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The authors investigate the impact of the US stock market on the economies of the BRICS and major industrialized economies (G7).

The authors construct the world economy and the vulnerability between economies using three economic integration variables: bilateral trade, bilateral direct investment and bilateral equity positions. Global vector autoregressive (GVAR) empirical studies usually adopt trade integration to estimate models. The authors complement these studies by using bilateral financial flows.

The authors summarize the results in four points: (1) financial integration variables increase the effect of the US stock market on the BRICS and G7, (2) the US shock produces similar responses in these groups regarding industrial production, stock markets and confidence but different responses regarding domestic currencies: in the BRICS, the authors detect appreciation of the currencies, while in the G7, the authors find depreciation, (3) G7 stock markets and policy rates are more sensitive to the US shock than the BRICS and (4) the estimates point out to heterogeneities such as the importance of industrial production to the transmission shock in Japan and China, the exchange rate to India, Japan and the UK, the interest rates to the Eurozone and the UK and confidence to Brazil, South Africa and Canada.

The results reinforce the importance of taking into account different levels of economic development.

The authors construct the world economy and the vulnerability between economies using three economic integration variables: bilateral trade, bilateral direct investment and bilateral equity positions. GVAR empirical studies usually adopt trade integration to estimate models. The authors complement these studies by using bilateral financial flows.

The purpose of this study is to reveal the dynamics of house prices and sales in spatial and temporal dimensions across British regions.

This paper incorporates two empirical approaches to describe the behaviour of property prices across British regions. The models are applied to two different data sets. The first empirical approach is to apply the price diffusion model proposed by Holly et al. (2011) to the UK house price index data set. The second empirical approach is to apply a bivariate global vector autoregression model without a time trend to house prices and transaction volumes retrieved from the nationwide building society.

Identifying shocks to London house prices in the GVAR model, based on the generalized impulse response functions framework, I find some heterogeneity in responses to house price changes; for example, South East England responds stronger than the remaining provincial regions. The main pattern detected in responses and characteristic for each region is the fairly rapid fading of the shock. The spatial-temporal diffusion model demonstrates the presence of a ripple effect: a shock emanating from London is dispersed contemporaneously and spatially to other regions, affecting prices in nondominant regions with a delay.

The main contribution of this work is the betterment in understanding how house price changes move across regions and time within a UK context.

This article analyzes the impact of monetary policy on income inequality across 16 advanced economies. The author investigates three key points: (1) the relationship between domestic monetary policy and domestic income inequality, (2) the spillover effect of USA monetary policy (including quantitative easing) on international inequality and (3) the quantitative influence of the monetary policies of both the USA and the Eurozone on the formation of domestic income inequalities.

The author employed the Global Vector Autoregressive (GVAR) model, which uses Vector Autoregressive with Exogenous Variables (VARXs) models of each economy to build an integrated system that enables us to evaluate individual responses to global shocks.

The author's analysis reveals that (1) contractionary monetary policy exacerbates domestic inequality and (2) USA monetary policy, including quantitative easing, affects international inequality. Furthermore, the author's variance decomposition analysis highlights that USA monetary policy is especially influential on income inequality in Norway and Sweden. Additionally, the cointegrating analysis confirms that monetary policy's impact on inequality persists in the long term.

Most of the studies focused on investigating domestic economies as closed economies. However, the author's approach differs in that the author uses the GVAR, which treats all economies as open. This allows us to incorporate the world economy into the domestic dynamics and connect the economies using bilateral trade. Another advantage of the GVAR is that it captures spillover effects by modeling each economy and constructing the international economy.

In this work, the authors analyze the dynamic interdependencies between military expenditures and the real economy for the period 1970–2018, and the authors' approach allows for the existence of dominant economies in the system.

In this study, the authors employ a Network General Equilibrium GVAR (global vector autoregressive) model.

By accounting for the interconnection among the top twelve military spenders, the authors' findings show that China acts as a leader in the global military scene based on the respective centrality measures. Meanwhile, statistically significant deviations from equilibrium are observed in most of the economies' military expenses, when subjected to an unanticipated unit shock of other countries. Nonetheless, in the medium run, the shocks tend to die out and economies converge to an equilibrium position.

With the authors' methodology the authors are able to capture not only the effect of nearness on a country's military spending, as the past literature has documented, but also a country's defense and economic dependencies with other countries and how a unit's military expenses could shape the spending of the rest. Using state-to-the-art quantitative and econometric techniques, the authors provide robust and comprehensive analysis.

A major concern for policymakers and researchers is to ascertain the movement of price levels and employment rates. Predicting the trends of these variables will assist the government in making policies to stabilize the economy. The objective of this chapter is to forecast the unemployment rate and Consumer Price Index (CPI) for the period 2022 to 2031 for the Indian economy. For this purpose, the authors analyse the prediction capability of the univariate auto-regressive integrated moving average (ARIMA) model and the vector autoregressive (VAR) model. The dataset for India's annual CPI and unemployment rate pertains to a 30-year time period from 1991 to 2021. The result shows that the inflation forecasts derived from the ARIMA model are more precise than that of the VAR model. Whereas, unemployment rate forecasts obtained from the VAR model are more reliable than that of the ARIMA model. It is also observed that predicted unemployment rates hover around 5.7% in the forthcoming years, while the forecasted inflation rate witnesses an increasing trend.

This study aims to assess how real income, oil prices and gold prices affect housing prices in China from 2010 to 2021.

This study uses a novel bootstrap autoregressive distributed lag (ARDL) testing to empirically analyze the short and long links among the tested variables.

The ARDL estimations demonstrate a positive impact of oil price shocks and real income on housing market prices in both the phrases of the short and long run. Furthermore, the results reveal that gold price shocks negatively affect housing prices both in the short and long run. The result can be attributed to China’s housing market and advanced infrastructure, resulting in a drop in housing prices as gold prices increase. Additionally, the prediction of housing market prices will provide a base and direction for housing market investors to forecast housing prices and avoid losses.

To the best of the authors’ knowledge, this is the first attempt to analyze the effect of gold price shocks on housing market prices in China.

The paper aims at analyzing the co-movements between stock returns and oil prices (West Texas Intermediate, Brent) controlling or not for COVID-19.

It uses continuous wavelet transforms and wavelet coherence over the period July 19, 2019 to August 16, 2021 based on daily data. Continuous wavelet transforms provide an over complete representation of stock returns signals by letting the translation and scale parameters of the wavelets vary continuously.

There are not significant evidence supporting the fact that the COVID-19 has altered the relationship between stock returns and oil prices except perhaps in the case of South Africa. In fact, Southern African Development Community stock markets react more to oil prices than to health shock such as the COVID-19.

The findings of the study are original and have not been published anywhere prior.

Since 1966, the Severn crossing has been connecting England and Wales. In January 2018, its ownership returned to the UK Government, and this marked the start of a toll-free journey across the two countries and made commuting between the regions more affordable. In this paper, we examine the impact of the toll removal on the property market.

We employ property-level data from the Land Registry and a difference-in-differences (DiD) empirical model for the periods 2016–2018 and 2019–2021 to capture the pre- and post-toll removal dynamics. The DiD estimation allows us to examine the causal relationship between policy changes and property prices.

Our findings suggest that property prices in Newport and Monmouthshire (South East Wales) are positively affected by the policy, which results in a statistically significant increase of 5.8% more than those located in the South West England (Bristol and South Gloucestershire) region in the period 2019–2021. The impact can reach up to 13.1% for properties located in a 10 km radius of the bridge. The results indicate that the toll removal enables the ripple effect across the two markets by reducing commuting costs.

This is the first paper that examines the Severn Crossing case study. Its contribution is significant since we provide empirical evidence on how reduced transportation costs increase property prices in the lowest income region and have the opposite effect on the area with higher incomes and economic activity levels.

Carbon reduction in the construction industry is related to the achievement of carbon emission peaks and carbon neutrality targets. Therefore, exploring the influence of current carbon reduction policies on the construction industry is necessary. China’s low-carbon pilot (LCP) policy has been extensively studied, while LCPs mechanism and effectiveness on carbon reduction in the construction industry remain to be explored.

This study selected four provincial LCP regions as case studies and adopted the grounded theory method for case studies to analyze the implementation mechanism of the LCP policy on carbon reduction in the construction industry. Then, this study adopted the propensity score matching and difference-in-differences regression (PSM-DID) approach to evaluate the influence of the LCP policy on carbon intensity (CI) in the construction industry by using panel data taken from 30 provinces in China between 2008 and 2017.

The authors found that (1) the LCP policy promotes carbon reduction in the construction industry through the crossing implementation mechanism of five vertical support approaches and five horizontal support approaches. (2). The LCP policy can significantly reduce CI in the construction industry.

The study not only explored how is the LCP policy implemented, but also examined the effectiveness of the LCP policy in the construction industry. The policy implications of this study can help policy-makers better achieve low-carbon development targets in the construction industry.