Vector Autoregression Analysis Research Articles

Uncertainties of Economic Policy and Government Management Stability Played Important Roles in Increasing Suicides in Japan from 2009 to 2023.

Standardized suicide mortality rates per 100,000 (SMRs) in Japan consistently decreased from 2009 to 2019 but increased from 2020. The causes of these temporal SMR fluctuations remain to be clarified. Therefore, this study was conducted to identify the causalities underlying the recently transformed fluctuations of suicide mortality in Japan. Monthly suicide numbers disaggregated by sex and social standing, and political uncertainty indices, such as economic policy uncertainty (EPU) and government management instability (AENROP), were obtained from Japanese government databases. Interrupted time-series analysis was performed to analyze temporal fluctuations of SMRs disaggregated by sex/social standing associated with the three General Principles of Suicide Prevention Policy (GPSPP) periods and the COVID-19 pandemic. Panel data and vector autoregressive analyses were conducted to investigate causalities from political uncertainties to SMRs. During the first and second GPSPPs (2009-2017), all SMRs disaggregated by sex and social standing decreased, whereas those of unemployed females did not change. During the third GPSPP (2017-2022), decreasing trends in all SMRs were attenuated compared to previous periods. All female SMRs, except unemployed females, showed sharp increases synchronized with the pandemic outbreak. No male SMRs showed sharply increasing at the pandemic outbreak. SMRs of unemployed males/females drastically increased in the later periods of the pandemic, while SMRs of employed and multiple-person/single-person household males did not increase during the pandemic. SMR of unemployed males was positively related to AENROP but not EPU. Other male SMRs were positively related to EPU/AENROP. On the contrary, not all female SMRs were related to EPU/AENROP. Increasing AENROP generally contributed to increasing male SMRs throughout the observation period; however, susceptibility to AENROP and/or political information might have unexpectedly contributed to suppressing the sharply increasing male SMRs induced by large-scale social shocks (the COVID-19 pandemic outbreak) in Japan.

Strategizing Low Carbon Urban Planning Through Environmental Impact Assessment by Artificial Intelligence Driven Carbon Foot-print Forecasting

Addressing the associated rise in Carbon Emissions (CE) as smart cities expand becomes paramount. Effective low-carbon urban planning demands robust, precise assessments. This research introduces a cutting-edge solution via an Artificial Intelligence (AI) -driven Carbon Footprint (CF) impact assessment. A detailed dataset, collected over 3 years, was harnessed to gather insights into vital urban factors, including CE, Energy Consumption (EC) patterns, variations in land use, transportation dynamics, and changes in air quality. The cornerstone of this research is developing the Multi-modal Stacked VAR-LSTM model. This model proposes to provide accurate CF predictions for urban environments by merging the capabilities of Vector Autoregression (VAR) with Long Short-Term Memory (LSTM) neural networks. The process encompasses dedicated assessments for each data segment, harnessing VAR to delineate interdependencies and refining these predictions with the LSTM network using the residuals from the VAR analysis. By interweaving AI-driven carbon footprint impact assessments into the urban planning discourse, this study underscores the vast potential in sculpting future urban development strategies that are sustainable and sensitive to carbon impact.

Kausalitas Antara Variabel Makro dan Stunting di Sumatera Barat Periode 2021 -2023

This research intends to look at the causal relationship between stunting, unemployment, poverty, income inequality, human development index in West Sumatra for the period 2021 - 2023. The type of data used is secondary data in the form of panel data from the Central Statistics Agency (BPS) and the Indonesian Nutrition Status Survey from 2021 to 2023 mapping of 19 regencies/cities in West Sumatra. The data analysis used is vector auto regression analysis (VAR) and vector error correction model (VECM). In inductive analysis there are several tests, namely Optimum Lag Test, Model Stability Test, Johansen Cointegration Test, Granger Causality Test (Granger Causality), VECM Estimation, Impulse Response Function (IRF) Analysis, Forecast Error Variance Decomposition (FEVD) The results of this study show that there is a significant relationship between the human development index and income inequality. There is a significant relationship between unemployment and poverty and income inequality significant relationship between stunting and HDI, poverty, income inequality and unemployment.

Measuring Business Cycle Stylized Facts in Selected Oil-Producing Economies: A Comparative Study

AbstractWe document the stylized facts relating to the contemporary business cycle in eleven oil-producing economies that account for over 65% of global oil production using a set of macroeconomic aggregates covering the period 1981 to 2021. In the context of Nigeria, a vector autoregression analysis is used to measure a one standard deviation oil price shock on time series such as trade balance, real GDP, inter alia. Our study is unique in that we compared four different detrending techniques, including Hodrick–Prescott (HP), Baxter-King, Christiano–Fitzgerald, and Butterworth filters. This is a departure from the conventional method of relying on the HP filter, which has been known to have limitations, particularly in the context of oil-producing economies. Our findings indicate that the selected oil-producing economies with countercyclical inflation patterns suggest that supply shocks drive the business cycles. Conversely, the oil-producing economies with inflation in sync (procyclical) with the business cycle suggest that demand shocks may be the primary driver of business cycle variations. Additionally, our study shows that oil price innovations lead to higher inflation in Nigeria.

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.

Resilience of developing economies to external shocks: empirical evidence from CEMAC countries

PurposeThis study aims to investigate the effects of three significant events – the 1994 CFA currency depreciation, the 2008 Global Financial Crisis (GFC), and instances of political coups – on the relationships between FDI inflow, economic growth, and governance within the Central African Economic and Monetary Community (CEMAC) countries. It seeks to evaluate how these events influence the linkages between FDI, economic growth, and governance, to aid the understanding of responses to external shocks and internal political disruptions.Design/methodology/approachThe study employs a panel Vector Autoregression (VAR) analysis using data from 1990 to 2019 by exploring the dynamic relationships among FDI inflow, economic growth, and aggregate governance indicators within the CEMAC sub-region. The analysis was conducted utilizing the EViews software package, facilitating robust examination through the introduction of the Bayesian VAR to facilitate the interpretation of parameters and the data.FindingsThe results indicate that, contrary to initial hypotheses, growth and governance do not emerge as determinants for attracting FDI within the CEMAC sub-region. However, governance stands out as a crucial determining factor for economic growth. Furthermore, the study suggests that the 1994 CFA currency depreciation, the 2008 GFC, and instances of political coups did not significantly impact FDI, growth, and governance within these countries. Despite the potential vulnerability of the CEMAC countries to external shocks, the effects of these events on the dynamics of FDI, economic growth, and governance were not apparent. Notably, political instability, as evidenced by coups, emerges as a significant factor shaping the interactions between FDI, growth, and governance in CEMAC countries.Research limitations/implicationsThese findings have significant implications for policymakers and stakeholders in the CEMAC countries. Understanding that governance has a central role in driving economic growth places great importance of prioritizing governance reforms to foster sustainable development. Moreover, the identification of political instability as a key determinant affecting the relationships between FDI, growth, and governance emphasizes the need for political stability and effective governance structures to attract and sustain FDI inflows as well as foster economic growth.Originality/valueThis study contributes to the existing literature by offering insights into the linkages between FDI, economic growth, governance, and external shocks within the CEMAC sub-region. By examining the specific impacts of the 1994 CFA currency depreciation, the 2008 GFC, and political coups on these dynamics, the study provides original perspectives on the resilience of CEMAC countries to external and internal disruptions.

Reaksi Pasar Modal terhadap Faktor Ekonomi dan Faktor Non Ekonomi di Indonesia

The purpose of this study is (1) to examine how much influence economic and non-economic factors have on the movement of the Jakarta Composite Index (CSPI), (2) to examine the most dominant factor in the movement of the Composite Stock Price Index (CSPI). The method in this study uses monthly data and is processed using the Vector Auto Regression (VAR) analysis technique. the results of the variance decomposition test before social / political turmoil showed that the most dominant contribution was recorded at the CSPI itself 61 percent, with economic factors 21 percent and non-economic factors 18 percent. However, the results of the variance decomposition test after social / political turmoil showed that the most dominant contribution was recorded in non-economic factors, 52 percent, with economic factors 36 percent, and CSPI itself at 18 percent. Thus, there are differences before and after social / political turmoil.

An extension analysis of Amihud's illiquidity premium: Evidence from the Taiwan stock market

This study proposes a novel illiquidity measure that excludes overnight returns and incorporates the recency effect, and investigates whether this new metric more effectively captures the illiquidity premium in the Taiwan stock market relative to Amihud's (2002) measure and other illiquidity measures that exclude overnight returns or account for the recency effect. Cross-sectional and time-series asset pricing tests reveal that our measure dominates other illiquidity measures, yielding a more profitable long–short strategy. Vector autoregressive analysis confirms these results for the aggregate market. Our study contributes to the measurement of liquidity in emerging markets and suggests potential improvements for portfolio management and asset pricing models.

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.

Sectoral Composition of Outward FDI and its Impact on Export Diversification in Saudi Arabia

This research explores the relationship between outward foreign direct investment (FDI) and export diversification in emerging economies, with a focus on the case study of Saudi Arabia. Utilizing a multi-equation model and vector autoregression analysis, the study investigates various factors such as the level and sectoral composition of outward FDI, income levels, institutional factors, and regional trade agreements. The findings suggest that while outward FDI can influence export diversification, its impact varies based on factors such as the sectoral distribution of investments, income levels, and institutional quality. Additionally, cointegration and vector error correction models reveal long-term relationships among key variables, highlighting the complex interplay between economic development, governance, and investment patterns. Impulse response analysis sheds light on the dynamic nature of shocks and their effects on the economy over time, emphasizing the interconnectedness of variables. Variance decomposition analysis further elucidates the significance of shocks in explaining fluctuations in model variables, underscoring the systemic interdependencies at play. Overall, the study provides valuable insights into the mechanisms through which outward FDI influences export diversification and economic development in emerging economies like Saudi Arabia and institutional reforms are essential; strengthening institutions and improving regulatory frameworks can amplify the positive effects of outward FDI on export diversification.

The Effect of Over-Confidence on Jiwasraya's 2017-2020 Payment Defaults

Overconfidence is a term that refers to the human tendency to estimate their abilities, knowledge, or results with more confidence than they should. PT. Jiwasraya Insurance (Persero), the oldest insurance company in Indonesia which is owned by BUMN, was declared to have failed to pay its customers' policies in 2020. This failure to pay was caused by errors in managing customer funds, namely by collecting funds for JS Products. Saving Plan and investing with customer funds in high risk stocks. The purpose of this research is to find out why Jiwasraya experienced payment failure. Therefore, the variable prices of high-risk shares purchased by companies from 2017 to 2020 is used as a secondary variable. Secondary data was obtained from the Indonesian Stock Exchange and the Financial Services Authority. This study uses. Vector Auto regressive (VAR) analysis technique with Stata/IC 15.0 software. The research results show that it is the Jiwasraya company's share price that influences Jiwasraya's shares in the short term. In the long term, it shows that the share price of Semen Baturaja does not have a significant effect on Jiwasraya's share price. Meanwhile, the share price of the West Java Development Bank has a significant negative effect on Jiwasraya's share price. Then, PP Properti's share price has a significant positive effect on Jiwasraya's share price. The results of this research can be used as consideration for companies and investors in making investment decisions so as not to take high-risk actions because of too high self-confidence.

Offshoring, firm-level adjustment and labor market outcomes

This paper studies how the China shock affects unemployment rates and wage inequality across high-skilled and low-skilled workers in the United States, with particular emphasis on the dynamic and general equilibrium channels of firms' production locations and entry decisions. To shed light on the subject, I build a two-country trade-in-task model with firm heterogeneity, search-and-matching labor market frictions, and firms' endogenous selections into entry and offshoring. The model, consistent with evidence from vector autoregression analyses, uncovers important dynamics with implications for the impact of the China shock on U.S. worker inequality. Namely, it shows association between a decrease in offshoring costs and a short-lived increase in low-skilled unemployment in the source country, a longer-term decline in high-skilled unemployment, a transient expansion of the wage gap between high- and low-skilled workers, and an increase in firm entry.

Correlation between energy consumption and economic growth – based on the perspective of panel vector autoregressive analysis

ABSTRACT Efforts are underway to balance economic development and ecological preservation by transforming the energy structure. This study uses the Cobb-Douglas production function to examine the relationship between energy and economic growth. Through panel data and regression models, 32 countries are analyzed in terms of global energy consumption and economic development. Countries are further grouped based on energy intensity and economic level to explore dynamic relationships. Previous studies lacked understanding of this relationship under varying conditions, while this study proposes a novel approach. Results indicate a one-way relationship between renewable energy consumption and economic growth. Further analysis reveals that countries with higher energy intensity and economic level experience a negative impact on economic growth from increased renewable energy consumption. These research findings have implications for government decision-making.

The Impact of Fiscal Policy on Macroeconomic Conditions in Pakistan: Insights from a Structural Vector Autoregression Analysis

The Impact of Fiscal Policy on Macroeconomic Conditions in Pakistan: Insights from a Structural Vector Autoregression Analysis

Spillover effects of energy transition metals in Chile

This paper examines the impact of spillover effects of energy transition metals in Chile. The study aims at identifying the causal relationships among these energy transition metals and other major assets like oil, gold, and bitcoin, and how they shape Chile’s economy, both pre-COVID-19 and during the first months of the pandemic. Our Structural Vector Autoregressive analysis is carried out with daily frequency data for two separate periods, the pre-COVID-19 analysis (the whole of 2018) and the COVID-19 analysis (Feb 2020 to end of 2020). Our results suggest that Chile has been highly responsive to renewable energy shocks during COVID-19, in concert with an increased responsiveness to traditional oil-driven shocks. This lends weight to the idea that traditional energies are still relevant and concomitant with energy transition, especially in a small open resource-oriented economy like Chile. In addition to that, bitcoin shocks seem to have contributed to Chile’s transition, likely as a result of bitcoin’s extensive use of energy and its potential to increase renewable energy capacity. Policywise, our findings call for a watchful eye on regulatory constraints, excessively high royalties, and the maintenance of stabilization funds to mitigate uncertainty, all of which can affect Chile’s competitive edge at an untimely hour.

The Effects of Economic Cycle Shocks on Polluting Emissions in Developing Countries: Insights from a Panel Vector Autoregression Analysis

The Effects of Economic Cycle Shocks on Polluting Emissions in Developing Countries: Insights from a Panel Vector Autoregression Analysis

Estimating Posterior Sensitivities with Application to Structural Analysis of Bayesian Vector Autoregressions

The inherent feature of Bayesian empirical analysis is the dependence of posterior inference on prior parameters, which researchers typically specify. However, quantifying the magnitude of this dependence remains difficult. This article extends Infinitesimal Perturbation Analysis, widely used in classical simulation, to compute asymptotically unbiased and consistent sensitivities of posterior statistics with respect to prior parameters from Markov chain Monte Carlo inference via Gibbs sampling. The method demonstrates the possibility of efficiently computing the complete set of prior sensitivities for a wide range of posterior statistics, alongside the estimation algorithm using Automatic Differentiation. The method’s application is exemplified in Bayesian Vector Autoregression analysis of fiscal policy in U.S. macroeconomic time series data. The analysis assesses the sensitivities of posterior estimates, including the Impulse response functions and Forecast error variance decompositions, to prior parameters under common Minnesota shrinkage priors. The findings illuminate the significant and intricate influence of prior specification on the posterior distribution. This effect is particularly notable in crucial posterior statistics, such as the substantial absolute eigenvalue of the companion matrix, ultimately shaping the structural analysis.

Impacts of Working Hours, Wages, and Regular Employment Opportunity on Suicide Mortalities of Employed and Unemployed Individuals before and during the COVID-19 Pandemic in Japan.

Standardized suicide mortality rates per 100,000 population (SMRs) in Japan consistently decreased from 2009-2019, but these decreasing trends were reversed to increase in 2020. To clarify the mechanisms of recent increasing suicide in Japan, temporal fluctuations of SMRs disaggregated by sex and employment status (employed and unemployed individuals) and labor indices such as working hours, wages, and regular employment opportunity index (REO) from January 2012 to June 2023 were analyzed using interrupted time-series analysis. Additionally, temporal causalities from labor indices to SMRs were analyzed using vector autoregressive and non-linear auto-regressive distributed lag analyses. Decreasing trends among employed SMRs of both sexes were attenuated after the enactment of the "Work Style Reform Program" in 2018, but male SMRs were unaffected by the COVID-19 pandemic. However, female employed SMRs sharply increased, synchronized with the "Work Style Reform Act" and the COVID-19 pandemic outbreak (the COVID-19 impact was greater than the "Work Style Reform Act"). Additionally, unemployed SMRs of both sexes sharply increased with the revision and scale-down of countermeasures against economic deterioration caused by COVID-19 ("revision of economic supportive countermeasures against economic deterioration caused by COVID-19"). Unexpectedly, after enacting the "Work Style Reform Act", wages decreased due to possibly decreasing working hours. Increasing REO, which consistently increased, was a protective factor for male suicides, but unemployed SMRs were not affected by any labor indices. It has been established that controlling a heavy workload plays an important role in suppressing the deterioration of physical and mental conditions, including suicide; however, this study suggested that, at least within appropriate ranges of working hours, decreasing working hours due to excessive management probably contributes to increasing suicides of some vulnerable individuals via de-creasing their wages. Although governmental welfare and economic support measures had to be revised according to rapidly changing situations during the COVID-19 pandemic, this study also suggested that temporal gaps among a part of revisions of several welfare and economic support measures were unexpectedly involved in drastically/sharply increasing suicides of unemployed individuals in 2022.

Uncertainty and international fund flows: A cross-country analysis

This paper investigates the impacts of uncertainty on international portfolio allocation decisions and returns across 27 countries. Using panel structural vector autoregressive analysis, a positive link between both country-level and global uncertainty with equity fund flows is uncovered, highlighting the role of investor sentiment in capital allocation decisions. Findings also support the portfolio rebalancing theory, revealing the negative influence of past returns on fund flows. Results show that the VIX demonstrates a positive association with fund flows, particularly during periods of elevated global uncertainty. Moreover, this research analyzes differences between developed and emerging markets in their sensitivity to uncertainty shocks.

Analisis Kebijakan Moneter melalui Jalur Harga Aset di Negara Muslim Berkembang

This research aims to analyze the contribution of variables from the interaction of monetary policy transmission variables (SBI, STOCK, INVESTMENT, GDP, INFLATION). This research uses secondary data or time series, namely from the first quarter of 2011 to the first quarter of 2016. The data analysis model in this research is Vector Autoregression (VAR) model and sharpened with Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) analysis. The results of the VAR analysis show that the past variable (t-1) contributes to the current variable both the variable itself and other variables and from the estimation results it turns out that there is a reciprocal relationship between the variables where all the variables are monetary policy transmission variables (SBI, STOCK, INVESTMENT, GDP, INFLATION) contribute to each other. The results of the IRF analysis show that the stability of the response of all variables is formed in period 5 or the medium and long term, where the response of other variables to changes in one variable shows different variations from positive to negative responses or vice versa, and there are variables whose responses remain positive or remains negative from the short term to the long term. The results of the FEVD analysis show that there are variables that have the largest contribution to the variable itself in the short, medium and long term, such as GDP and SB. Meanwhile, other variables that have the greatest influence on the variable itself in the short, medium and long term are GDP which is most influenced by INVESTMENT, Inflation is most influenced by GDP, INVESTMENT is most influenced by Inflation, SB is most influenced by INVESTMENT while it is most influenced by SB. The results of the analysis of the interaction of each monetary policy transmission variable in the path of asset prices in developing Muslim countries in the short, medium and long term show that monetary policy transmission can increase economic growth and achieve the inflation targets of developing Muslim countries.