Vector Autoregression Research Articles

Association Between X/Twitter and Prescribing Behavior During the COVID-19 Pandemic: Retrospective Ecological Study

Background Social media has become a vital tool for health care providers to quickly share information. However, its lack of content curation and expertise poses risks of misinformation and premature dissemination of unvalidated data, potentially leading to widespread harmful effects due to the rapid and large-scale spread of incorrect information. Objective We aim to determine whether social media had an undue association with the prescribing behavior of hydroxychloroquine, using the COVID-19 pandemic as the setting. Methods In this retrospective study, we gathered the use of hydroxychloroquine in 48 hospitals in the United States between January and December 2020. Social media data from X/Twitter was collected using Brandwatch, a commercial aggregator with access to X/Twitter’s data, and focused on mentions of “hydroxychloroquine” and “Plaquenil.” Tweets were categorized by sentiment (positive, negative, or neutral) using Brandwatch’s sentiment analysis tool, with results classified by date. Hydroxychloroquine prescription data from the National COVID Cohort Collaborative for 2020 was used. Granger causality and linear regression models were used to examine relationships between X/Twitter mentions and prescription trends, using optimum time lags determined via vector auto-regression. Results A total of 581,748 patients with confirmed COVID-19 were identified. The median daily number of positive COVID-19 cases was 1318.5 (IQR 1005.75-1940.3). Before the first confirmed COVID-19 case, hydroxychloroquine was prescribed at a median rate of 559 (IQR 339.25-728.25) new prescriptions per day. A day-of-the-week effect was noted in both prescriptions and case counts. During the pandemic in 2020, hydroxychloroquine prescriptions increased significantly, with a median of 685.5 (IQR 459.75-897.25) per day, representing a 22.6% rise from baseline. The peak occurred on April 2, 2020, with 3411 prescriptions, a 397.6% increase. Hydroxychloroquine mentions on X/Twitter peaked at 254,770 per day on April 5, 2020, compared to a baseline of 9124 mentions per day before January 21, 2020. During this study’s period, 3,823,595 total tweets were recorded, with 10.09% (n=386,115) positive, 37.87% (n=1,448,030) negative, and 52.03% (n=1,989,450) neutral sentiments. A 1-day lag was identified as the optimal time for causal association between tweets and hydroxychloroquine prescriptions. Univariate analysis showed significant associations across all sentiment types, with the largest impact from positive tweets. Multivariate analysis revealed only neutral and negative tweets significantly affected next-day prescription rates. Conclusions During the first year of the COVID-19 pandemic, there was a significant association between X/Twitter mentions and the number of prescriptions of hydroxychloroquine. This study showed that X/Twitter has an association with the prescribing behavior of hydroxychloroquine. Clinicians need to be vigilant about their potential unconscious exposure to social media as a source of medical knowledge, and health systems and organizations need to be more diligent in identifying expertise, source, and quality of evidence when shared on social media platforms.

How climate volatility influences human rights

We explore how climate volatility influences human rights practices. We argue that climate volatility intensifies competition for material resources, increases citizens’ grievances against the state, and leads to more frequent repression as governments seek to respond to environmentally induced protests and local conflicts. We situate this argument in the existing literature’s emphasis on three mechanisms linking climate shocks and repression: resource shocks, mobilization shocks, and migration shocks. We assert that the climate volatility-human rights relationship is strongest in the context of civil conflict as rebel groups and governments place a higher priority on survival and material well-being rather than governance and public goods provision. Using a pooled time series dataset of 194 countries from 1946 to 2017, we estimate a panel vector autoregression (PVAR) model. Our analyses show that for countries experiencing armed conflicts, increased precipitation volatility significantly reduces human rights protections for multiple years. Indicators for the three mechanisms also show indirect relationships to repression through the precipitation variable, consistent with our theory. We illustrate our findings with case vignettes from Benin and Mali. Our results demonstrate that climate volatility alters states’ strategies for addressing internal security threats.

Time-heterogeneous impacts of global temperature cycle on world business cycle

ABSTRACT We study the statistical relationship between the world business cycle and the global temperature cycle. To amplify the signal-to-noise ratio, we estimate a two-state latent dynamic process from the original data using the endogenous regime-switching methodology. Subsequently, we apply a time-varying structural VAR analysis to identify the time-heterogeneous relationship between the extracted latent factors. Our findings provide empirical evidence that the global mean temperature cycle has a negative impact on the world business cycle during super El Niño periods, which are characterized by relatively high temperature variance records given the past information.

A VAR Framework of Exchange Rates, Interest Rates, and Inflation Through COVID-19 in Turkey: Empirical Evidence From Linear Cointegration and Causality Analysis

In this study, we investigate the impact of exchange rates and interest rates on inflation in Turkey using monthly data from January 2004 to July 2020 obtained from the Turkish Statistical Institute. Our vector autoregressive (VAR) model showed an evidence of stochastic behaviour among the series. The autoregressive distributed lag (ARDL) results showed a short-run and long-run covariate between exchange rates, interest rates and inflation. Specifically, the findings showed a short-run and long-run relationship between inflation, producer price index (PPI), and Turkish interbank offer rates (TIBOR). However, there was no connection between inflation and dollar exchange rates (DSR) and commercial banks’ interest rates (CBIR). The VAR Granger causality results revealed the variables to be exogenous except DSR, which displayed endogenous to other variables. Nevertheless, the results revealed unidirectional causality from the producer price index (PPI) to DSR and unidirectional causation from TIBOR to CBIR. It means an increase in production costs through raw materials importation led to devaluation of the Turkish lira. Similarly, TIBOR rates drive CBIR high, making domestic lending more expensive, which will inhibit loan provisions to the private sectors, which will result in an economic contraction and eventually high inflation. Our unit roots breakpoint results pointed to breaks in the dataset were between 2016-2019, reflecting the effects of the Fetullah Terrorist Organization (FETO) failed coup, the 2018 U.S. embargo, and an assumed fiscal dominance as the major and direct causes of economic instability and inflation. However, Covid-19 may have acted as a contributing factor since then. Thus, we recommend that the monetary authorities articulate policy to avoid the assumed fiscal dominance.

Evaluating Enteric Fermentation-Driven Environmental Kuznets Curve Dynamics: A Bayesian Vector Autoregression Comparative Study of the EU and Least Developed Countries

Global warming and climate change, primarily driven by human activities, with agriculture playing a significant role, have become central topics of scientific research. Livestock production, especially enteric fermentation, is a major source of greenhouse gas emissions, making it a focal point for both climate change adaptation and mitigation strategies. Both the European Union (EU) and Least Developed Countries (LDCs) are highly dependent on agriculture, particularly livestock, which plays a key role in their economic growth. In developing countries, livestock systems are evolving rapidly due to various factors, while in the EU, the livestock sector remains economically and socially significant, representing 36% of total agricultural activity. This study explores the environmental impact of enteric fermentation in livestock production, alongside the economic value it generates in both the EU and LDCs. The analysis utilizes a Bayesian Vector Autoregression (BVAR) methodology, which provides a more robust performance compared to traditional models like Vector Autoregression (VAR) and the Vector-error Correction Model (VECM). This research identifies significant relationships between the variables studied, with structural breaks quantified to reflect the impact of initiatives undertaken in both regions. Interestingly, the results challenge the environmental Kuznets curve, which hypothesizes an inverted U-shaped relationship between economic growth and environmental degradation, as proposed by Stern. This suggests that stronger economic incentives may be necessary to enhance policy effectiveness and promote eco-efficiency. The distinctive characteristics of livestock production in the EU and LDCs should be carefully considered when shaping agricultural policies, with a strong emphasis on farmer education as a critical factor for success. Additionally, corporate management practices must be tailored to address the unique needs, strengths, and challenges of livestock businesses in these two diverse regions.

Interactive effects of intangible cultural heritage and tourism development: a study based on the data panel PVAR model and coupled coordination model

AbstractExploring the coordination and analysis of intangible cultural heritage (ICH) and tourism development is a key issue in advancing cultural heritage and conservation in Chinese society. This paper employs panel data from 30 Chinese provinces from 2013 to 2022, utilizing the coupled coordination degree model and the panel vector autoregression (PVAR) model. In terms of provinces and regions, the data investigates the development relationship between intangible cultural heritage and tourism. The results of the study are: 1. The degree of coupling coordination between the two has shown an increasing trend over the ten-year sample data period. In 2021 and 2022, COVID-19 will have a stronger negative impact on the coupling degree. 2. The eastern coastal provinces of China, as well as the municipal areas, have a higher level of coupling coordination. Inland provinces, such as those in central and western China, exhibit relatively lower levels of coordination. The PVAR model data reveals a positive correlation between the interactions generated by intangible cultural heritage. The development of intangible cultural heritage has a particular impact on tourism. Intangible cultural heritage, in turn, depends on its own development. We should strengthen the development of intangible cultural heritage in the future. The study's findings provide theoretical references for the coordinated development of intangible cultural heritage and tourism in China.

Effects of Natural Disasters on West Africa Sahel’s Economy: The Case of Burkina Faso, Mali and Niger

The Sahel region is facing the adverse effects of the natural disasters due to the drought and floods which are the most frequent events. These effects can be spread and affected the all the Sahel States economy. Therefore, this study attempts to analyze the effects of these natural disasters on the macroeconomic variables such as production, consumption, investment and inflation in the three (3) the northern central Sahel States. To do so, the panel vector autoregressive model (PVAR) is run on the World Bank and the International Emergency Disasters Database (EM-DAT) data from 1990 to 2021. The results revealed that the occurrence of drought shock generates significantly a negative impact on the GDP per capita at 5% level across the northern central Sahel countries (Burkina Faso, Mali, and Niger). Also, the lagged of the macroeconomic variables such as production, investment and inflation have respectively a significant and positive impact on consumption at 5% level, production at 5% level and consumption at 10% level. Furthermore, the results show an asymmetry of natural disaster shocks at the scale of the northern central Sahel States but these shocks are symmetrical in pairs. As a result, the appropriate policies must make to absorb these shocks and avoid any hysteresis effect.

How Road and Rail Transport Respond to Economic Growth and Energy Prices: A Study for Poland

Transport drives economies. This statement covers complex and multifaceted economic, environmental, and political issues. The literature mainly describes the unidirectional impact of transport on the economy, and far less often bilateral or reverse impacts. This is the context in which this study was conducted. The question of whether the economy (GDP and exports and imports) and energy prices (crude oil and diesel) have an impact on road and rail transport in Poland was examined. The research was based on correlation methodology and VAR modelling for the January 2010–March 2024 period (quarterly data). It was found that there is no sufficiently strong basis to speak of an inverse relationship, i.e., that the economy is the cause of transport. This confirms the majority of studies, but it has been shown that this relationship occurs in the current period. And this statement means that both road transport, which is developing, and rail transport, which is declining in Poland, are able to serve the economy on a continuous basis. Moreover, rail transport is positively affected by changes in fuel prices, but the basic energy resource used in rail transport is not fuel but electricity. Therefore, as fuel prices rise, investment in rail transport or combined transport can have positive economic and environmental effects in the future.

Large quarterly Bayesian vector autoregression model for the modern Russian economy

It is often difficult to choose the necessary variables when we are forecasting the economic dynamics. On the one hand, vector autoregression (VAR) models can solve this problem by taking into account a sufficiently large number of variables. However, on the other hand, excessive parametrization of such models is not always justified, because it is often possible to choose such a small combination of variables the predictive power of which will be no worse than unlimited VARs with a large number of variables and lags. Bayesian methods help to solve this problem by introducing a priori restrictions on the VAR coefficients. The main goal of this work is to build a Large quarterly Bayesian vector autoregression (QBVAR) for the modern Russian economy. The hypotheses of the study: 1) BVAR(p) models reveal their maximum potential with a small number of variables, but with a large number of lags, 2) The Minnesota prior distribution is not always the best option for the modern Russian economy in the BVAR(p) model. As a result of the analysis, it turned out that for a number of the most important macroeconomic variables (GDP, inflation, key rate, unemployment, wages), Normal-Flat / Wishart-type priors turned out to be the optimal. In case of 29 input variables and two lags, the predictive power of the QBVAR(2) model is worse than the equivalent frequency VAR(2) or VECM(2). However, when it was possible to find the optimal combinations of variables, QBVAR(p) turned to be several times more accurate than its analogous frequency VAR(p) for all important macro indicators. In addition, the stiffness parameters of the considered a priori distributions decreases with increasing lags entering the model, i. e. the higher the order of the model with the minimum optimal set of variables, the tighter the distribution is necessary to obtain more accurate predictions. For the Russian realities, it is necessary to use very tight priors, 6–8 variables and 9–12 lags in a quarterly representation.

Exchange Rate Dynamics and Monetary Fundamentals: Evidence from South Asian Countries

This paper examined the role of monetary fundamentals in determining nominal exchange rate changes in South Asian countries. The Vector Autoregressive (VAR) technique and Autoregressive Distribution Lag (ARDL) were used. Results from the VAR indicate that the exchange rate responds to shock originating from differences in money supply, inflation differential, interest rate, and real income differential and are consistent with the short-run predictions of the monetary model of the exchange rate. The results show that inflation and interest rate differentials are responsible for the majority of changes in exchange rates in the short run. The conclusion from the result is that, particularly in the short run, monetary fundamentals are the primary causes of exchange rate fluctuations in South Asian countries.

A comparative analysis of variants of machine learning and time series models in predicting women’s participation in the labor force

Labor force participation of Egyptian women has been a chronic economic problem in Egypt. Despite the improvement in the human capital front, whether on the education or health indicators, female labor force participation remains persistently low. This study proposes a hybrid machine-learning model that integrates principal component analysis (PCA) for feature extraction with various machine learning and time-series models to predict women’s employment in times of crisis. Various machine learning (ML) algorithms, such as support vector machine (SVM), neural network, K-nearest neighbor (KNN), linear regression, random forest, and AdaBoost, in addition to popular time series algorithms, including autoregressive integrated moving average (ARIMA) and vector autoregressive (VAR) models, have been applied to an actual dataset from the public sector. The manpower dataset considered gender from different regions, ages, and educational levels. The dataset was then trained, tested, and evaluated. For performance validation, forecasting accuracy metrics were constructed using mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), R-squared (R2), and cross-validated root mean squared error (CVRMSE). Another Dickey-Fuller test was performed to evaluate and compare the accuracy of the applied models, and the results showed that AdaBoost outperforms the other methods by an accuracy of 100%. Compared to alternative works, our findings demonstrate a comprehensive comparative analysis for predicting women’s participation in different regions during an economic crisis.

Analyzing financial interdependencies: The cointegration of Jordan and BRIC stock markets

This study examines the financial integration between Jordan and the BRIC economies (Brazil, Russia, India, and China) to determine whether long-term equilibrium relationships exist and to assess implications for portfolio diversification and policy. Drawing on daily stock index data from 01 January 2014, to 31 August 2024, the study employs econometric techniques, including Granger Causality tests, Johansen Cointegration, and Vector Autoregression (VAR). The stationarity of stock indices at the first difference level is confirmed through unit root testing. Results indicate minimal long-term cointegration between Jordan and BRIC markets, pointing to low integration and potential diversification benefits for institutional investors. However, short-term causal links—particularly between Jordan and the Russian and Indian markets—highlight these countries’ influence on Jordan’s stock fluctuations. The findings suggest that, in the absence of long-term cointegration, investors may mitigate risk by investing in less correlated markets, such as Jordan, while leveraging short-term partnerships with Russia and India. Additionally, the study provides valuable insights for business leaders considering strategic alliances with BRIC counterparts in sectors like technology, agriculture, and energy, and calls for future research into factors like regulatory frameworks and geopolitical stability that may limit long-term financial integration. These results have significant implications for institutional investors, business executives, and policymakers, suggesting targeted strategies for financial stability, risk mitigation, and economic collaboration.

The determinants of volatility connectedness of South African equity super sectors

The paper aims to explore the determinants of total volatility connectedness of nine super sectors on the Johannesburg Stock Exchange (JSE) market from 3rd January 2006 to 31st December 2021. These sectors are Automobile and Parts, Chemical, Telecommunication, Technology, Energy, Health, Finance, Insurance, and General Industrials. The paper applied Diebold and Yilmaz connectedness matrix and the time-varying parameter – vector autoregressive (TVP-VAR) model to determine the sectorial total volatility connectedness index (STVCI). After that, the nonlinear autoregressive distributed lag model (NARDL) was used to determine the asymmetric effects and the drivers of STVCI. It was found that the partial sum decomposition of the South African volatility index (SAVI) and Economic Policy Uncertainty Index (EPU) are the key determinants of the STVCI both in the long and short run. However, domestic market return (DMR) shows no significant asymmetric effect on STVCI. The study concluded that SAVI and EPU are the key determinants of volatility connectedness among the JSE super-sectors. The results unveil important implications for sectorial investors and policymakers on potential regulations and stability of the significant determinants of spillover risk.

Employment Shift in Response to a Technology Shock: An Analysis of Two Rigidities and Two Agents

This paper examines the relationship between a technology shock and employment, considering price, wage rigidities, and heterogeneous agents. To explore this relationship, we utilized a Dynamic Stochastic General Equilibrium (DSGE) model, incorporating households with varying savings rates. For empirical validation, we conducted a Structural Vector Autoregression (SVAR) analysis using data from two economies with distinct savings patterns—the United States and China. This approach allowed us to assess the impact of technology shocks on employment dynamics across different savings environments. Under these conditions, we observe that the effect of technology on aggregate employment is initially positive. Still, it gradually decreases in the mid-term, eventually switching to a negative impact before slowly recovering to equilibrium. The reason for this phenomenon depends on (i) the magnitude of fluctuations in price and wage, precisely, which variable’s fluctuations have a greater magnitude, and (ii) which effect, between income effect and substitute effect, is preferred by restricted and unrestricted households. Due to (i), real wages change, and because of (ii), households make different labor supply decisions, leading to fluctuations in employment in response to technology shocks.

Geopolitical Risks and Stock Market Volatility in the SAARC Region

Abstract This study examined the stock market volatility of the member states of the South Asian Association for Regional Cooperation (SAARC) and geopolitical risk (GPR). The analysis period covered January 2014 to March 2024, and the time–frequency wavelet method was used to process the data. The time-varying parameter vector autoregression and spatial autoregressive models helped determine the dynamic connectedness of volatility in the analyzed states. The findings revealed similar stock market connections in Bangladesh and India. In addition, a comparative analysis of stocks in India and Pakistan led to the identification of common elements. The connection between geopolitical concerns and Sri Lankan stocks was the strongest and increased in intensity after 2019. GPR and Nepal’s stock market maintained a continuous but low-intensity relationship. The dynamic connectedness between member states’ stock markets was limited during the review period. The study results could encourage SAARC governments to bridge their political differences to ensure that South Asia becomes a strong partner in the global economy. Equally, our results can benefit investors, financial institutions, regulatory authorities, and governments.

Tail risk transmission between decomposed oil shocks and green finance markets: evidence from an EVT–TVP–VAR approach

ABSTRACT This paper provides a novel framework based on extreme value theory, i.e. the autoregressive conditional Fréchet–time-varying parameters–vector autoregression (AcF–TVP–VAR) framework, to explore the tail risk spillovers between decomposed oil shocks (supply, demand, and risk shocks) and green finance markets. The empirical results reveal that there are significant time-varying tail risk spillovers between the two markets, with spillovers intensifying during extreme events. Additionally, throughout the sample period, supply shock is found to be a net transmitter of tail risk, while clean energy market is a net receiver. Notably, the safe haven properties of green bond market are captured, indicating it can serve as a diversified investment option. The findings are important for financial investors and policymakers.

The Central Role of Daily Hopeful Feelings in Predicting Well-Being: A Network Perspective.

Hope is associated with many positive outcomes. However, most research on hope has focused on its cognitive aspect, with few studies examining the affective aspect. To further clarify the nature of hope and its association with well-being, this study examined the relationship between the affective aspect of hope-namely, hopeful feelings-and various aspects of well-being. A daily diary analysis was conducted with 187 Chinese adults (meanage = 28.17 ± 5.56). Hopeful feelings and affective (positive and negative emotions), eudaimonic (meaning in life), and evaluative (life satisfaction) aspects of well-being were assessed twice per day (morning and night) for 14 consecutive days. A multilevel vector autoregressive model was used to investigate the temporal, contemporaneous, and between-person networks of hopeful feelings with positive emotion and well-being. Hopeful feelings had the strongest outstrength in the temporal networks and were the most central node in the between-person networks. In addition, hopeful feelings exhibited significant cross-lagged predictive roles on all the well-being nodes except negative emotion. These findings revealed a close association between hopeful feelings and well-being, with hopeful feelings predicting various aspects of daily well-being. Future interventions to improve well-being should focus on the enrichment of daily hopeful feelings.

Big Data Analytics for Forecasting Tourism Recovery in Bali Island Using Multivariate Time Series

Bali is a famous tourist area and can significantly contribute to the Indonesian tourism sector. The COVID-19 pandemic has made Indonesian tourism, including Bali tourism, experience a decline. In March 2022, COVID-19 cases decreased, and the government began to relax some policies. The tourism sector is vital in economic recovery efforts after the COVID-19 pandemic. Therefore, it is necessary to identify tourism recovery to determine strategies and policies related to Indonesian tourism, especially in Bali. Multivariate time series forecasting of tourism demand can be used to identify tourism recovery using several significant data sources. The methods used are Vector Autoregressive (VAR), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The data used are the monthly official number of tourists, room occupancy rate, Google Trends, number of booking.com user reviews, and nighttime light intensity in Bali Province from January 2019 to December 2022. The results show that the best forecasting method is VAR, and modeling with multivariate time series forecasting can improve the performance of forecasting results. In addition, big data can be used as a source of supporting data that can provide better forecasting results, and the size of the dataset affects the selection of the best model. Furthermore, the descriptive and forecasting analysis results show that Bali tourism has experienced post-pandemic tourism recovery. The strategies and policies of the Bali government to restore Bali tourism faster are good enough.

Illusion of stability: An empirical analysis of inflation data manipulation by russia after 2022

This paper explores the perceived resilience of russia’s economy under severe sanctions, investigating the potential falsification of economic data to demonstrate the growth. The hypothesis is that the relationship between the official inflation rate and the FMCG deflator index during 2019–2021 significantly differs from that of 2022–2024. Statistical methods, such as correlation analysis, Granger causality tests, and differences tests (e.g., t-tests and Wilcoxon tests), are used along with vector autoregressive (VAR) models and robust linear regressions. The study covers the pre-invasion period (2019–2021) and the post-invasion period (2022–2024), focusing on indicators like the official inflation rate, inflation expectations, CPI, and the FMCG deflator index. Findings reveal a shift from a direct to an inverse correlation between official inflation and the FMCG deflator post-2022, suggesting data manipulation. Pre-2022 models predict inflation 2-3 times higher than both post-2022 models and official statistics, raising concerns about the reliability of russia’s economic data. Further research should explore indirect metrics, such as electricity production and cargo shipments, for additional evidence of data falsification. AcknowledgmentsAlex Plastun gratefully acknowledges financial support from the New Europe College (NEC), the Center for Advanced Study, and Sumy State University.Anna Vorontsova gratefully acknowledges financial support from Sumy State University.

Interval Time Series Forecasting: An Innovative Approach Transforming Interval to Single Time Series

ABSTRACTInterval Time Series are present in everyday life. An example is the opening and closing value of some stock in a market for certain time intervals. The forecasting plays an essential role in many financial organisations. The development of new mathematical tools or improving the existing ones will lead to more accurate forecasting techniques. Interval Arithmetic is a mathematical field that uses intervals by nature and algorithms that use it are involved in the solution of Interval Time Series. Another classical algorithm is VAR models. In this paper, a method that comes from the insurance sector is used to forecast Brent Oil monthly values. The innovation of what we propose is that it converts the Interval Time Series system into a single time series and can propagate the results back to each time series of the system. This way the researcher works with only one time series instead of two (or more). The forecasting algorithm is a choice of the researcher, expediting the development of forecasting (even ARIMA can be applied). We demonstrate our methodology in forecasting the Brent Oil monthly prices by applying the ANFIS algorithm.