Search

I examine the response of Saudi Arabia's military spending to a positive shock in Iran's economic globalization index. Using vector autoregression (VAR)-based impulse response functions with data from 1990 to 2020 and controlling for other key economic and demographic indicators, I find a negative and statistically significant response of Saudi Arabia's military spending to a positive shock in Iran's economic integration in the global markets. I also observe a similar decreasing response of Saudi Arabia's military spending following a positive shock in Iran's political globalization index. However, I do not find such negative and significant response for positive shocks in other components of Iran's globalization, such as in the social and cultural aspects. Finally, I do not observe a significant positive response from either country's military spending to a positive shock in the other's military spending, suggesting that the military budgeting of either country is not strictly interconnected. In early 2023, both countries reached an agreement to reactivate their political relationship, promising a decrease in regional tension and a favorable outlook for the potential easing of economic sanctions on Iran. This may result in a significant peace dividend, especially for Saudi Arabia. [ABSTRACT FROM AUTHOR]

Summary: Expansionary monetary policy combined with unconventional measures led to a decline in the profitability of U.S. and European banks. This paper studies whether such measures also affect the asset concentration in the European banking sector. The findings of this research add value to previous research, taking a step deeper into examining the consequences of expansionary monetary policy. It is found that reductions in the European central bank’s (ECB’s) key policy rate can predominantly explain the concentration growth in the eurozone countries. Furthermore, the ECB’s monetary policy had a more substantial influence on the growth of the concentration of banks outside the eurozone than those countries’ own monetary policies. Thus, the expansionary monetary policy poses specific challenges to financial stability in Europe. [ABSTRACT FROM AUTHOR]

This study provides empirical evidence on the relative impact of innovations in information content and noise embedded in economic news and social media sentiments on DJIA, S&P 500, NASDAQ, and Russell 2000 index returns. We find that economic news sentiments are relatively more rational and have a greater impact than irrational social media sentiments. There exist significant negative effects of three distinct categories of social media sentiments and a significant positive impact of economic news sentiments on stock returns. The magnitude of the impact of the economic news sentiments is larger. In addition, the economic news sentiments seem to have greater information content and are driven by risk factors to a greater extent than the sentiments of social media, which probably contain more noise. There are significant negative responses of stock returns to irrational components of social media sentiments while significant positive responses to rational components of economic news sentiments. Lastly, the magnitude of the impact of rational economic news sentiments is higher than that of irrational social media sentiments. Our results are consistent with the view that business news is a manifestation of a rational outlook to a larger extent than social media and can drive stock valuations. [ABSTRACT FROM AUTHOR]

This paper aimed to explore the impact of the dynamic relationship between broiler price and pig price on the healthy and stable development of the livestock and poultry breeding industry. Selecting monthly data on broiler price and pig price from January 2020 to August 2023, this study empirically explores the dynamic correlation between broiler price and pig price using VAR models, impulse response functions, and Granger causality. The results showed that an increase in the price change rate of broiler can effectively increase the price change rate of pig, and an increase in the price change rate of pig can also effectively increase the price change rate of broiler. There is a bidirectional causal relationship between the price of broiler and the price of pig. Compared to the price of pig, the price change rate of broiler is mainly influenced by their own prices. The price change rate of pig is mainly influenced by both the price change rate of broiler and their own price change rate, and the degree of influence of the price change rate of broiler is higher than that of their own. The price change rate of broiler plays a dominant role in the fluctuations of broiler price and pig price. The research shows that there is a dynamic correlation between the price of broiler and the price of pig, which can effectively improve the information judgment ability of price changes, in order to help the livestock breeding industry to improve quality and efficiency. [ABSTRACT FROM AUTHOR]

Bayesian Vector Autoregressive models have become the natural response to the dense parametrization often required by multivariate time series modeling. However, the Bayesian approach is somehow new to the gretl ecosystem: classical analysis of Vector Autoregressions (VARs) is natively supported and supplemented via addons and function packages, but a Bayesian counterpart in the form of equally general and advanced functions or, at a lower level, in the form of didactic example scripts is missing. This paper pursues the second route describing, via a replication exercise, how to perform basic Bayesian inference in gretl using VARs, with particular reference to structural analysis. The contribution goes in the direction of providing new hints and tools, available to the gretl user, for a more complete and up-to-date understanding of modern macroeconometrics. [ABSTRACT FROM AUTHOR]

In 2005, the Fifth Plenary Session of the 16th Central Committee of the Communist Party of China introduced a strategic plan to advance the construction of a new socialist countryside, thereby providing a policy foundation for the robust development of rural tourism. Against this policy backdrop, the present study investigates the impact of rural tourism policies on the spatial evolution of ethnic minority villages in Yanbian Prefecture, utilizing data from the period 2004–2023. As a representative region in China where ethnic minorities coexist, Yanbian Prefecture exhibits distinctive cultural and spatial features in its Korean villages, making it a key pilot area for rural tourism development. This study utilizes the PMC index model, the coupled coordination degree model, and the vector autoregressive model to analyze the implementation effects of rural tourism policies and to establish an index system for rural spatial construction. By examining the spatial evolution of representative ethnic minority villages in Yanbian Prefecture, the research explores the dynamic interactions between tourism policies and rural construction, as well as the underlying causal mechanisms. The findings indicate that: (1) in ethnic minority villages, geographic characteristics and various constraints contribute to delayed initial policy effects, with negative fluctuations observed, highlighting a distinct lag effect in the policy implementation process; and (2) a significant Granger causality exists between tourism policies and rural spatial construction, with varying effects observed across different dimensions. The study centers on ethnic minority settlements, systematically analyzing the dynamic effects of tourism policies in the context of their spatial evolution characteristics. It offers sustainable development policy recommendations tailored to the unique attributes of ethnic minority villages. lt is suggested that the actual needs of village construction and long-term development goals should be fully considered when formulating and implementing policies to promote the sustainable development of ethnic minority areas. [ABSTRACT FROM AUTHOR]

Meteorological parameters are increasingly influenced by unsustainable development and environmental pollution, contributing to the rising frequency of natural calamities. Accurate prediction of these parameters is crucial for prevention. This study uses historical data to predict six key meteorological parameters, such as dew point, cloud cover, precipitation, temperature, wind speed, and wind direction of Bengaluru and Delhi city of India. Using cloud-based services ensures scalability and computational efficiency for data storage, processing, and model deployment in weather parameters. To address data gaps, generative adversarial networks (GANs) are employed for data imputation within those parameters. Hence, the present work predicts six important meteorological parameters using the vector autoregression model (VAR), vector moving average model (VMA), vector autoregression moving average model (VARMA), and cointegrated vector autoregression model (CVAR). The comparative analysis demonstrates the superior performance of the CVAR model over other models, as measured by the normalized mean square error (nMSE) and normalized root mean square error (nRMSE) for a single parameter (2.12, 0.3) and (1.21, 0.24) for both the cities, in forecasting all the specified meteorological parameters.

AbstractThe behavioral finance perspective posits that fluctuations in security prices are significantly influenced by investors' emotional responses, which can, in turn, affect stock returns. Understanding the sources of stock price changes is critical within asset pricing theory, highlighting the necessity of exploring the effects of investor sentiment on stock returns. This study aimed to investigate how investor sentiment shocks impact both normal and abnormal returns in the oil products sector of the Tehran Stock Exchange. To measure abnormal returns, we employed the six-factor model developed by Fama and French (2018). Investor sentiment was evaluated using the Relative Strength Index (RSI), the Psychological Line Index (PLI), trading volume, and the Adjusted Turnover Rate (ATR). Data were collected from 12 companies in the oil products industry over a period of 1,584 months, spanning from 2010 to 2020. The findings revealed that the influence of sentiment shocks on normal returns was more pronounced than on abnormal returns. Conversely, the impact of normal return shocks on sentiment was initially positive but became negative over time. Additionally, positive shocks to abnormal returns adversely affected investor sentiment, with the peak effect observed after five periods. This research enhances the understanding of how investor sentiment shocks influence normal and abnormal returns in the oil products sector, offering valuable insights into behavioral differences among investors.

Ensuring food security and promoting sustainable agriculture are critical global priorities, particularly in Algeria, a country endowed with substantial agricultural potential yet challenged by resource inefficiencies, climate variability, and a heavy reliance on food imports. This paper investigates the role of sustainable agricultural practices in advancing Algeria's progress toward Sustainable Development Goal 2 (SDG 2: Zero Hunger), focusing on key factors influencing agricultural productivity and food security. Using a Vector Autoregressive (VAR) model, the study examines dynamic relationships between government agricultural expenditures, food production indices, and non-oil GDP growth over the period from 2010 to 2022. Algeria’s agricultural sector faces substantial structural and external barriers, such as insufficient water resource management, limited rural development initiatives, and the vulnerabilities imposed by climate change, which exacerbate the country’s dependence on food imports to meet growing domestic demand. The findings reveal that increased government spending on agriculture has a statistically significant and positive impact on agricultural productivity and food security in the short term. However, the long-term benefits of such expenditures diminish without consistent investment and the implementation of adaptive measures. Furthermore, non-oil GDP growth has a varying impact on food security, underscoring the need for economic diversification to enhance resilience within the agricultural sector. The study also highlights the importance of targeted investments in agricultural infrastructure, sustainable water resource management, and integrated rural development to achieve lasting food security and sustainability. By addressing these issues, Algeria can unlock its agricultural potential, reduce dependency on imports, and ensure a more sustainable and self-reliant future. The study provides actionable insights for policymakers, development organizations, and stakeholders aiming to improve food security and promote sustainable agricultural practices, offering a comprehensive roadmap for achieving long-term agricultural and economic sustainability.

Times of crises underscores the importance of guarding against deteriorations in the quality of loan portfolio through effective credit risk management. The purpose of the study is to examine the credit risk resilience of Namibia’s banking sector and forecast the quality of its loan portfolio. Methodologically, the study is hinged on the theories related to information asymmetry, moral hazard, and adverse selection. The methods include a VAR and an ARIMA out of sample dynamic forecasting model. The study employs secondary time-series data for the period 1996Q1–2021Q4 from various sources including the Bank of Namibia, the Namibia Statistics Agency, the World Bank and some others. The stress-testing results analysed via the VAR’s impulse responses show that Namibia’s banking sector is highly susceptible to various shocks with the early warnings emanating primarily from the non-performing loan itself, followed by the monetary, institutional, bank-specific, and interest rate indicators. The forecast for 2023Q4–2025Q4 obtained from the ARIMA model reveals that the riskiness of its loan portfolio is predicted to persist beyond the benchmark of 4 % set by the Bank of Namibia. The findings highlight important policy interventions, including the need to strengthen the mechanisms for monitoring the share of non-performing loans, re-evaluate existing policies, continue to ensure a sound macroeconomic and financial environment, and require banks to maintain a minimum capital adequacy ratio.

Copyright of International Journal of Professional Business Review (JPBReview) is the property of Open Access Publications LLC and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the intensification of global climate change issues and increasingly stringent carbon emission controls, carbon emissions trading has become a crucial market-based mechanism for reducing carbon emissions. A deeper understanding of the factors influencing carbon emission rights prices and their formation mechanisms can enhance market transparency and effectiveness and promote the healthy development of the carbon trading market. This paper identifies the main factors affecting carbon emission rights prices and explores their formation mechanisms from both the internal and external perspectives of enterprises. Using a vector autoregression (VAR) model, it quantifies the impacts of energy prices, the climate, and technology levels on carbon trading prices in Shanghai, China. The results show that, in the short term, enterprise technology levels and energy prices significantly impact carbon emission rights prices, while the climate becomes more critical in the medium to long term. These findings propose specific policy recommendations for improving the carbon emissions trading market, with a focus on these three factors. [ABSTRACT FROM AUTHOR]

Copyright of European Journal of Applied Economics is the property of Singidunum University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to promote the green, low-carbon and high-quality development of agriculture, this paper adopted the super-efficiency SBM model and the entropy method to measure the agricultural carbon emission efficiency and the high-quality development level of agriculture in the Yellow River Basin, and discussed the interaction between the two by using VAR model. The results show that a) the agricultural carbon emission efficiency in the Yellow River Basin is on the rise as a whole. The agricultural carbon emission efficiency of Gansu and Shanxi, Sichuan, Shandong, Henan, Shaanxi and Qinghai, Ningxia and Inner Mongolia is at a lower, medium and higher level respectively. b) The overall level of high-quality agricultural development shows a fluctuating upward trend. Shaanxi, Shanxi, Ningxia and Inner Mongolia, Qinghai and Gansu, Henan, Sichuan and Shandong are at a lower, medium and higher level respectively. c) The improvement of agricultural carbon emission efficiency has a significant effect on the promotion of high-quality agricultural development, while the improvement of high-quality agricultural development has no significant effect on agricultural carbon emission efficiency. Based on the research results, some suggestions were put forward to improve the efficiency of agricultural carbon emissions and promote the high-quality development of agriculture in the Yellow River Basin from the aspects of green and sustainable development. [ABSTRACT FROM AUTHOR]

In this research, meteorological data from eleven stations were monitored to build a suitable model for predicting dew point values. Given the importance of dew point temperature in forecasting frosts, rainfall, and other meteorological applications, accurate prediction of this parameter is crucial. The stations included in the study are Bam, Birjand, Chabahar, Iranshahr, Kerman, Mashhad, Sabzevar, Tabas, Torbat Heydarieh, Zabul, and Zahedan, all located in dry climates. Initially, the correlation between various weather parameters and dew point was analyzed. Based on the highest correlation, three parameters--average, maximum, and minimum temperature--were selected as input variables for the model. CARMA and VAR models were used for analysis, and the stability of the residuals from both models was calculated. The series were then developed using the GARCH model. As a result, dew point modeling for the eleven meteorological stations was achieved with the CARMA-GARCH and VAR-GARCH models. Our findings show that the VAR-GARCH model outperformed the CARMA-GARCH model in both training and testing phases, making it the best model for this research. One key factor in the VAR-GARCH model's superior performance is its enhanced memory for processing time series data. The definitive result indicates that developing the residuals using the GARCH model improves the accuracy of both primary models by 6% to 30% in the testing phase. [ABSTRACT FROM AUTHOR]

Public debt is determined by borrowings undertaken by a government to finance its short- or long-term financial needs and to ensure that macroeconomic objectives are met within budgetary constraints. In Romania, public debt has been on an upward trajectory, a trend that has been further exacerbated in recent years by the COVID-19 pandemic. Additionally, a significant non-economic event influencing Romania's public debt is the war in Ukraine. To analyze this, a stochastic debt sustainability analysis was conducted, incorporating the unique characteristics of Romania's emerging market into the research methodology. The projections focused on achieving satisfactory results by following two lines of research. The first direction involved developing four scenarios to assess the risks presented by macroeconomic shocks. Particular emphasis was placed on an unusual negative shock, specifically the war in Ukraine, with forecasts indicating that the debt-to-GDP ratio could reach 102% by 2026. However, if policymakers implement discretionary measures, this level could be contained below 88%. The second direction of research aimed to establish the maximum safe limit of public debt for Romania, which was determined to be 70%. This threshold would allow the emerging economy to manage a reasonable level of risk without requiring excessive fiscal efforts to maintain long-term stability. [ABSTRACT FROM AUTHOR]

The Belt and Road Initiative (BRI) is a key component of China’s future international trading network. Through the Suez Canal (SC), the Maritime Silk Road Initiative (MSRI) links China to other parts of Asia, Africa, and Europe, making it a major route for global seaborne trade. This is because of the SC’s unique location on the MSRI. This study uses annual data from 1990 to 2022 to examine this dynamic relationship. We use the Vector Auto-Regressive (VAR) model and Impulse Response Function to account for the interaction effect of the variables. Model results show that China’s seaborne trade will increase SC trade by 23%, and China’s BRI investment projects are anticipated to have a significant impact on SC cargo trade in the medium and long run. Developing more value-added services in adjacent regions and attracting large Chinese investments might make the SC a worldwide logistics and a green energy hub.

The primary objective of this study is to examine the relationships between energy consumption in agriculture from renewable and non-renewable sources and the production levels in the agricultural sector across European Union countries. Additionally, this study aims to identify countries that differ in the development of their agricultural sector in terms of environmental care and to highlight the causes and consequences of these identified disparities. The classification of countries was conducted using the Principal Component Analysis method and a biplot. Panel data for the period 2000–2022, a VAR model, the impulse response function (IRF), and causality tests were used for this study. The results indicate two distinct groups of countries that significantly differ in adopting green agricultural practices. Only seven EU countries stand out for sustainable agriculture with low pesticide use, a significant share of organic farms, and high use of renewable energy in agriculture. Energy consumption affects agricultural production differently in the two groups of countries studied: in countries with sustainable agriculture, an increase in renewable energy consumption translates into a positive increase in agricultural production. On the other hand, an increase in non-renewable energy consumption shows a dampening effect on agricultural production growth, especially in countries with less sustainable agriculture. The results of this study highlight the need to promote renewable energy development in agriculture and raise awareness about the adverse environmental effects of intensive agriculture while emphasising the positive impact of organic agriculture on agricultural production. [ABSTRACT FROM AUTHOR]

Copyright of Integrated Intelligent Energy is the property of Editorial Department of Integrated Intelligent Energy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the era of digital economy, the livestock industry, as a key driving force for the development of modern agriculture and the promotion of agricultural economic growth, plays an important role in pig prices in China. Based on pig price data from February 2011 to January 2023, spanning a total of 144 months, this paper employs the VAR model to empirically test the impact of changes in the livestock industry structure on pig prices. The results show that changes in the livestock industry structure are a Granger cause of pig price fluctuations. The pig prices are affected by the lagged impact of changes in the livestock industry structure. The pig prices were negatively impacted by the changes in the livestock industry structure in the first two periods, and and began to be positively affected by the changes in the livestock industry structure from the third period. The response reached its peak in the 12th period, and the impact tended to disappear in the 24th period. The shock response cycle of changes in the livestock industry structure to pig prices is approximately two years, with a contribution rate of 10.85% still present by the end of the second year. The study indicates that changes in the livestock industry structure have a positive impact on the pig prices. [ABSTRACT FROM AUTHOR]

Knowledge of the causality between Energy Consumption and GDP is important because it leads to the future actions of policymakers, such as developing infrastructure in case GDP growth depends on Energy Consumption. Hence, the Granger Causality between the Gross Domestic Product and Energy Consumption of Ecuador is analysed in this research. For this purpose, a VAR model was developed with data from 1965 to 2022. Before including the time series inside the VAR model, ARIMA models were evaluated so that the need for differentiation and the use of dummy variables was detected. To ensure that the models include all possible information from the available data, the residuals were diagnosed until they did not have any autocorrelation between each other, there was no evidence of heteroskedasticity, and the residuals had a normal distribution. The Akaike Information Criteria and the Schwarz criteria indexes were compared to detect causality. The Granger p-value was also used to detect the probability of having null coefficients in the added time series. In the end, it was shown that Energy Consumption Granger causes Gross Domestic Product growth, but the same does not happen in the reverse direction. As a consequence, the government could support the development of energy infrastructure to incentivise economic growth. [ABSTRACT FROM AUTHOR]

This study presents a new approach for predicting water levels of the Odra/Oder river using vector autoregressive models (VAR). We use water level time series from 27 gauging stations, on which we interpolate no-data gaps using the LinAR method and detect outliers with two separate methods: the extreme values (EV) approach and the isolation forest (IFO) algorithm. Before removing potential outliers, we propose a hydrological evaluation based on multivariate data analysis. Finally, we consider three separate data scenarios, i.e. LinAR (no outlier rejection), EV, and IFO. VAR models for six prediction gauges were built in a moving window manner on the most recent 720 hourly water levels prior to each prediction. The analysis covered the time range from January 2016 to May 2022 and resulted in ≈ 1,000,000 water level forecasts (3 scenarios x 6 gauges x 55,000 hourly time steps) with lead time of 72 h. The analysis of root mean squared error (RMSE) indicates that the VAR model performs well, especially for 24-hour predictions, with RMSE values ranging from 8 to 28 cm. The model was also found to have skills in predicting a rising limb of a hydrograph. Our numerical experiments showed the susceptibility of the VAR predictions to artefacts. The IFO method was found to detect outliers skilfully, which allowed to produce the most accurate VAR-based predictions. [ABSTRACT FROM AUTHOR]

Based on the perspective of the industrial chain, this article examines the fluctuation of beef cattle prices and its causes, aiming to stabilize the market price of beef cattle and promote the high-quality development of the livestock industry. Based on the monthly data from January 2012 to January 2022, a VAR model is used to analyze the fluctuation of beef cattle prices and its causes. The results show that the price of beef cattle itself is the biggest factor causing its price changes. The impact of the calf price and feed price in the upstream of the industrial chain on the price of beef cattle is time-varying and cyclical, the impact of the beef price in the downstream of the industrial chain on the price of beef cattle is consistent and relatively stable, and the impact of external factors on the price of beef cattle is gradually increasing. The study suggests that it is necessary to adhere to the 'government + market' regulatory model, ensure the price stability of the upstream and downstream of the beef cattle industry chain, take multiple measures to deal with external market shocks, and thus promote the stable operation of the beef cattle industry, making a positive contribution to the construction of animal husbandry. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Economic Cooperation & Development is the property of Statistical Economic & Social Research & Training Centre for Islamic Countries and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research explores the connection between market-wide investor sentiment and stock market return within the framework of the Indian stock market. The study investigates how investor sentiment affects stock market returns, specifically examining the Nifty50 indices in the Indian stock market between April 2013 and March 2023. By employing PCA, we developed a sentiment index (SENT) for investors using five proxy market variables for sentiment. We used the VAR model, VAR Granger causality tests, impulse response, and Variance decomposition analysis to investigate how the sentiment index relates to market returns. The findings indicate that the Sentiment Index Granger-causes stock returns, highlighting its predictive value. Short-term shocks to the Sentiment Index significantly impact stock returns, while the effect of stock returns on sentiment is minimal and only temporary. Furthermore, the results show that stock returns are primarily influenced by their past values, but over time, the Sentiment Index increasingly influences variations in stock returns. This supports a moderate positive relationship between investor sentiment and market behavior. The findings contribute to a deeper understanding of behavioral finance in emerging markets and offer valuable insights for investors, policymakers, and finance professionals. [ABSTRACT FROM AUTHOR]

This paper aims to examine the effect of the budget balance on national financial security in the linkage of macroeconomic variables by using the VAR model approach from 1995 to 2020 in Vietnam. Moreover, the methods of Impulse Response Function (IRF) and variance decomposition (FEDV) analysis show the impact of budget balance on the financial security situation in the short run. The results indicate that budget balance has a positive effect in the short term and explains about 2.04% of Vietnam's financial security index. Several policy implications are suggested to enhance the financial stability in Vietnam during the Covid-19 pandemic. [ABSTRACT FROM AUTHOR]

Purpose -- The research identifies the impact of the Russia-Ukraine war on the stock prices of six publicly traded European airlines and evaluates their ability to adapt to this situation. Research methodology -- The dependence of the development of airline stock prices on the level of public and institutional stress due to the war was measured using GoogleTrends and is analysed on the basis of a Vector Autoregression model (VAR). Findings -- A short-term negative relationship was confirmed between the development of stock prices and GoogleTrends; the impact of the stress related to the war was negligible about 5 months after the outbreak thereof. Those companies that were the fastest to adapt to the shock of the war in terms of share prices are identified. Research limitations -- The link between GoogleTrends, as an input variable reflecting market sentiment, and the stock prices of European airlines, is considered a limitation. Practical implications -- The impact of investor sentiment on the returns on the stocks of airlines is a thing of the past; which is an important finding for financial market participants and airlines alike. Originality/value -- The ability of the specific airlines to adapt to the shock of war creates a competitive advantage. [ABSTRACT FROM AUTHOR]

The study examines the relationship between monetary policy and the stock market cycle in Vietnam. Using data from 2007 to 2022 and employing a VAR model, we estimate the impact of monetary policy factors on stock market cycle variables. The findings confirm a two-way causal relationship between monetary policy and the stock market cycle. However, the influence of cyclical fluctuations on the monetary policy transmission mechanism is weak, exhibits a lag, and predominantly occurs in the medium and long term. These results indicate that Vietnam's monetary policy requires time to absorb financial shocks and adjust to economic conditions. The study enhances the understanding of monetary policies in stock market regulation and provides evidence of its bidirectional nature concerning the market cycle. [ABSTRACT FROM AUTHOR]

This study aims to reveal the effects of exchange rate shocks and exchanges on Turkiye’s agricultural export value. In this research, using the time series vector autoregressive model (VAR), the aim of the research was achieved. Monthly data between 2000:1 and 2022:12 were used for exchange rates and agricultural exports. The data for agricultural export value are taken from the Turkish Statistical Institute (TUK), and the Exchange Rate data are taken from the Central Bank of the Republic of Turkiye (TCMB) database. The research findings were interpreted using stability, impact response analysis, variance decomposition, and causality analysis. The findings of this study show that shocks in the exchange rate in the first month do not affect and/or explain the value of agricultural exports. In the following months, it was found that dollar-dry shocks accounted for an error variance of agricultural exports between 0.01% and 1.76. The results show a significant interaction between the exchange rate and the value of agricultural exports. These results support those of the Granger Causality Test. While the shocks in the exchange rate positively affect the value of agricultural exports in Turkey in the first months, the effect of the exchange rate on agricultural exports is balanced within a year. This can be considered to have a meaningful short-term effect. [ABSTRACT FROM AUTHOR]