Vector Autoregression Research Articles

Can stronger governance and institutional quality drive growth through inward foreign direct investments in BRICS nations?

Background: This research focusses on the significance of institutional quality (INSQ) and governance in shaping the relationship between foreign direct investments (FDI) and growth. It specifically examines the BRICS nations (Brazil, Russia, India, China and South Africa) because of their economic significance in the global economy.Aim: This study aims to analyse the impact of INSQ and governance on sustainable growth, with a particular focus on its effects through the channels of FDI in the BRICS countries.Setting: Annual panel data from the Organisation for Economic Cooperation and Development (OECD) statistics, the United Nations Conference on Trade and Development (UNCTAD) statistics and the World Bank indicators spanning two decades (2000–2022) are used to analyse BRICS nations.Methods: The research study employed the Bayesian time-varying coefficient vector autoregression (BTVC-VAR) model to achieve the objective of the study.Results: The findings indicate that there is no long-term relationship between FDI, INSQ and economic growth in the BRICS countries. However, there is a noticeable co-movement among these variables in the short run.Conclusion: Given the obtained results, the policymakers should prioritise efforts to strengthen institutional capacity in the short term while focussing on the Sustainable Development Goals (SDG-8 and SDG-16).Contribution: The existing studies have assumed static economic, social and political conditions, potentially failing to accurately depict the complexities of an actual economy. This study offers methodological innovation by employing Bayesian time-varying coefficient vector autoregression (BTVC-VAR), enabling coefficients to adapt to evolving economic conditions over time. This effectively captures the dynamic nature of variables and provides reliable estimates.

Public debt determinants: A time-varying analysis of core and peripheral Euro area countries

This study employs a Bayesian Interacted Panel VAR model to estimate time-varying Generalized Forecast Error Variance Decomposition, analyzing how key determinants affect debt in Core and Peripheral Euro Area countries. Results highlight varying effects of determinants across periods and subgroups.

Macroeconomic effects of monetary policy in Japan: an analysis using interest rate futures surprises

AbstractWe estimate the effects of monetary policy on the aggregate economy in Japan during the last three decades when the effective lower bound (ELB) on interest rates was occasionally binding. We use monetary policy surprises from the interest rate futures market as the external instrument to identify monetary policy shocks in the vector autoregressive model. We find that monetary policy has been effective in Japan during the last three decades, and the effect was more persistent in the ELB regime than in the non-ELB regime. In a simulation exercise, we further show that a New Keynesian model with forward guidance can replicate our empirical finding.

Financial Markets Integration and the Consequences for Financial Stability

Financial integration, as part of the financial market union, implies closer interaction between global economies. The positive contribution of digitization can be found in the specialized literature, but it fails to provide an overview of the link between financial intermediation indicators and the use of digitization, taking into account the impact on the individual. In this sense, the motivation of this research consists in emphasizing therole of digital financial services in reducing poverty, having a beneficial impact on economic development and the economy welfare.The study relevance resides in capturing the Global Findex Index trend, by measuring the use of financial services every 3 years, starting in 2011. As a result, the research aims to analyze the financial intermediation degree, and, implicitly, digitization, based on a survey of 140 economies, using a Vector Autoregressive model, impulse response functions and the variance decomposition of the included macroeconomic variables.The research resultssummarize the fintech contribution in diminishingthe financial instability risks and outlining the policy implications for governments, benefits such as money laundering and reducing corruption.

Diagnostic analystics in the Bayesian vector autoregressive model

The vector autoregressive model is extensively employed in macroeconomics, finance, and the natural sciences. However, it often encounters the issue of over-parametrization, resulting in undesirable behavior within the model. In this paper, we utilize the Bayesian vector autoregressive model as a tool to tackle this problem. Additionally, we employ the Bayesian local influence method to detect possible extreme observations under the model. We use posterior inference to estimate related parameters and construct Bayesian perturbation schemes for priors, variance, and data perturbations. Bayesian local influence is conducted based on three objective functions: Bayes factor, ϕ divergence, and posterior mean. To demonstrate the effectiveness of the diagnostics, we conduct numerical simulations. In the real data example, we use US inflation, unemployment, and interest rate data to verify this method's effectiveness.

Do Illegal Migration Affect Tourism Arrivals in Greece? A Vector Autoregression Analysis

Introduction: This paper explores the impact of illegal migration on the tourism sector in Greece. Utilizing a Vector autoregression (VAR) methodology, the research examines the relationship between the influx of illegal migrants and the number of tourist arrivals. The study presents the migration as a phenomenon that takes place dynamically the last decades in Europe in general and the Greek territory particular. By providing, empirical evidence on the short-term and long-term impacts of illegal migration on tourism in Greece, this study contributes to the broader discourse on migration and economic stability. The research's findings reveal that illegal migration does not significantly influence tourism arrivals in Greece. This indicates that tourists’ decisions to visit Greece are driven by factors other than concerns related to illegal migration. Objective: The objective of this study is to investigate the relationship between the influx of illegal migrants and the number of tourist arrivals Theoretical Framework: In this topic, the main concepts and theories that underpin the research are presented: the Model of cyclicity, Lee’s migration model. Method: The research examines the relationship between the influx of illegal migrants and the number of tourist arrivals over a specific period. The methodology used for this research is Vector autoregression analysis, with monthly data for the period January 2015 to December 2023. This analytical approach allows for the examination of the dynamic interactions and potential causality between the two variables over time. Results and Discussion: The results obtained revealed reveal that illegal migration does not significantly influence tourism arrivals in Greece. Changes in the number of migrants arriving do not predict changes in tourist arrivals, and changes in tourist arrivals do not predict the number of migrants arriving in Greece. The outcome is positive for the Greek tourism industry, which is of a great importance for the country’s GDP and employment politics. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of tourism. These implications could encompass the socio-economic sector of the country. Originality/Value: This study contributes to the literature by a vector autoregression analysis to the broader discourse on migration and economic stability. The relevance and value of this research are evidenced by the findings, that reveal that illegal migration does not significantly impact tourism arrivals in Greece. This indicates that tourists’ decisions to visit Greece are driven by factors other than concerns related to illegal migration.

The Influencing Factors of RMB Exchange Rate in the Background of Anti-globalizationAn Empirical Evidence Based on VAR Model

Affected by the epidemic and geopolitics, global economic growth has slowed down, trade protectionism has begun to emerge, and anti-globalization sentiment has begun to sprout. With the pace of the Federal Reserve's interest rate hike, the global exchange rate, especially the RMB exchange rate, is gradually under pressure, which has a great impact on the economy and financial sector of the whole country. The research topic of this paper is the influencing factors of the RMB exchange rate in the background of anti-globalization, and the empirical analysis of the VAR model based on Stata software. The study found that rising domestic interest rates, a rising balance between import and export trade and rising uncertainty about US economic policy will lead to the appreciation of the RMB, while the growth of money supply will lead to the depreciation of RMB. It is worth noting that the RMB exchange rate is less affected by the uncertain economic policy factors in the United States, which shows that under the current anti-globalization and international game background, China still has a relatively independent control of the foreign exchange market.

Impulse Response of External Debt Stock and Gross Capital Formation on Economic Growth in Bangladesh: Var Model, Granger Causality and Variance Decomposition Analysis

Purpose: This study employs an annual time series data set from 1987 to 2018 gathered from the World Development Indicators (WDI), 2021 to analyze the properties of external debt stock on financial progress(proxied by economic growth) in Bangladesh. Methodology: The objectives are determined using a variety of econometric techniques, including the Phillips-Perron (PP) unit root test, Var model, Johansen co integration test, Impulse Response Function (IRF), Variance decomposition function, and Granger causality test. The get the result of the Phillips-Perron (PP) unit root test which indicates the stationarity of all the variables at the initial difference. Findings: There is an expectant impact on financial progress (proxied by Gross Domestic Product Per capita) in Bangladesh, according to the short run Var model with the two independent variables External Debt Stock (proxied by public debt) and second lag of Gross Fixed Capital. Block Exogeneity/ Wald Tests and Johansen Co-integration test show that both lnEDS and lnGCF have a strong constructive impact on Bangladesh's financial progress in the short as well as long run. Pairwise, Granger Causality also indulgence the Bi-directional causality between lnGDPPC and lnEDS and Uni-directional causality from lnGCF to lnGDPPC. The influences of lnEDS and lnGCF on lnGDPPC are rising in the long term, showing that both variables are significantly exogenous on the dependent variable. Practical Implication: Bangladesh is now a fast-growing economy with several mega projects for infrastructural, human, and technological advancements such as roads, train, power, education, health, information technology, gas, and energy. External debt service and gross capital formation are crucial for Bangladeshi economic growth. The government should establish a priority aim for long-term economic growth by implementing suitable measures to control, maintain, regulate, and stabilize inflation at a reasonable level. Originality/Value: In a developing country, Savings within the country are insufficient to support investment for financial progress, according to the dual-gap theory and the Keynesian theory of public debt. As a result, bridging this gap for the required level of investment and achieving targeted economic growth will necessitate a bigger inflow of foreign aid or external borrowings. That’s why this study is very important. Limitations: Some limitations of this study include the absence of recent data with the factors. Also numerous factors can influence Bangladesh economic growth for instances, remittances, exchange rates and foreign direct investment all have a crucial impact on GDP growth.

Pure contagion vs. financial interconnection in the subprime crisis context: Short- and long-term dynamics

This paper examines the difference between pure contagion and financial interconnection by studying the U.S. and some American and Asian markets in the subprime crisis context. These markets are affected by the mortgage crisis, with data available from January 1, 2003 to December 30, 2011. The paper first identifies the turmoil period via the wavelet technique and adopts cointegration and Granger causality approaches by estimating vector autoregressive (VAR) and vector error correction models (VECM) models. Based on daily returns from stock market indices in five American countries (Mexico, Brazil, Canada, Argentina, and the U.S.) and eight Asian ones (Hong Kong, Japan, India, Indonesia, Malaysia, Singapore, Korea, and China), the results show eight cases of pure contagion and 10 cases of financial interconnection. In addition, there were high co-movements in the short term and low co-movements in the long term for financial interconnection cases. These findings have several implications for investors looking to diversify their portfolios internationally and for portfolio managers to expect and limit market risk. The results provide additional guidance to regulators and policymakers.

Substitution or creation? Identifying the role of artificial intelligence in employment

Recognising the significant role of artificial intelligence in the labour market is essential for China to develop sustainably. The research utilises the mixed frequency vector auto-regression (MF-VAR) technique, which would innovatively incorporate data at different frequencies into one model to identify the intricate correlation between the monthly artificial intelligence index (AII) and the quarterly unemployment rate (UR) in China. Through comparison, the MF-VAR method has a more substantial explanatory power than the low-frequency VAR (LF-VAR) model, the impulse responses of the former reveal that AII exerts favourable and adverse influences on UR. Among them, the positive effect occurs on the AII in the first and second months. In contrast, the negative one appears on the AII in the third month, highlighting that artificial intelligence has both stimulating and inhibiting effects on the labour market in China. By analysing UR’s predictive error variance decomposition, the total impact of China’s artificial intelligence technology on employment is a substitution; this outcome is accordant with the theoretical dis¬cussion. In the new round of scientific and technological revolution and industrial transformation, meaningful recommendations for China would be put forward to avert the wave of unemployment brought by the development of artificial intelligence technology.

Examining contemporaneous and temporal associations of real-time suicidal ideation using network analysis.

Suicidal ideation arises from a complex interplay of multiple interacting risk factors over time. Recently, ecological momentary assessment (EMA) has increased our understanding of factors associated with real-time suicidal ideation, as well as those predicting ideation at the level of hours and days. Here we used statistical network methods to investigate which cognitive-affective risk and protective factors are associated with the temporal dynamics of suicidal ideation. The SAFE study is a longitudinal cohort study of 82 participants with current suicidal ideation who completed 4×/day EMA over 21 days. We modeled contemporaneous (t) and temporal (t + 1) associations of three suicidal ideation components (passive ideation, active ideation, and acquired capability) and their predictors (positive and negative affect, anxiety, hopelessness, loneliness, burdensomeness, and optimism) using multilevel vector auto-regression models. Contemporaneously, passive suicidal ideation was positively associated with sadness, hopelessness, loneliness, and burdensomeness, and negatively with happiness, calmness, and optimism; active suicidal ideation was positively associated with passive suicidal ideation, sadness, and shame; and acquired capability only with passive and active suicidal ideation. Acquired capability and hopelessness positively predicted passive ideation at t + 1, which in turn predicted active ideation; acquired capability was positively predicted at t + 1 by shame, and negatively by burdensomeness. Our findings show that systematic real-time associations exist between suicidal ideation and its predictors, and that different factors may uniquely influence distinct components of ideation. These factors may represent important targets for safety planning and risk detection.

Research on the synergy of the water–energy–food composite system in the Beijing–Tianjin–Hebei region

ABSTRACT Water, energy, and food are interdependent and mutually restrictive, and changes in any one resource may have an impact on the others. In order to promote the sustainable utilization of resources, it is important to study the synergy of the water–energy–food composite system (WEFCS). Taking the Beijing–Tianjin–Hebei region as an example, this study adopts the composite system synergy degree model to quantitatively measure the synergy degree of WEFCS from 2011 to 2021, then constructs an evolutionary model to identify the interactions between the subsystems, and finally analyzes the interactions dynamically by using the panel vector autoregression model. The results show that the WEFCS synergy level in the Beijing–Tianjin–Hebei region is generally basic and shows low stability during the study period. Regional subsystems are more dependent than collaborative or competitive. Moreover, the regional water subsystem has a short-term positive impact on the energy subsystem, the energy subsystem has a short-term negative impact on the food subsystem, and the food subsystem has a delayed and complex influence on the water and energy subsystems, with the water subsystem being particularly affected. This study provides a decision-making basis for policymakers to optimize resource allocation.

Sources of macroeconomic fluctuations in Tunisia: a structural VAR approach

Sources of macroeconomic fluctuations in Tunisia: a structural VAR approach

Modelling the Relationship between Air Pollution and Economic Growth in Somalia

Throughout human history, there has been a clear connection between pollution and the economy. However, the relationship between environmental harm and economic progress is complex, and disagreements between various academic disciplines have made it difficult to understand this phenomenon fully. A unit root test was conducted to gain more insight, which proposed the possibility of further empirical analysis. The Johansen cointegration method was also used to show a long-term connection between air pollution and economic growth. At the same time, the vector autoregressive model (VAR) indicated a short-term connection between the two. A Vector Error Correction Model (VECM) was then employed to estimate the relationship, and the findings confirmed the existence of the Environmental Kuznets Curve (EKC). The EKC demonstrates that economic growth has positive and negative effects on air pollution. The Granger causality test conducted in Somalia showed a causal link between economic growth and air pollution. The EKC model further supported this finding by demonstrating that economic growth has a statistically significant positive impact on greenhouse gas (GHG) emissions. This implies that Somalia has seen increased GHG emissions, leading to environmental degradation. Therefore, the Somali government must prioritise the implementation of a sustainable energy policy to improve air quality and prevent further environmental deterioration.

The Relationship between Renewable Energy Consumption and Economic Growth: Insights from Iceland and Azerbaijan

Iceland leads globally in per capita green energy production, while oil-dependent Azerbaijan endeavors to transition its energy policy towards sustainable alternatives. Investigating the impacts of renewable energy consumption on the economy represents a significant contemporary scientific inquiry. The objective of this research is to empirically examine the causal relationship between GDP per capita, a key metric of economic growth, and renewable energy consumption, a fundamental aspect of the green economy. In this research, the Toda-Yamamoto causality test framework of the vector autoregressive (VAR) model was utilized to examine the causal relationship between the variables. The outcomes of this investigation reveal a significant causality between renewable energy consumption and economic growth in Iceland and Azerbaijan.

Spillover Effects between Oil, Gold, Stock, and Exchange Rate Returns in Thailand: An Extended Joint Connected TVP-VAR Approach

The objective of this study is to analyze the spillover effects among the returns of oil, gold, the stock market, and exchange rates in Thailand. Using the time-varying parameter vector autoregression model (TVP-VAR) with extended joint connectedness and data from April 2002 to March 2024, our analysis reveals a moderate level of the dynamic linkage among these returns. We observe that the dynamic connectedness among all returns varies over time, influenced by global economic events and country situations. Notably, in Thailand's landscape, stock market and gold returns act as net shock transmitters, with the stock market exhibiting the highest volatility among all variables, while oil and exchange rate markets function as net recipients. These insights significantly contribute to understanding asset and commodity markets and offer valuable policy implications for effectively managing these markets.

Term structures and firm dynamics: A FAVAR approach

This study employs a Factor-Augmented Vector Auto-Regression approach with Chinese bond markets and aggregated firm-level data to investigate the impact of term structure shocks on firm dynamics and aggregate firm-level variables. We address the potential endogenous issue using a novel instrumental variable based on textual analysis, and find that term structure shocks have long-run and persistent influences on several firm-level outcomes, and that firm dynamics play a critical role in the transmission of term structure shocks.

Development of Econometric Models for Financial Performance Forecasting in Companies

This study develops an econometric model to predict corporate financial performance. The goal is to improve the accuracy of predictions by analysing relevant economic and financial variables. The model combines statistical and econometric analysis to identify significant input variables such as asset turnover, firm size, capital structure, and liquidity. The study also highlights the importance of external factors, such as environmental policies and knowledge management, that affect corporate financial performance. Using ARIMA and VAR models, the study shows that selecting the correct parameters, such as the number of lags, is critical to improving prediction accuracy. The developed model is evaluated based on RMSE, MAD, and MAPE metrics, which show that the econometric model offers more accurate predictions than the classical statistical model. These results contribute significantly to understanding corporate financial performance dynamics and can be a reliable tool in strategic decision-making across various industry sectors.

Evaluating the spatial mismatch between population and factor endowments: The case of the European Union

ABSTRACT Considerable research has been conducted on the trade-off between reducing regional inequality and promoting regional growth. However, there is a lack of empirical research exploring the relationship between this trade-off and the spatial mismatch between the distribution of regional populations and the spatial distribution of regional factor endowments. This paper assesses the existence of spatial mismatches between production factors and populations in European regions, with a focus on how these imbalances might affect regional disparities and growth. First, we compute spatial mismatch indices for capital stock and employment in relation to the distribution of population. Second, we use a vector autoregressive (VAR) approach to identify dynamic interactions in the short run. The results indicate that the spatial mismatch between population and labour plays a key role in the trade-off between European economic growth and regional inequality. The findings suggest that eliminating spatial mismatches between the spatial distribution of population and labour could lead to an increase in both economic growth and regional inequality. Moreover, the implementation of regional policies for resource allocation based on population-related criteria does not appear to mitigate regional economic inequality.

Debt erosion: Asymmetric response to demand and supply shocks

This paper explores the effect of inflation supply and demand shocks on government debt for a sample of 32 advanced economies and 24 emerging markets from 1970 to 2022. The shocks are identified using a sign-restricted structural vector autoregression model with quarterly data. Estimations of dynamic panel regressions and local projections suggest that supply shocks lead to persistent increases in government debt, while demand shocks result in long-lasting declines. Furthermore, high debt levels amplify the impacts of both supply and demand shocks by more than three times. Specifically, supply shocks contribute to an increase in debt through elevated borrowing costs and prolonged depreciation, whereas demand shocks erode debt through persistent improvements in the primary balance, driven by increased revenues.