Econometric Model Research Articles

Intensity of Adoption of Improved Malt Barley Production Technologies in Ethiopia: A Case Study in Oromia Region

Improved malt barley production technology packages are not yet widely adopted in Ethiopia. Stakeholders, including brewers, malt factories, research institutes, and farmer-based organizations, have been collaborating and promoting new malt barley production technologies in order to boost the intensity of acceptance. The aim of this study was to find out the intensity of improved malt barley production technologies adoption in Arsi zones, Oromia region, Ethiopia. A stratified random sampling approach with multiple stages was employed to collect primary data from 384 malt barley household heads. The data was analyzed using descriptive statistics, and the intensity of adoption of improved malt barley production technologies was determined using an econometric Tobit regression model. The findings specified that the most adopted improved malt barley variety was Traveler (47.92%), which was followed in descending order by Iboni (14.58%), Sabini (9.38%), Grace (5.47%), Holker (4.43%), and 18.23% unnamed varieties. The Tobit model result also depicted that the intensity to which improved malt barley varieties adopted were affected by, taking into account factors including contract farming involvement, cooperative membership, off-farm income, size of livestock holdings, access to training, and mobile ownership. Enhancing farmers’ knowledge of the advantages of contract farming, income diversification, and mobile phone-based information delivery are among the many ways to support access to and accelerate the adoption of improved malt barley technology in the research area and beyond.

Re-Visiting the Co-movement between FinTech, Economic Policy Uncertainty, Governance Quality and Bank Variability in Pakistan: A Wavelet Coherence Approach

The recent literature about FinTech services in Pakistani commercial banks has grabbed much attention as these services have increased rapidly in the last few decades. This paper examines the co-movement between FinTech, economic policy uncertainty, bank- variability, and governance quality. The data was obtained from 2014-2020 on a monthly frequency. To address the relationship and co-movement, we have applied more advanced and sophisticated econometric models, autoregressive distributed lag model (ARDL), and continuous wavelet (CTW) to investigate the relationship between the variables. These approaches provide flexibility of assumptions for time-series data and provide robust results. The results indicate a short and long-run symmetric relationship with a significant co-movement between the variables.

Influence mechanism of digital economy development on the supply efficiency of ecological products

Improving the supply efficiency of ecological products (EPSE) is of great significance to protect the ecological environment, promote the development of green industry and the sustainable growth of the economy. This paper focuses on constructing an evaluation index system for EPSE and exploring the relationship between the development level of the digital economy (DE) and EPSE in China. To measure the EPSE, the study employs the Undesirable Slacks-Based Measurement (SBM) Model across 30 provinces, cities and districts in China from 2011 to 2022. Furthermore, the research utilizes spatial econometric models, panel threshold effect models, and other methodologies to investigate the impact mechanism and non-linear relationship between DE and EPSE. The research shows that: (1) The overall level of EPSE in the study area is relatively low, with significant development differences observed; (2) The DE has a nota-ble spatial spillover effect on EPSE, with a significantly negative impact in neighboring areas; (3) The development of the DE promotes EPSE through the upgrading of human capital structure, industrial structure, and increases in local government fiscal revenue and corporate operating profits; (4) Heterogeneity analysis shows that the impact of DE on EPSE varies significantly across eastern, central, and western China, with positive effects pronounced in the eastern and western regions; (5) Regression results of the threshold effect indicate a significant single threshold effect on the impact of DE development level on EPSE. Specifically, when the threshold value is less than 0.1232, DE significantly contributes to the improvement of EPSE. This paper contributes new literature evidence and factual references to the understanding of the causal relationship between DE and EPSE. The findings highlight the importance of considering spatial spillover effects, impact mechanisms, and regional heterogeneity in analyzing the relationship between DE and EPSE. The research also suggests that promoting the development of the digital economy could be a viable strategy to enhance EPSE, especially in regions where the threshold value is below 0.1232

Can Social Technologies Drive Purchases in E-Commerce Live Streaming? An Empirical Study of Broadcasters’ Cognitive and Affective Social Call-to-Actions

As an emerging social technology, live streaming has facilitated a synchronous and interactive selling setting for e-commerce sellers and consumers. Despite growing operations management (OM) in e-commerce live streaming (ELS), the prior literature has largely neglected the directive social operations of ELS broadcasters, which are crucial to a company's business operations and marketing strategies. To mitigate this gap, we investigated the effects of social call-to-actions (SCTAs), an ELS-specific directive social operation, on consumer purchase outcomes in ELS. To explicate the mechanisms driving the purchase effect of SCTAs, we draw upon the notions of cognitive and affective marketing appeals to categorize SCTAs into two types: cognitive and affective SCTAs. We measure broadcasters’ SCTAs from ELS speech-to-text data using text-mining techniques and adopt econometric model estimations via an instrumental variables identification approach to quantify the relationships between ELS broadcasters’ SCTAs and consumer purchases. Our results uncover a significant positive purchase effect of affective SCTAs but an insignificant effect of cognitive SCTAs on product-level purchase orders in ELS. Specifically, a one-unit increase in affective SCTA-related words per minute is related to increased purchase orders of a product by 0.43%, i.e., averaging a 1.10% growth in demand per minute. Additionally, we find that the effects of cognitive and affective SCTAs are contingent on product and broadcaster types. Our session-level mediation effect analysis verifies the underlying mechanisms that drive the purchase effects of broadcasters’ SCTAs. Specifically, we find that cognitive SCTAs impede social engagements and thereby purchases, whereas affective SCTAs can boost social engagements, leading to increased session traffic and ultimately product sales in ELS. Our study provides novel empirical findings and important practical implications for ELS platforms and broadcasters.

EXCHANGE RATE DYNAMICS AND THEIR IMPLICATIONS

This research paper explores the dynamics of exchange rate fluctuations and their far-reaching implications for the global economy. Exchange rates are pivotal in shaping trade balances, inflation, and capital flows, making them a critical factor for policymakers, multinational corporations, and investors. By examining both theoretical frameworks and empirical evidence, this study analyzes the effects of exchange rate movements on key economic variables such as inflation rates, gross domestic product (GDP), foreign direct investment (FDI), and international trade balances. The paper employs a combination of econometric modeling and historical data analysis to evaluate how these fluctuations have influenced emerging and developed economies over the past two decades. Results indicate that exchange rate volatility can lead to inflationary pressures in import-dependent economies, disrupt trade balances, and influence FDI inflows. The findings also underscore the importance of stable exchange rate policies to mitigate economic uncertainty. This study concludes with recommendations for future research, particularly regarding the influence of digital currencies and geopolitical tensions on exchange rate stability.

The Impact of Key Performance Indicators on Faculty Research in Higher Education

This study investigates the effectiveness of key performance indicators (KPIs) in evaluating the research contributions of academic staff in Uzbekistan, aiming to provide a nuanced understanding of faculty performance assessment. Employing a mixed-methods approach, the research integrates quantitative bibliometric analysis of publication data from 500 faculty members with qualitative interviews from 30 academics. The quantitative analysis focuses on metrics such as total publications, citation counts, and h-index scores, while the qualitative component explores faculty perceptions regarding the influence of KPIs on their research activities and career development. The analysis reveals significant variability in research productivity, with faculty averaging 12.4 publications and 110.2 citations. Econometric modeling indicates a strong positive correlation between publication metrics and citation impact, suggesting that higher publication output enhances academic visibility. Qualitative insights highlight the pressures of a "publish-or-perish" culture, which adversely affects research quality and faculty morale, particularly in contexts with limited institutional support. This research contributes to the discourse on academic performance evaluation by emphasizing the limitations of traditional metrics and advocating for a more holistic assessment framework. It highlights the need for higher education institutions to consider both quantitative and qualitative measures to better reflect the complexities of academic contributions in Uzbekistan.

Research on the social functions of trade to support the development of marketability of farm products

In recent years, there has been a problem of reducing the marketability of agricultural products of farms for many types of goods. Among the key food products supplied by farmers, the negative dynamics of sales was noted for the sale of potatoes and vegetables. This situation is largely related to the annual fluctuations in agricultural production, which is especially evident in the cultivation of fruit and vegetable crops. Using econometric modeling based on the construction of regressions based on panel data, the article shows the negative impact of changes in production volumes on the percentage of marketability of farm products. Farmers, like other small-scale enterprises, do not have a developed marketing infrastructure, unlike agricultural organizations. Therefore, farms are particularly sensitive to fluctuations in production volumes, which create for them the problem of lack or excess of resources for sales. An important way to solve this problem is the activity of trade organizations aimed at implementing social functions to support the sale of farm products. In Russia, such social practices are being implemented by the largest representatives of the retail industry in a pilot mode in certain regions of the country. In particular, in some areas, agricultural aggregators are operating, launched by the “X5 Group” and “Magnit” retail chains, which perform technological and marketing functions to ensure the sale of farm goods. There are other social projects of trade organizations, for example, “Agrocontract” and “Farmer›s Island”, initiated by “X5 Group”, which involve the conclusion of direct contracts with farmers in order to reduce the purchase prices of goods and provide for the purchase of various unique goods from farmers. The expansion of the social functions of trade organizations will significantly overcome the existing problems of marketing farm products and provide such farms with sufficient income and profit for further self-development of activities.

Industry spillover effects of robot applications on labor productivity: Evidence from China

This paper provides new insights into the industry spillover on labor productivity by discussing the spillover effects of robot application among industries in three spillover directions: horizontal, forward, and backward spillover. In addition, this paper measures the three spillovers with the weight matrices from the year 2012 input-output table by the Chinese National Bureau of Statistics. Using a spatial econometric model and 2000–2010 data on China's secondary industry, this paper tests three hypotheses relating to the direct effect and the spillover effects of robot applications. The main results show that the robot application of one industry can improve labor productivity significantly. Moreover, all three kinds of spillover have significant positive effects on the labor productivity of a focal industry. Among them, the forward spillover exerts the greatest impact on labor productivity. The heterogeneity effects exist due to the industry's factor intensity and technological structure etc. The findings of this study could provide several important implications for the development of a given industry and the industry chain of a country as well.

Corporate Social Responsibility, Earnings Management, and Cash Holding: A Study of Non-Financial Firms in Pakistan

This study investigates the interplay between CSR practices, earning management, and cash holdings in Pakistani publicly listed companies (PLCs). Spanning the period from 2013 to 2022, the research aims to provide empirical insights into how CSR initiatives influence financial practices within the Pakistani context. Methodologically, the study employs panel unit root tests and compares Ordinary Least Squares (OLS), fixed effects, and random effects models to analyze a sample of 202 non-financial firms listed on the Pakistan Stock Exchange. The implications of these findings underscore the importance of strategic CSR integration to enhance financial transparency, mitigate agency conflicts, and foster sustainable corporate growth. Managerial implications suggest that executives should focus on enhancing board effectiveness, strengthening audit oversight, aligning managerial incentives with long-term shareholder interests, and integrating CSR initiatives into core business strategies. However, the study acknowledges several limitations. These include the sector-specific focus on non-financial firms, which may limit generalizability to other sectors within the Pakistani economy. The study's reliance on panel unit root tests and econometric models, while robust, may benefit from alternative methodological approaches and sensitivity analyses. In conclusion, while this study contributes valuable insights into the CSR practices and financial management of Pakistani PLCs, future research should explore longitudinal trends, sector-specific variations, the role of emerging technologies in integration of CSR into corporate strategies. Addressing these areas would further enhance our understanding and inform policy interventions aimed at promoting transparency, accountability, and sustainable in emerging market economies like Pakistan.

Beyond Parametric Bounds: Exploring Regional Unemployment Patterns Using Semiparametric Spatial Autoregression

Abstract Background It is a well-known phenomenon that nonlinearities that are inherent in the relationship among economic variables negatively affect the commonly used estimators in the econometric models. The nonlinearities cause an instability of the estimated parameters that, in particular, are unable to capture a local relationship between the response and the covariate. Objectives The main aim of the paper is the simultaneous consideration of spatial effects as well as nonlinearities through an advanced semiparametric spatial autoregressive econometric model. The paper seeks to contribute to empirical studies of regional science focused on the application of semiparametric spatial autoregressive econometric models. Methods/Approach We outline an approach that can be used to correct nonlinearities by incorporating a semiparametric idea within the framework of econometric models. We use an expansion by penalised basis splines that are highly flexible and are able to capture local nonlinearities between variables. Results In the empirical study, we fit different econometric models that attempt to explain the dynamics of the European Union's regional unemployment. Conclusions The results show that regional unemployment exhibits significant spatial dependence, indicating interconnectedness among neighbouring regions and suggesting the adoption of a semiparametric spatial autoregressive model for improved modelling flexibility, surpassing traditional parametric approaches.

Spatial data analysis with R: wrangling, visualization and econometric models

Spatial data analysis with R: wrangling, visualization and econometric models

Assessing the impact of the European Union-Vietnam free trade agreement on labor market and employment in Vietnam

PurposeThis study investigates the impact of the European Union-Vietnam Free Trade Agreement (EVFTA) on Vietnam’s labor market and employment outcomes, considering the potential distributional consequences across various groups of workers and regions.Design/methodology/approachA comprehensive quantitative framework is employed, combining the gravity model of trade to estimate the EVFTA’s effects on trade flows, the CISAF_M1 model to simulate sectoral employment impacts and econometric models to analyze the effects on wages and decent work. The analysis draws upon a rich dataset spanning the period before and after the EVFTA’s implementation.FindingsThe EVFTA has significantly boosted Vietnam’s exports to the EU, leading to substantial job creation in labor-intensive sectors such as textiles, apparel and footwear. The agreement has also contributed to wage growth and improved access to social insurance for workers in export-oriented industries. However, the impact on other aspects of job quality is less clear. The distributional analysis reveals that the EVFTA’s employment effects have been relatively balanced across gender but have favored younger and less skilled workers as well as those in rural areas.Originality/valueThis study provides a comprehensive and nuanced assessment of the EVFTA’s impact on Vietnam’s labor market, addressing a gap in the literature on the employment effects of new-generation free trade agreements in developing countries. The findings highlight the importance of considering distributional aspects when evaluating the socioeconomic implications of trade liberalization and offer valuable insights for policymakers aiming to promote inclusive growth in the context of deepening international economic integration.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-03-2024-0236

Can diplomatic activities promote foreign direct investment? Empirical evidence from China

PurposeThis study investigates the impact of Chinese senior leaders’ diplomatic visits on outward and inward foreign direct investment (OFDI and IFDI, respectively). Additionally, it aims to discern the differential impacts of diplomatic visits on FDI across different Chinese administrations.Design/methodology/approachWe utilize data about the diplomatic visits of Chinese senior leaders to 146 host countries. The dataset was divided into two leadership eras: the tenure of President Hu Jintao and Prime Minister Wen Jiabao from 2003 to 2012 and the period from 2013 to 2021 under President Xi Jinping and Prime Minister Li Keqiang. We employ generalized least squares, lagged effect, and two-stage least squares methods to estimate the econometric model. This analytical framework assesses the influence of high-level diplomatic visits on FDI flows, addressing potential time-serial and endogeneity concerns.FindingsThe findings reveal that senior leaders’ diplomatic visits significantly boost Chinese OFDI. The effect on IFDI in China is not statistically significant. The administrations of Hu and Xi played positive roles in promoting OFDI. However, only Hu’s administration showed a statistically significant positive relationship with IFDI. The result suggests a potential increase in IFDI between China and host countries within two to three years following diplomatic visits.Originality/valueThis study contributes to the understanding of how Chinese senior leaders’ diplomatic activities affect foreign direct investment. It provides a deeper understanding of Chinese senior leaders’ diplomatic efforts to compensate for investment across two administration periods. It offers insights into the potential influence of diplomatic efforts on FDI, enriching the understanding of diplomacy’s role in international business contexts.

Can Data Empower Indigenous People? Unveiling an Innovative Dataset for Quantitative Analysis, Replication Modeling, and Economic Development

Abstract Indigenous peoples are among the most vulnerable, ignored, and marginalized groups in society. Poverty is the oldest social problem and difficult to counter. The Indigenous people with which the authors live and work, the Agta Tabangnon, suffer from poverty and multidimensional socioeconomic deprivations. Indigenous peoples’ studies are qualitative, while poverty studies are typically generic, exposed to large sampling errors, and intended for nationwide decisions. Therefore, measuring poverty for specific tribes through complete enumeration with multifaceted disaggregation is critical for economic development. There is no comprehensive census specifically designed for Indigenous peoples to encompass the multidimensional aspects of their way of life. Nonetheless, the authors are resourceful in generating useful datasets from their partners. The locale is situated in the poorest district of the poorest province in the poorest region of Luzon, Philippines. The datasets contain multidimensional poverty indicators that are readily usable, along with complementary analytics to visualize the data. They may serve to measure poverty in Indigenous communities across different regions and countries. By utilizing this data, further empirical analysis, regressions, machine learning, and econometric modeling can be conducted. It can be freely utilized to target policies that address the multifaceted poverty and promote economic development within tribal communities.

Determinants of commercialization among onion producer households in southern Ethiopia: a double hurdle approach

This study examined the determinants of onion commercialization of smallholder farmers in the Wolayita and Gomo zones of southern Ethiopia. Both primary and secondary data were used in this study. Primary data were collected from randomly selected 160 onion producers by using a structured questionnaire. Both descriptive statistics and the double-hurdle econometric model were employed in the analysis. The descriptive statistics indicated that the mean commercialization level of smallholder farmers in the study area was 79.51%, highlighting that onion producers in this region primarily focus on marketing their produce. Using the econometric model the first-stage double hurdle results revealed that family size, farming experience, frequency of extension contacts, and distance to the nearest market were factors significantly affecting the market participation decision of onion producers. The second stage of the double hurdle results indicates that education level, family size, frequency of extension contacts, land size allotted for onion production, and distance to the nearest market were factors that significantly affected the level of onion commercialization. Therefore, policies aimed at increasing farmers' access to better road networks and transportation facilities, improving access to education, promoting agricultural training programs, and improving extension services are recommended to improve the commercialization of onion production.

Level of social security expenditures and economic growth rate based on econometric regression modelling: New evidence from OECD countries

This research article examines the relationship between the level of social welfare expenditure and economic growth rates, based on unbalanced panel data from 38 OECD countries covering the period from 1985 to 2022. Four hypotheses are formulated regarding the impact of social expenditure on economic growth rates. Through multiple iterations of regression model building, employing various combinations of dependent and independent variables, and conducting tests for stationarity and causality, compelling empirical evidence was obtained on the negative influence of social welfare spending on economic growth rates. The study takes into account both government and non-governmental expenditures on social welfare, a novelty in this field. This approach allows for a detailed examination of the effects of different components on economic growth and provides a more comprehensive understanding of the relationships. The findings indicate that countries with high levels of social welfare spending experience a slowdown in economic growth rates. This is associated with increasing demands on social security systems, their growing inclusivity, and the escalating required levels of financing, which are increasingly covered by debt sources. The research highlights the need to strike a balance between social expenditures and economic growth rates and proposes a set of measures to ensure economic growth outpaces the indexing of social expenditures. The abstract underscores the relevance of the study in light of the widespread recognition of the necessity to combat inequality, poverty, and destitution, and calls on OECD countries’ governments to pay increased attention to social policy in order to achieve sustainable and balanced economic growth.

Investigating the dynamic nexus between fiscal deficit and inflation: empirical evidence from BRICS economies

PurposeThe debate surrounding whether fiscal actions are responsible for inflation has been a central focus in macro-public finance. In this regard, the present study makes a novel attempt to assess the nexus between fiscal deficit and inflation in the presence of output growth, trade openness and money supply using a balanced panel dataset from Brazil, Russia, India, China, and South Africa (BRICS) economies.Design/methodology/approachIn conducting the empirical analysis, the study initially addresses the issues of cross-section dependency, heterogeneous slope coefficients and nonstationarity. The assessment of long-run cointegration was carried out by using novel third-generation cointegration tests introduced by Westerlund and Edgerton (2008) and Banerjee and Carrion-i-Silvestre (2017). The authors utilise the advanced and latest estimation technique, the cross-section augmented autoregressive-distributed lag (CS-ARDL) model, for long-run and short-run analyses. Finally, to investigate the causal relationship between fiscal deficit and inflation within a panel framework, this study employs the novel test (JKS) introduced by Juodis et al. (2021).FindingsUsing recent econometric tests, the study validates the existence of cross-sectional dependency and slope heterogeneity. The results of the third-generation cointegration test by Westerlund and Edgerton (2008) and Banerjee and Carrion-i-Silvestre (2017) show that the variables are cointegrated in the long run. The CS-ARDL model revealed a positive relationship between inflation and fiscal deficit. The implication of this finding suggests that the fiscal deficit plays an inflationary role in BRICS economies. Finally, the JKS causality test results found a bi-directional causal association between fiscal deficit and inflation.Practical implicationsThe findings suggest several significant policy recommendations. It is recommended that a well-executed fiscal consolidation strategy be adopted to attain sound fiscal health and lower inflation. A disciplined fiscal approach is not only vital for effective monetary policy but also essential for maintaining macroeconomic stability. Monetary authorities must establish credible practices to effectively manage the macroeconomic system, and policy stances should align with the specific needs of the economy. The bidirectional causality between fiscal deficit and inflation suggests that relying solely on fiscal measures is inadequate for managing inflationary pressures or stabilising fiscal balances. Thus, a more comprehensive approach is required, including measures such as sustaining economic growth, reducing import dependency, diversifying exports and ensuring exchange rate stability.Originality/valueNumerous empirical studies have explored the link between fiscal deficit and inflation. However, most research studies within BRICS economies have focused on individual countries rather than considering the group as a whole. This limited scope may fail to capture the unique characteristics and interactions within these economies. To the best of the author’s knowledge, the present study is the first attempt to examine the issue from a panel perspective across BRICS economies. The methodological novelty of the present study is that it represents the first attempt, at least within emerging market economies, to investigate the nexus between fiscal deficit and inflation using second- and third-generation econometric models. From a policy perspective, the authors highlight that BRICS economies must prioritise fiscal discipline through measures such as reducing unproductive expenditure and improving tax collection. Close coordination between fiscal and monetary authorities is essential to ensuring that monetary policy supports fiscal consolidation efforts while maintaining price stability.

A machine learning algorithm to explore the drivers of carbon emissions in Chinese cities

As the world’s largest energy consumer and carbon emitter, the task of carbon emission reduction is imminent. In order to realize the dual-carbon goal at an early date, it is necessary to study the key factors affecting China’s carbon emissions and their non-linear relationships. This paper compares the performance of six machine learning algorithms to that of traditional econometric models in predicting carbon emissions in China from 2011 to 2020 using panel data from 254 cities in China. Specifically, it analyzes the comparative importance of domestic economic, external economic, and policy uncertainty factors as well as the nonparametric relationship between these factors and carbon emissions based on the Extra-trees model. Results show that energy consumption (ENC) remains the root cause of increased carbon emissions among domestic economic factors, although government intervention (GOV) and digital finance (DIG) can significantly reduce it. Next, among the external economic and policy uncertainty factors, foreign direct investment (FDI) and economic policy uncertainty (EPU) are important factors influencing carbon emissions, and the partial dependence plots (PDPs) confirm the pollution haven hypothesis and also reveal the role of EPU in reducing carbon emissions. The heterogeneity of factors affecting carbon emissions is also analyzed under different city sizes, and it is found that ENC is a common driving factor in cities of different sizes, but there are some differences. Finally, appropriate policy recommendations are proposed by us to help China move rapidly towards a green and sustainable development path.

Do News Reports and Google Searches Impact Bitcoin Prices? An Analysis of Granger Causality

Objective: to analyze the Granger causality relationship between the number of Bitcoin news articles, the number of Google searches related to the term "Bitcoin," and the price of the cryptocurrency. The study aims to understand how these variables influence each other using an econometric time series model. Theoretical framework: the paper is based on studies on financial market efficiency, cryptocurrencies, and Granger causality theory. It reviews research that explores the impact of media information and online searches on the price of financial assets, such as Bitcoin, while also discussing Bitcoin’s role as both a speculative asset and a transactional currency. Method: the construction of a time series with weekly data, from 2015 to 2018, covering Bitcoin price, the number of news articles, and Google searches. The Vector Autoregressive (VAR) model was used to conduct the Granger causality test and verify if there are causal relationships between these variables. Results and Discussion: the findings indicate a triple causality relationship among the studied variables. Bitcoin's price influences both the number of news articles and Google searches, and in turn, the articles and searches also influence Bitcoin's price. This suggests that increased interest and media coverage of Bitcoin drive its demand and price. Research implications: the research suggests that Bitcoin's price behavior is influenced not only by traditional economic factors but also by media information and the volume of online searches. This highlights the importance of informational factors in the cryptocurrency market and the influence of digital media on asset pricing. Originality/value: the study proposes integrating data from news articles and Google searches with Bitcoin price behavior, applying Granger causality analysis. Its contribution lies in demonstrating the importance of media in the dissemination and pricing of cryptocurrencies, providing a new perspective for the literature on digital assets and speculative behavior.

Development and evolution of mathematical and econometric models for the analysis of macroeconomic processes (including historical perspective) and their forecasting

Practical usefulness of factor-vector autoregression has been considered, which makes it possible to study macroeconomic processes impact on the monetary policy state, to conduct full-scale monitoring and to identify economic growth impact on monetary policy. Publications devoted to the issues of unstable economic development and mathematical models describing the process of economic dynamics under conditions of high inflation and unstable development have been analyzed. The basic scenario in economy has been given. Monetary policy impact on macroeconomic variables using vector autoregressive (VAR) models has been studied. The FAVAR model with international reserves as the monetary policy variable and MIACR where the responses of one-year government bond rate and consumer index were positive have been estimated as the main instruments. The impulse responses shown indicate that the Russian authorities had a lack of monetary policy independence due to the country’s high dependence on the oil and natural gas sectors, which does not reveal evidence of monetary policy impact on lower consumer prices.