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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.

The purpose of this study is to examine two issues, namely the degree of current account deficit (CAD) sustainability and the degree of capital mobility.

The sample for this study comprises 24 Latin American and Caribbean countries, including three regional agreements: Andean Community, MERCOSUR (Mercado Común del Sur), and SICA (Central American Integration System). This study employs the dynamic common correlated effects mean group (DCCEMG) estimator in a panel data set to investigate the long-run relationship between savings and investment along with short-run dynamics.

The findings indicate that CAD is weakly sustainable in the Latin American and Caribbean region, MERCOSUR, and SICA, while CAD is strongly unsustainable in the Andean Community. The sub-period analysis reveals that CAD has been adversely affected by the 2008 crisis. However, in the post-crisis period, CAD has been slowly decreasing in the Latin American and Caribbean region and Andean Community, whereas CAD has continued increasing in MERCOSUR and SICA. Further, the estimates of error-correction terms and short-run coefficients indicate that the Andean Community and MERCOSUR observe a higher degree of long-run and short-run capital mobility than SICA.

The results carry fundamental implications for policy-making processes aimed at maintaining sustainable CADs.

This study gives an alternative interpretation of the “Feldstein-Horioka” coefficient in terms of CAD sustainability and analyses the saving–investment relationship in light of Chudik and Pesaran (2015).

Czechia's economic growth is substantially dependent on foreign trade. An independent monetary policy in a managed floating exchange rate regime gives a unique perspective on the effects of the exchange rate on foreign trade. This chapter evaluates the effects of exchange rate development on different sectors of Czechia's foreign trade. Using disaggregated data based on trading partner and product category, the period from 1999 to 2020 is analyzed. Czechia's 10 major trading partners are included in the estimation. The relationship between exchange rates and foreign trade is assessed through a Johansen cointegration approach and modified vector error correction model. The results of the Johansen cointegration test indicate that the majority of the aggregate bilateral trade balances are in a long-term relationship with Czechia's gross domestic product (GDP), foreign GDP and exchange rate movements. The J-curve is proved only in chemicals and related products traded with France, manufactured goods traded with Italy and Slovakia and mineral fuels and lubricants traded with the Netherlands.

The study aims to estimate the short- and long-run effects of monetary policy on residential property prices in South Africa. Over the past decades, there has been a monetary policy shift, from targeting money supply and exchange rate to inflation. The shifts have affected residential property market dynamics.

The Johansen cointegration approach was used to estimate the effects of changes in monetary policy proxies on residential property prices using quarterly data from 1980 to 2022.

Mortgage finance and economic growth have a significant positive long-run effect on residential property prices. The consumer price index, the inflation targeting framework, interest rates and exchange rates have a significant negative long-run effect on residential property prices. The Granger causality test has depicted that exchange rate significantly influences residential property prices in the short run, and interest rates, inflation targeting framework, gross domestic product, money supply consumer price index and exchange rate can quickly return to equilibrium when they are in disequilibrium.

There are limited arguments whether the inflation targeting monetary policy framework in South Africa has prevented residential property market boom and bust scenarios. The study has found that the implementation of inflation targeting framework has successfully reduced booms in residential property prices in South Africa.

Foreign direct investment in the real estate (FDIRE) sector is required to bridge the gap between investment needed and domestic funds. Further, foreign direct investment is gaining importance because other sources of raising finance such as External Commercial Borrowing and foreign currency convertible bonds have been banned in the Indian real estate sector. Therefore, the objective of the study is to explore the determinants attracting foreign direct investment in real estate and to assess the impact of those variables on foreign direct investments in real estate.

Johansen cointegration test, vector error correction model along with variance decomposition and impulse response function are employed to understand the nexus of the relationship between various macroeconomic variables and foreign direct investment in real estate.

The results indicate that infrastructure, GDP and tourism act as drivers of foreign direct investment in real estate. However, interest rates act as a barrier.

This article aimed at exploring factors attracting FDIRE along with estimating the impact of identified variables on FDI in real estate. Unlike other studies, this study considers FDI in real estate instead of foreign real estate investments.

Climate change-related weather events significantly affect rice production. In this paper, we investigate the impact of and interrelationships between agriculture inputs, climate change factors and financial variables on rice production in India from 1970–2021.

This study is based on the time series analysis; the unit root test has been employed to unveil the integration order. Further, the study used various econometric techniques, including vector autoregression estimates (VAR), cointegration test, autoregressive distributed lag (ARDL) model and diagnostic test for ARDL, fully modified least squares (FMOLS), canonical cointegrating regression (CCR), impulse response functions (IRF) and the variance decomposition method (VDM) to validate the long- and short-term impacts of climate change on rice production in India of the scrutinized variables.

The study's findings revealed that the rice area, precipitation and maximum temperature have a significant and positive impact on rice production in the short run. In the long run, rice area (ß = 1.162), pesticide consumption (ß = 0.089) and domestic credit to private sector (ß = 0.068) have a positive and significant impact on rice production. The results show that minimum temperature and direct institutional credit for agriculture have a significant but negative impact on rice production in the short run. Minimum temperature, pesticide consumption, domestic credit to the private sector and direct institutional credit for agriculture have a negative and significant impact on rice production in the long run.

The present study makes valuable and original contributions to the literature by examining the short- and long-term impacts of climate change on rice production in India over 1970–2021. To the best of the authors’ knowledge, The majority of the studies examined the impact of climate change on rice production with the consideration of only “mean temperature” as one of the climatic variables, while in the present study, the authors have considered both minimum as well as maximum temperature. Furthermore, the authors also considered the financial variables in the model.

This paper aims to examine the causation linking financial technology to economic growth in the East African Community states from 1997 to 2019.

Autoregressive distributed lag is used. Gross domestic product per capita proxies economic growth, automated teller machines, point of sale, debit card ownership and mobile banking measure financial technology.

The results unveil a significant relationship between financial technology and economic growth. The findings show bidirectional causality between automated teller machine and economic growth, with unidirectional causation from economic growth to point of sales and internet banking, mobile banking and government effectiveness to economic growth. The error correction term is negatively significant, demonstrating a long-term convergence between Fintech measures and economic growth.

The governments should effectively enact and implement policies that protect investments in financial technologies to boost economic growth in the East African Community countries. The government should reduce taxes on financial technology equipment and related services. The use of automated teller machine, debit card ownership and internet banking should be encouraged through cashless transactions. Financial institutions should adopt cashless operation policies to encourage the use of financial technologies.

Research results on the bond between financial technology and economic growth are not conclusive. These studies demonstrate that technological innovations are double edged-swords, with both positive and negative sides. The results are conflicting; some reveal positive relationships, while others show negative links. Hence, research is required to fill the lacuna.

This study aims to examine the impact of some real variables such as real effective exchange rates, real mortgage rates, real money supply, real construction cost index and housing sales on the real housing prices.

This study uses a nonlinear autoregressive distributed lag (NARDL) model in the monthly period of 2010:1–2021:10.

The real effective exchange rate has a positive and symmetric effect. The decreasing effect of negative changes in real money supply on real housing prices is higher than the increasing effect of positive changes. Only positive changes in the real construction cost index have an increasing and statistically significant effect on real house prices, while only negative changes in housing sales have a small negative sign and a small increasing effect on housing prices. The fact that the positive and negative changes in real mortgage rates are negative and positive, respectively, indicates that both have a reducing effect on real housing prices.

This study suggests the first NARDL model that investigates the asymmetric effects on real housing prices instead of nominal housing prices for Turkey. In addition, the study is the first, to the best of the authors’ knowledge, to examine the effects of the five real variables on real housing prices.

This study investigates the correlation between economic development and scientific knowledge production indicators in the BRICS countries from 2000 to 2020, highlighting the importance of human resources, natural resources, and innovation. Addressing a gap in the existing literature, this study aims to contribute significantly to understanding this relationship.

Employing a descriptive statistical approach, this study utilizes GDP and per capita income as economic indicators and scientific data from WoS and SCOPUS databases, focusing on scientific document production and citations per document.

The analysis reveals a strong correlation between economic development and scientific performance within the BRICS nations during the specified period. It emphasizes the interdependence of economic progress and scientific prowess, underscoring that they cannot be considered independently.

However, limitations exist, notably the reliance on specific databases that might not cover the entire scientific output and the inability to capture all factors influencing economic and scientific development.

Understanding this interdependence has crucial originality. Policymakers and stakeholders in BRICS countries can leverage these insights to prioritize investments in human capital development and scientific research. This approach can foster sustainable economic growth by reducing reliance on natural resources.

The purpose of this paper is to design a new trajectory error compensation method to improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction motion.

A trajectory error compensation method based on admittance-extended Kalman filter (AEKF) error fusion for human–exoskeleton interaction control. The admittance controller is used to calculate the trajectory error adjustment through the feedback human–exoskeleton interaction force, and the actual trajectory error is obtained through the encoder feedback of exoskeleton and the designed trajectory. By using the fusion and prediction characteristics of EKF, the calculated trajectory error adjustment and the actual error are fused to obtain a new trajectory error compensation, which is feedback to the knee exoskeleton controller. This method is designed to be capable of improving the trajectory tracking performance of the knee exoskeleton and enhancing the compliance of knee exoskeleton interaction.

Six volunteers conducted comparative experiments on four different motion frequencies. The experimental results show that this method can effectively improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction.

The AEKF method first uses the data fusion idea to fuse the estimated error with measurement errors, obtaining more accurate trajectory error compensation for the knee exoskeleton motion control. This work provides great benefits for the trajectory tracking performance and compliance of lower limb exoskeletons in human–exoskeleton interaction movements.