Vector Autoregression Research Articles

Countering the “Climate Cult” – Framing Cascades in Far-Right Digital Networks

ABSTRACT In many contemporary democracies, digital networks on the far-right have established themselves as “alternatives” to liberal institutions. Within this nexus of parties, hyper-partisan news, and social media channels, opposition to climate change mitigation has emerged as a central cross-cutting issue. Despite the prominence of these trends, we still know relatively little about the hierarchies that govern these communicative ties. This study draws on the concept of cascading frame activation to trace communication flows between four distinct actor types within the German far right. We apply a mixed-methods approach across social media and websites over a period of 19 months. First, we conduct a time series analysis via a vector autoregression model, to assess potential causal relationships between the issue agendas of far-right actor types. Automated text analysis then inductively generates a lexicon exclusive to far-right posts about climate change, which we qualitatively assess and manually cluster into distinct frame categories. We conclude by tracing framing cascade sequences across the various actor types. Our results reveal significant associations between the issue agenda of far-right actors, especially among right-wing news sites (RNS) and hyperactive accounts. Within this network, the most dominant frames demarcate out-groups, such as the Green Party or climate activists. Notably, RNS play a central role in shaping this discourse, initiating 42% of the identified frames before they find wider dissemination. These findings expand our understanding of hierarchies and new communication pathways among partisan media, political parties, and their extended digital networks on the far right.

Capital Market and Economic Growth in Nigeria

In spite of its expected role of bridging the saving and investment gap for economic growth, the impact of the Nigerian capital market on the growth of the economy remains unclear. This study looked at the influence of the capital market on economic growth in Nigeria between 1981 and 2022, using vector autoregressive (VAR) analysis. The variables used included: real gross domestic product (RGDP), market capitalization (MCAP), total value of securities traded (TVST), and gross fixed capital formation (GFCF). The study concluded that while RGDP and MCAP influenced their own outcome as well as the outcome of other variables positively in both the short and long run, TVST and GFCF influenced their own outcome as well as the outcome of other variables negatively in both the short and long run, using the VAR system tools of impulse response function and forecast error variance decomposition. Based on the findings of this study, we recommended among others the urgent need to boost the perception of local firms listed in the Nigerian stock exchange both locally and internationally in terms of the total value of their corporate assets and performance measured by price-to-earnings, price-to-sales and return-on-equity metrics. This can be improved upon when these firms are able to increase the quality of their products and services thereby making them better competitors in the global space.

Women's empowerment and tourism: Emerging determinants of poverty in low and middle‐income countries

AbstractThis study examines the impact of women's empowerment and tourism on poverty in the developing economies of South Asia, sub‐Saharan Africa and Latin America. Current study formulates a vector autoregression (VAR) model to estimate the long‐run association between emerging determinants of poverty reduction. Our empirical findings show that tourism and women's empowerment have significant and negative long‐term associations with poverty in South Asia, Latin America and Africa. Moreover, this study found that women's empowerment is relatively more important in poverty reduction than tourism in South Asia and Africa, whereas in Latin America, tourism has a relatively greater contribution to poverty. Similarly, the results of the vector error‐correction (VECM) Granger Causality/Block Exogeneity Wald test indicated unidirectional causality between women's empowerment and poverty, tourism and poverty in South Asia. Similarly, unidirectional causality exists between women's empowerment and poverty in Latin America and Africa. Our econometric discussion helps suggest policy suggestions to overcome the challenges of poverty in developing economies.

Analyzing the influence of web search behavior on electricity market price: a case study of Japan electric power exchange

AbstractThe Japan Electric Power Exchange (JEPX) has introduced a feed-in premium to promote the trading of renewable energy electricity in the market; thus, the exchange has become increasingly important for RE companies to maintain profitability in market trading. However, electricity prices are not only affected by directly measurable factors such as electricity demand, fuel prices, and weather but also by corporate bidding strategies, social conditions, and other human behaviors, making it difficult to predict electricity prices. Given that electricity demand is related to human behavior, this study focuses on web search behavior and clarifies the relationship between keyword search volumes and electricity market prices in Japan. Correlation and vector autoregression analyses results show a moderately strong positive correlation between the logarithmic difference of the keyword search volume and that of the electricity price. In addition, we find that the logarithmic difference of the electricity price tends to increase when that of the keyword search volume on the previous day increases. These results suggest that search volumes of specific keywords can be effective explanatory variables for area price prediction models and can help identify signs of price spikes.

Effect of conventional and unconventional monetary policy shocks on housing prices in Canada

This paper investigates the relative importance and effect of conventional and unconventional Canadian monetary policy surprises on housing prices. Using a credible approach to identify structural monetary policy shocks in Canada and a comprehensive Bayesian VAR model to analyze their effects on financial and macroeconomic variables, we find that both conventional monetary policy and quantitative easing shocks have a significant and persistent effect on housing prices in Canada. However, the inflationary effect of a quantitative easing shock is more pronounced than that of an easing target monetary policy surprise. Specifically, the peak effect of a 25-basis point expansionary conventional monetary policy shock is a 2.30% increase in real housing prices while a comparable quantitative easing surprise leads to a peak of 4.56% increase in real housing prices. We conclude that expansionary monetary policy in various forms has had significant inflationary effects on Canadian housing prices. But quantitative easing surprises stand out as the most prominent contributory factor to the escalating Canadian real estate price. Quantitative easing impact consistently outweighs the effects of forward guidance and conventional monetary policy shocks.

COVID-19, the Russia–Ukraine war and the connectedness between the U.S. and Chinese agricultural futures markets

This study focuses on how recent global crises such as the COVID-19 pandemic and the Russia–Ukraine war have affected the relationship between the U.S. and Chinese agricultural futures markets. By applying wavelet coherence analysis (WCA) and time-varying parameter vector autoregression (TVP-VAR), we obtain the following findings. First, both events have changed the correlation and lead–lag comovement between U.S. and Chinese soybean and corn futures returns but have little impact on the comovement between the two cotton futures returns. Second, U.S. agricultural markets transmit more volatility risk to Chinese markets than the risk spillover from the reverse direction. Third, the risk spillover enhancement effect from the war is stronger than that from the pandemic, which is obvious in both the soybean and corn futures markets but not in the cotton market. Our paper has implications for policy makers seeking to stabilize agricultural commodity prices during global crisis episodes and for designing strategies for cross-market hedging of spillover risks among commodity markets for international investors.

Heterogeneous macro and financial effects of ECB asset purchase programs

Like other central banks, the ECB resorted to asset purchase programs (APPs) to replace conventional policy measures. We examine their impact on the Euro area with a focus on the heterogeneity among its constituents and across financial markets. Our analysis combines a Bayesian structural VAR with an identification scheme based on market surprises at the announcement time, effectively capturing structural dynamics. At the Euro area level, APPs stimulate the economy, lower government bond yields, elevate stock prices, and reduce corporate and sovereign stress. The impact shows heterogeneity in the stock market with a widened value-growth spread in stocks and varying sector impacts, particularly favoring financial stocks, and across countries with stronger effects on southern Euro area countries. Our results show strong spillover effects between countries, indicating challenges in the precise targeting of APPs.

Loss-based prior for the degrees of freedom of the Wishart distribution

Motivated by the proliferation of extensive macroeconomic and health datasets necessitating accurate forecasts, a novel approach is introduced to address Vector Autoregressive (VAR) models. This approach employs the global-local shrinkage-Wishart prior. Unlike conventional VAR models, where degrees of freedom are predetermined to be equivalent to the size of the variable plus one or equal to zero, the proposed method integrates a hyperprior for the degrees of freedom to account for the uncertainty in the parameter values. Specifically, a loss-based prior is derived to leverage information regarding the data-inherent degrees of freedom. The efficacy of the proposed prior is demonstrated in a multivariate setting both for forecasting macroeconomic data, and Dengue infection data.

Blended identification in structural VARs

The proposed blended approach combines identification via heteroskedasticity with sign/narrative restrictions, and instrumental variables. Since heteroskedasticity can point identify shocks, its use results in a sharp reduction of the potentially large identified sets stemming from other approaches. Conversely, sign/narrative restrictions or instrumental variables offer natural solutions to the labeling problem and can help when conditions for point identification through heteroskedasticity are not met. Blending these methods together resolves their respective key issues and leverages their advantages. We illustrate the benefits of the approach in Monte Carlo experiments, and apply it to several examples taken from the literature.

Enhancing prediction and analysis of UK road traffic accident severity using AI: Integration of machine learning, econometric techniques, and time series forecasting in public health research

This research project explored into the intricacies of road traffic accidents severity in the UK, employing a potent combination of machine learning algorithms, econometric techniques, and traditional statistical methods to analyse longitudinal historical data. Our robust analysis framework includes descriptive, inferential, bivariate, multivariate methodologies, correlation analysis: Pearson's and Spearman's Rank Correlation Coefficient, multiple logistic regression models, Multicollinearity Assessment, and Model Validation. In addressing heteroscedasticity or autocorrelation in error terms, we've advanced the precision and reliability of our regression analyses using the Generalized Method of Moments (GMM). Additionally, our application of the Vector Autoregressive (VAR) model and the Autoregressive Integrated Moving Average (ARIMA) models have enabled accurate time series forecasting. With this approach, we've achieved superior predictive accuracy and marked by a Mean Absolute Scaled Error (MASE) of 0.800 and a Mean Error (ME) of -73.80 compared to a naive forecast. The project further extends its machine learning application by creating a random forest classifier model with a precision of 73%, a recall of 78%, and an F1-score of 73%. Building on this, we employed the H2O AutoML process to optimize our model selection, resulting in an XGBoost model that exhibits exceptional predictive power as evidenced by an RMSE of 0.1761205782994506 and MAE of 0.0874235576229789. Factor Analysis was leveraged to identify underlying variables or factors that explain the pattern of correlations within a set of observed variables. Scoring history, a tool to observe the model's performance throughout the training process was incorporated to ensure the highest possible performance of our machine learning models. We also incorporated Explainable AI (XAI) techniques, utilizing the SHAP (Shapley Additive Explanations) model to comprehend the contributing factors to accident severity. Features such as Driver_Home_Area_Type, Longitude, Driver_IMD_Decile, Road_Type, Casualty_Home_Area_Type, and Casualty_IMD_Decile were identified as significant influencers. Our research contributes to the nuanced understanding of traffic accident severity and demonstrates the potential of advanced statistical, econometric, machine learning techniques in informing evidence based interventions and policies for enhancing road safety.

Monetary policy surprises on the banking sector: The role of the information and pure monetary shocks

This paper analyzes how monetary policy surprises in Chile affects the real and financial sector separating between a pure monetary policy shock and an information shock. Using inter-day movements of futures of interest rate in the banking system, we identify an information shock when labor data is released and a pure monetary policy shock when the central bank reveals their interest rate decision, and their effects are quantified through an external vector autoregression model. Our results suggest that a pure monetary policy shock produce an appreciation of nominal exchange rate, and contractionary effects on the economy. However, an information shock does not necessarily produce adverse effects. This paper contribute to the literature in two dimensions: studying the effect of the main driver behind the central bank announcements, and their transmission to the banking sector and consequently to the real and monetary sector.

Insights into the dynamics of market efficiency spillover of financial assets in different equity markets

This paper provides multidisciplinary insights into the dynamics of market efficiency spillover of financial assets, considering Bitcoin, DXY (U.S. Dollar Index), S&P 500, and crude oil in different equity markets. Given this, we employ the multifractal detrended fluctuation analysis (MF-DFA) and the time-varying parameter vector autoregressive models (TVP-VAR) to illuminate these complex interplays between these assets and other equity markets. Our findings display that Bitcoin acts as a net transmitter, whereas DXY functions as a net receiver. Additionally, Bitcoin, DXY, and S&P 500 clearly exhibit dynamics in information transmission, whereas crude oil does not. It means that the low connectedness of the oil market in terms of market efficiency spillover, suggesting that the oil market may play an effective role as a hedging instrument for other assets.

The Risk Spillover Effects and Network Connectedness Between Real Estate and Other Sectors in China

Using a high-dimensional time-varying parameter vector autoregression framework, we propose a volatility spillover network to analyze risk contagion direction, path, and intensity under supply-demand relationships when important events break out in the real estate market. We characterize daily return volatility spillovers across firms from January 2010 to April 2022 and find a high correlation among these firms because the real linkage based on industrial chain structure strengthened. Meanwhile, the outbreak of essential events in the real estate market corresponds to several peak values of return volatility. The empirical results show that excessive correlation induces risk contagion across the network, and spillover effects are significant under extreme market conditions, manifesting as cross-industry contagion and intersectoral diffusion. The volatility spillover effects within real estate firms are higher than spillover effects across markets. In addition, the industrial chain structure better explains the mechanism of cross-industry risk contagion, firms located upstream of the real estate industry chain are risk receivers, and those located downstream are risk spillovers. Real estate firms mainly spread risks to financial institutions and firms that provide production goods. Our findings are of constructive significance to the related policies by regulatory authorities and provide multi-angle empirical evidence support for the risk supervision theory of “too connected to fail.”

Examining the local government financial pressure on housing prices: evidence from China

The study examines the influence of local government financial pressure on average real estate sales prices. Annual data from the Chinese National Bureau of Statistics from 2000 to 2020 were analyzed based on the vector autoregressive model. Fixed asset investment and the growth rate of the population were included as moderators to analyze their moderating effects on the relationship between local government financial pressure and average real estate sales price. Local government fiscal pressure has a significant positive impact on the average real estate selling price, fixed asset investment, and population growth rate are negatively correlated with average real estate sales price. Therefore, this paper suggests reducing the fiscal revenue and expenditure gap faced by local governments, alleviating the financial pressure on local governments, increasing the proportion of residential land supply, and limiting the dependence of local governments on land finance, to restrain the price fluctuation of the real estate market. Keywords: Fixed assets; growth rate; Housing price; investment; local fiscal pressure; population.

The Response of Macroeconomic Variables to Government Spending Shocks in the Sudanese Economy 1989-2019: Comparing the Structural Shocks (DSGE Approach) and Impulse Response (SVAR Model)

The importance that different (approaches and models) to modeling the macroeconomy place on theoretical coherence compared to their capacity to match the data and the quality of the econometric model description varies. Dynamic stochastic general equilibrium (DSGE) models are more theoretical, whereas vector autoregression (SVAR) models provide a better match to the data. For developed economies, there are well-established publications on measuring the response of economic indicators to government spending shocks and aggregate macroeconomic activity. In addition, such empirical studies in emerging nations are scarce. This research seeks to fill this void by utilizing the DSGE model and the SVAR approach to investigate the influence of the response of macroeconomic variables to government spending shocks in the Sudanese economy from 1989 to 2019. The findings indicate that the influence of government expenditure shocks on the Sudanese economy is inconsistent with Keynesian principles, as some selected macroeconomic indicators do not respond positively to government expenditure shocks. The non-responsiveness of the inflation rate and exchange rate to government expenditure shocks is demonstrated; this finding may indicate the monetary authority’s weakness in managing monetary variables in the Sudanese economy. In most situations, fiscal and monetary policies were in sync, and “double expansionary” and “double contractionary” policy coordination may be the proper approach; and also create tools that fit the Sudanese economy’s structure.

Analysis of the Promotion Effect of Agricultural Economic Management on Rural Economic Development Based on VAR Model

Under the background of my country’s new rural construction and the implementation of the rural revitalization strategy, the rural economy has ushered in an unprecedented opportunity for development. Agricultural economic management plays a catalytic role in providing direction guidance for rural economic development, promoting sustainable rural economic development, and providing a good environment for rural economic development. However, there are some drawbacks in agricultural economic management, which are mainly reflected in the imperfect agricultural economic management system and the lag in information infrastructure construction. In view of this, the author puts forward corresponding the advanced VAR model from the aspects of improving the agricultural economic management system, improving the application level of information technology, and improving the quality of the agricultural economic management team. Research shows that: through effective management of agricultural economy, the obstacles to agricultural economic development can be reduced from the source, thereby promoting the healthy and sustainable development of rural economy.

한국의 거시적 재정정책의 효과 분석

This study employs a dynamic analysis to assess the effectiveness of fiscal policy on the private sector within the Korean economy, utilizing long-term macroeconomic data. To achieve this, we conduct quantitative economic empirical analysis using the vector autoregressive model under the rolling window scheme. The major takeaways of this study confirm the necessity for strong fiscal policies in the Korean economy, given a new economic normal characterized by low growth, akin to advanced economies. Furthermore, it underscores the importance of implementing a balanced policy mix that combines both monetary and fiscal measures to guide the economy effectively towards stabilization.

주택순환국면에 따른 주택가격 및 거래량 간의 인과성 분석

It will be useful information to analyze using past housing market’s data can be predicted future direction. It will be very interesting information for market participants if the direction of prices and transaction volume can be predicted. Therefore, the purpose of this study is to identify the housing circulation phase by dividing it by period, focusing on the Seoul housing market, which is the center of price fluctuations in the current housing market, and to compare and analyze the correlation between housing prices and trading in each circulation phase. To analyze the correlation between the two variables, Granger causality test, vector autoregression model (VAR), and impulse response analysis were used. As a result of the study, the housing circulation phase is subdivided into four phases, and the direction of prices and transaction volume is analyzed differently for each phase. There is a positive correlation between price and trading and prices affect transaction volume in the short term, but the influence of transaction volume on prices is insignificant. However, it has been proven that one of the two variables has predictive power for the other. Although there is a limitation in that it is not possible to analyze the direct factors affecting the variables, it is believed that it can be used as useful information for housing participants and government policy officials.

NETWORK ANALYSIS OF THE MEXICAN STOCK MARKET

This study investigates the dynamics of equity networks in Mexico from 2018 to 2023, focusing on the impact of the COVID-19 pandemic. Methodological steps include calculating stock returns, estimating annual GARCH models, constructing lower-tailed dependency matrices, and forming networks based on these matrices. The characteristics of the resulting networks are described. In addition, 10,000 Erdos-Reyni simulations are performed to estimate GNAR models up to order two, selecting the best estimates according to AIC, BIC, and llk criteria. The predictive performance of GNAR models compared to univariate AR and VAR models is evaluated. These stages help to better understand the interconnection between Mexican financial markets, offering valuable insights for risk management and decision-making.

THE IMPACT OF TRADE LIBERALIZATION ON THE PERFORMANCE OF TANZANIA’S EXPORT SECTOR – A TIME SERIES ANALYSIS FROM 1980 TO 2019

Aim: This article aims to enhance understanding of the impact of trade liberalization on Tanzania’s export performance. As many studies conducted, both in developing and developed countries, have yielded mixed results and the relationship varies across different nations, it is difficult to provide a definitive answer to the question of whether trade liberalization affects Tanzania’s export sector without first conducting an empirical analysis. Methods: This study employs a quantitative research methodology because it allows for a larger sample size, greater objectivity, and accuracy. Due to the nature of the study and the data used, a statistical study design was chosen. Macroeconomic data spanning 1980 to 2019 was obtained from the World Bank and the Tanzania Bureau of Statistics to analyze the effects of trade liberalization on export performance using vector error correction and autoregressive distributed lag models. Results: The results reveal a sig­nificant positive correlation between trade liberalization and subsequent export performance, as well as the interdependencies between trade liberalization and foreign direct investment (FDI) strategies. There is a re­ciprocal relationship between trade liberalization and exchange rates, implying the significance of competent exchange rate management in enhancing export competitiveness. Conclusions: The study concludes that trade liberalization, FDI, and export performance have a long-term positive correlation. This implies that a carefully executed trade liberalization policy is crucial not only for the expansion of the export industry sector and the influx of capital, but also for the transformation and development of the nation.growth.