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This study aims to investigate the effect of monetary policy on housing prices for US economy. It specifically examines whether nominal or real interest rates are the key drivers behind fluctuations in housing prices in US.

Monthly data from January 1991 to July 2023 and various appropriate analytical tools such as unit root tests, Johansen’s cointegration test, vector error correction model (VECM), impulse response function and Granger causality test were applied for the data analysis.

The Johansen cointegration findings reveal the presence of a long-term relationship among the variables. VECM results indicate a negative correlation between nominal and real interest rates and housing prices in both the short and long terms, suggesting that a strict monetary policy can help in controlling the housing price increase in the USA. However, housing prices are more responsive to changes in nominal interest rates than to real interest rates. Additionally, the study reveals that the COVID-19 pandemic contributed to the upsurge in housing prices in the USA.

This study contributes by examining the role that nominal or real interest rates play in shaping housing prices in the USA. Moreover, given the recent significant upsurge in housing prices, this study presents a unique opportunity to investigate whether these price increases are influenced by the Federal Reserve's monetary policy decisions regarding nominal or real interest rates. Additionally, using monthly data, this study provides a deeper understanding of the fluctuations in housing prices and their connection to monetary policy tools.

This study aims to shed light on the experience of the United Arab Emirates (UAE) in balancing three main pillars: the environmental criteria, the reduction of CO₂ emissions and the economic growth. Based on the environmental Kuznets curve (EKC) framework, it will assess the causal relationship between economic indicators such as gross domestic product (GDP) per capita, trade openness and energy use and environmental indicators such as CO₂ emissions.

The analysis relies on a period of 40 years (1981–2020) where data is extracted from the World Bank database. This study uses the unit root test for time series stationarity, the optimal lag length test, the “Johansen” test for co-integration and the vector error correction model.

The paper concludes to two major findings. On a short-term basis, CO₂ emissions and economic indicators are negatively correlated, whereas on a long-term basis, there is no association between CO₂ emissions and economic indicators in the UAE.

The research ends with important recommendations. It illustrates the importance of rationalizing the use of primary resources and the necessity to embrace successful and efficient policies in the energy production.

More specifically, UAE is urged to address the problem of CO₂ emissions in the electricity sector and increase awareness of the use of environmentally friendly processes in the transport and industrial sectors. While setting their economic agendas, UAE are encouraged to meet environmental criteria and invest in renewable energy projects such as “Shams 1”, the largest solar power plant outside of Spain and the USA.

The current study is significant in its research on the environmental impact of economic development, trade openness and energy use policies in the UAE. It uses CO₂ emissions as an environmental proxy and evaluates the environmental policies adopted in the UAE to reduce its impact.

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.

In the current competitive era of industrialization, a significant level of innovation is necessary to meet the growing competition. There are many economic forces that determine the pace of innovation within a country. Among others, this study aims to focus on exploring the relevant role of corruption control (CC) in determining the innovation level.

For empirical analysis, the authors sample the 24 years of data (1996–2019) of Asian economies and use the fully modified ordinary least square (OLS) and dynamic OLS models to check the regression among variables. The selection of both techniques is based upon the empirical suggestions offered by unit root testing and the Johansen cointegration test.

The empirical findings infer the positive and statistically significant role of CC in boosting innovation. Strengthening the corruption-free environment encourages innovation activities within the country. In addition, foreign direct investment has a negative relationship with CC while financial development, economic growth, export volume and government subsidies positively determine the innovation level.

Based on empirical analysis, it is suggested that the policy officials should do more focus on CC to enhance the competitiveness of the country through more innovation.

The empirical analysis robust the findings of existing literature in an alternative data set and offers innovative views regarding the role of other factors in boosting the innovation level.

This study probes the psychological interplay between investor sentiment and the returns of cryptocurrencies Bitcoin and Ethereum. Employing the Granger causality test, the authors aim to gauge how extensively the Fear and Greed Index (FGI) can predict cryptocurrency return movements, exploring the intricate bond between investor emotions and market behavior.

The authors used the Granger causality test to achieve research objectives. Going beyond conventional linear analysis, the authors applied Smooth Quantile Regression, scrutinizing weekly data from July 2022 to June 2023 for Bitcoin and Ethereum. The study focus was to determine if the FGI, an indicator of investor sentiment, predicts shifts in cryptocurrency returns.

The study findings underscore the profound psychological sway within cryptocurrency markets. The FGI notably predicts the returns of Bitcoin and Ethereum, underscoring the lasting connection between investor emotions and market behavior. An intriguing feedback loop between the FGI and cryptocurrency returns was identified, accentuating emotions' persistent role in shaping market dynamics. While associations between sentiment and returns were observed at specific lag periods, the nonlinear Granger causality test didn't statistically support nonlinear causality. This suggests linear interactions predominantly govern variable relationships. Cointegration tests highlighted a stable, enduring link between the returns of Bitcoin, Ethereum and the FGI over the long term.

Despite valuable insights, it's crucial to acknowledge our nonlinear analysis's sensitivity to methodological choices. Specifics of time series data and the chosen time frame may have influenced outcomes. Additionally, direct exploration of macroeconomic and geopolitical factors was absent, signaling opportunities for future research.

This study enriches theoretical understanding by illuminating causal dynamics between investor sentiment and cryptocurrency returns. Its significance lies in spotlighting the pivotal role of investor sentiment in shaping cryptocurrency market behavior. It emphasizes the importance of considering this factor when navigating investment decisions in a highly volatile, dynamic market environment.

This paper aims to empirically examine the impact of economic development and energy consumption in Saudi Arabia (the leading OPEC giant and the Arab energy icon country) between 1971 and 2015, whilst incorporating globalization, financial development and capital accumulation.

This study uses econometric tools and the analytical framework based on the autoregressive distributed lag (ARDL) model.

The study found that, unlike economic development, globalization and financial development increased energy consumption. Also, capital accumulation created a boost in the country’s energy consumption. Results of variance decomposition indicate that the innovative shocks in globalization and financial development affected energy consumption at the rates of 15.28% and 28.98%, respectively, over 15 years’ period, while shocks in capital accumulation affected energy consumption at a rate of only about 1.24%. In addition, the results of impulse response function show that globalization and economic development were highly responsive to shocks in financial development, and capital accumulation greatly spurred financial development.

The findings of this study have implication for promoting an efficient and sustainable energy systems that enhance sustainable development based on the accrued benefits of globalization, financial development and capital accumulation.

Given the increasing level of globalization, financial development and energy consumption, our study uses econometric tools and the analytical framework based on the ARDL model to revisit how energy consumption is influenced by economic development in Saudi Arabia by incorporating other determinants of energy consumption such as globalization, financial development and capital accumulation. The results were validated based on the innovative accounting.

In developing countries, banks play a major role by acting as a conduit for the effective mobilization of funds from the surplus sectors of an economy for onward lending to the deficit sectors for productive investments that will in turn increase the level of employment and economic growth. There has being a rising trend in unemployment rate in Nigeria and South Africa and hence, the need for the study to assess the effectiveness of banking system credit in curbing unemployment rate by making a comparative analysis of Nigeria and South Africa covering the period of 1991–2018.

The study employed the unit root test, Johansen cointegration test, vector error correction model and VAR impulse response function in determining the relationship between the variables.

The major findings revealed that banking system credit matters in curbing unemployment rate in South Africa than in Nigeria. Also, other macroeconomic factors such as lending rate, inflation rate, Government expenditure and population growth were significant enough in influencing unemployment rate in South Africa than in Nigeria. Foreign direct investment was a significant factor in reducing unemployment rate in Nigeria than in South Africa. The cointegration test showed a long-term relationship between the variables in both countries while the speed of adjustment coefficient of the vector error correction model is faster in South Africa than in Nigeria.

Previous empirical studies on the relationship between banking system credit and unemployment rate have focused much on other regions such as Asia and Europe. Thus, the study is unique as it focused on the African region and also made a comparative analysis by testing the Keynesian theory of employment, interest and money on two emerging African economies which are Nigeria and South Africa.

This study aims to examine the share of foreign direct investment (FDI) in creating the value added (VA) of innovative and other industries in Poland in 2004–2020.

In terms of the empirical analysis of FDI stocks, their locations were divided into innovative and other industries. The differences in the creation of VA are presented by domestic and foreign enterprises. The impact of FDI stocks in individual industries on gross domestic product (GDP) changes was assessed using the vector error correction model (VECM).

FDI from innovative industries generated approx. 7% VA of the Polish economy in the years 2004–2020. In 2009–2018, the share of VA of foreign enterprises in innovative industries in Poland showed a faster growth (by 5 pp) than in other industries. The results of decomposition confirm that the level of explanation of GDP by FDI in innovative industries is higher than in other industries.

Changes in the classification of activities reduce the time series period available.

This study explains the participation of foreign and domestic enterprises in creating VA. The results are useful to pursuing the national investment policy.

The economic results of domestic and foreign enterprises in the host country affect the economic growth and development and ultimately the socio-economic conditions of life.

This work provides some additional explanations for the inconclusive results of international research into the impact of FDI on GDP or the spillovers effects. Its usefulness concerns the detailed impact of FDI by industrial structures on GDP.

Qatar, a wealthy country with an open economy has limited arable land. To meet its domestic food demand, the country heavily relies on food imports. Additionally, the over three year-long economic embargo enforced by regional neighbors and the covariate shock of the COVID-19 pandemic have demonstrated the country's vulnerability to food insecurity and potential for structural breaks in macroeconomic data. The purpose of this paper is to examine short- and long-run determinants of Qatar's imports of aggregate food, meats, dairy and cereals in the presence of structural breaks.

The authors use 24 years of food imports, gross domestic product (GDP) and consumer price index (CPI) data obtained from Qatar's Planning and Statistics Authority. They use the autoregressive distributed lag (ARDL) cointegration framework and Chambers and Pope's exact nonlinear aggregation approach.

Unit root tests in the presence of structural breaks reveal a mixture of I (1) and I (0) variables for which standard cointegration techniques do not apply. The authors found evidence of a significant long-run relationship between structural changes and food imports in Qatar. Impulse response functions indicate full adjustments within three-quarters of a year in the event of an exogenous shock to imports.

An exogenous shock of one standard deviation on this variable would reduce Qatar's food imports by about 2.5% during the first period but recover after the third period.

The failure of past aggregate food demand studies to go beyond standard unit root testing creates considerable doubt about the accuracy of their elasticity estimates. The authors avoid that to provide more credible findings.

The purpose of the paper is twofold: first, to observe the relationship between sale to list ratio (SLR)/ for-sale inventory (FSI)/ sale count nowcast (SCN) and real/nominal housing value, and second, to produce a handbook of empirical evidence that can serve as a foundation for future research on this topic.

This paper broadly compiles empirical evidence, using three of the most common causality tests in the field of housing economics. The analysis methods include lagged Pearson correlation test, Granger causality test and cointegration test.

Causal relationships were observed between SLR/FSI/SCN and both nominal and real housing values. SLR and SCN showed positive long-term correlations with housing value, whereas FSI had a negative correlation. Adjusting the housing value with the Consumer Price Index (CPI) to derive real housing values could potentially alter the direction of the causal relationships. It is crucial to distinguish the long-term relationship from temporal dynamics, as FSI displayed a positive immediate impulse–response relationship with nominal housing price despite the negative long-term correlation.

SLR/FSI/SCN are housing market parameters that have only recently begun to be documented and have seen little use in research. So far, housing market research has revolved around traditional macroeconomic indicators such as unemployment rate. To the best of the authors’ knowledge, this study is one of the first studies that introduce these three parameters into housing market research.