the joint representation vector. Methods: A knowledge representation learning (DRKRL) model that fuses external information for semantic enhancement is proposed, which combines entity descriptions and textual relations with a triadic structure. For entity descriptions, a vector representation is performed using a bi-directional long- and short-term memory network (Bi-LSTM) model and an attention mechanism. For the textual relations, a convolutional neural network is used to vectorially encode the relations between entities, and then an attention mechanism is used to obtain valuable information as complementary information to the triad. Results: Link prediction and triadic group classification experiments were conducted on the FB15K, FB15K-237, WN18, WN18RR, and NELL-995 datasets. Theoretical analysis and experimental results show that the DRKRL model proposed in this paper has higher accuracy and efficiency compared with existing models. Conclusion: Combining entity description information and textual relationship description information with triadic structure information can make the model have better performance and effectively improve the knowledge representation learning ability.

Geopolitical risks and energy uncertainty: Implications for global and domestic energy prices

This paper investigates the interaction between global geopolitical risks and global energy uncertainty by focusing on their implications for global and domestic energy prices of 157 countries. The empirical investigation is based on a structural vector autoregression model covering the monthly sample period between 1996 m1-2022m10, where global real economic activity is controlled for. The results show that a unit shock to global geopolitical risk (normalized to one standard deviation) results in about 1.13 units of an increase in global energy uncertainty (normalized to one standard deviation) in the long run (after two years), whereas the corresponding effects on global energy prices are statistically insignificant. In contrast, a unit shock to global energy uncertainty results in about 52 % (57 %) of a reduction in global energy prices (global economic activity), acting like negative global demand shocks. When statistically significant country-specific results are considered in the long run, a positive shock to the global geopolitical risk affects domestic energy prices positively (negatively) in 10 % (10 %) of oil producing countries, 32.1 % (19.7 %) of non-oil producing countries, 47.2 % (0 %) of advanced economies, 55 % (0 %) of euro area countries, 25 % (22.4 %) of emerging markets, and 22.2 % (26.7 %) of developing countries. In comparison, a positive shock to the global energy uncertainty affects domestic energy prices positively (negatively) in 5 % (40 %) of oil producing countries, 3.6 % (54 %) of non-oil producing countries, 0 % (61.1 %) of advanced economies, 0 % (50 %) of euro area countries, 3.9 % (56.6 %) of emerging markets, and 6.7 % (37.8 %) of developing countries. Important policy implications follow regarding the energy security of countries.

WITHDRAWN: the Impact of Monetary Policy on the Liquidity of Bond Market——Based on Chinese Local Government Bond Market

The bond market serves dual roles in fiscal and financial spheres, playing a crucial role in coordinating monetary policy. This paper investigates the impact of quantitative and price-based monetary policies on the liquidity level of China’s bond market. A comprehensive index measuring the liquidity of the local bond market is constructed using a combination weighting method that integrates the entropy method and the coefficient of variation. Employing the time-varying stochastic volatility structure vector autoregression (TVP-SV-SVAR) model on data spanning from 2013 to 2021, the study empirically compares the impulse response of local bond market liquidity to monetary policy shocks. Findings reveal that both types of monetary policy operations exhibit asymmetric, nonlinear, and time-varying impacts on bond market liquidity. Quantitative monetary instruments induce deeper impulse responses, with longer-lasting effects. These conclusions offer insights for monetary policy reforms and bond market development in China.

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.