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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.

In this paper we examine the validity of the J-curve hypothesis in four Southeast Asian economies (Indonesia, Malaysia, the Philippines and Thailand) over the 1980–2017 period.

We employ the linear autoregressive distributed lags (ARDL) model that captures the dynamic relationships between the variables and additionally use the nonlinear ARDL model that considers the asymmetric effects of the real exchange rate changes.

The estimated models were diagnostically sound, and the variables were found to be cointegrated. However, with the exception of Malaysia, the short- and long-run relationships did not attest to the presence of the J-curve effect. The trade flows were affected asymmetrically in Malaysia and the Philippines, suggesting the appropriateness of nonlinear ARDL in these countries.

The previous research tended to examine the effects of the real exchange rate changes on the agricultural trade balance and specifically the J-curve effect (deterioration of the trade balance followed by its improvement) in the developed economies and rarely in the developing ones. In this paper, we address this omission.

This study aims to construct alternative models to establish the dynamic relationship between exchange rates and housing affordability by estimating both the short- and long-run relationship between exchange rates and housing affordability for 18 OECD countries from 1975Q1 to 2022Q4. After that, this study demonstrates how this nexus behaves during high and low inflation regimes and turbulent times.

This study uses the panel autoregressive distributed lag technique to examine the nexus between housing affordability to capture the distinct characteristics of the sample countries and estimate various short- and long-run dynamics in the relationship between housing affordability and exchange rate.

Exchange rate appreciation improves housing affordability in the short run, whereas this connection tends to dissipate in the long run. Moreover, inflation can worsen housing affordability during turbulent times, such as the global financial crisis, in both the short and long run. Ignoring these changes in the relationship between exchange rates and housing affordability during turbulent times can lead to incorrect conclusions.

To the best of the authors’ knowledge, this study is the first to examine the association between exchange rates and housing affordability by demonstrating how these variables behave in high and low inflation regimes and turbulent times.

The COVID-19 pandemic and the onset of the conflict in Ukraine led to a sustained downturn in tourist arrivals (TA) in Russia. This paper aims to explore the influence of geopolitical risk (GPR) and other indices on TA over 1995–2023.

We employ a nonlinear autoregressive distributed lag (NARDL) model to analyze the effects, capturing both the positive and negative shocks of these variables on TA.

Our research demonstrates that the NARDL model is more effective in elucidating the complex dynamics between macroeconomic factors and TA. Both an increase and a decrease in GPR lead to an increase in TA. A 1% negative shock in GPR leads to an increase in TA by 1.68%, whereas a 1% positive shock in GPR also leads to an increase in TA by 0.5%. In other words, despite the increase in GPR, the number of tourists coming to Russia increases by 0.5% for every 1% increase in that risk. Several explanations could account for this phenomenon: (1) risk-tolerant tourists: some tourists might be less sensitive to GPR or they might find the associated risks acceptable; (2) economic incentives: increased risk might lead to a depreciation in the local currency and lower costs, making travel to Russia more affordable for international tourists; (3) niche tourism: some tourists might be attracted to destinations experiencing turmoil, either for the thrill or to gain firsthand experience of the situation; (4) lagged effects: there might be a time lag between the increase in risk and the actual impact on tourist behavior, meaning the effects might be observed differently over a longer period.

Our study, employing the NARDL model and utilizing a dataset spanning from 1995 to 2023, investigates the impact of GPR, gross domestic product (GDP), real effective exchange rate (REER) and economic policy uncertainty (EPU) on TA in Russia. This research is unique because the dataset was compiled by the authors. The results show a complex relationship between GPR and TA, indicating that factors influencing TA can be multifaceted and not always intuitive.

In Africa, recent data show that Nigeria is the second top remittance recipient behind Egypt, but welfare seems deteriorating. Most related reviewed literature is micro-based with surveys, giving credence to the dearth of macro-based literature whose gap this study attempted to fill. Thus, the main purpose of this study is to examine remittance flows and its welfare implications in Nigeria.

The study used quarterly data (1980Q1–2020Q4) from World Development Indicators (2020) and applied the dynamic ordinary least squares (DOLS) model.

Remittance flows were found to be significantly improving the welfare of Nigerians by about 0.04% for a percentage remittance increase. Financial sector development results show that while loans decrease welfare per individual significantly by 0.25% given a 1% increase in the loans accessible by the private sector, a percentage increase in broad money supply in circulation raises welfare per individual significantly by about 0.43%.

Since remittance is found to improve welfare, the study recommends that relevant stakeholders should endeavor to eliminate all form of bottlenecks (payment delays, remitting costs, transfer delays, poor policies and policy inconsistencies) inherent in remitting funds back to Nigeria. The implication of this is that if the impediments are minimized, remittances are bound to rise which will ultimately lead to improved welfare.

The existing literature revealed that there exists very limited or no macro-based study in this context, hence this novelty study.

This study aims to explore the determinants of public debt in selected South Asian Association for Regional Cooperation (SAARC) countries for 19 years, from 2001 to 2019.

Using ordinary fixed and random effect models, the authors examine the role of internal and external factors in determining the composition of public debt. Furthermore, for robustness, they compare the results with two-stage least square (2SLS) regression estimates after considering the problem of endogeneity, overidentification, under-identification and weak instruments.

The findings show that among the selected macroeconomic variables, inflation, exchange rate and broad money have significant negative effects on the debt-GDP ratio. In contrast, military spending, corruption and interest rates appear to positively influence the same as per 2SLS results. From the policymaking perspective, SAARC countries should focus more on reducing military spending and make a concerted effort to augment investments in productive projects. Further, with strong fiscal consolidation and institutional quality, it is important to mitigate the frequent occurrence of corruption conundrums in emerging economies for the development of a transparent economic system.

The study is distinct from previous studies in two ways. First, to the best of the authors’ knowledge, there are no studies focusing on SAARC countries in the context of public debt. Second, the study expands the existing literature on public debt by taking into account both external and internal debts to decipher the within-country and cross-country determinants of debt accumulation. More specifically, this model considers accountability and transparency in the public sector, cross-border security challenges and benefits of globalization by including explanatory variables such as corruption, military expenditure spending and capital inflows.

Many tourism academics have investigated the linkages between tourism, power and space; few have specifically addressed the profound links between tourism and geopolitics. In view of the restrictive assumptions of the linear framework used in the earlier studies, and hidden asymmetries present in the time series data. Against this backdrop, the study tries to find out how tourists may respond differently to favourable and unfavourable shocks in geopolitical risk (GPR).

In order to capture this asymmetric nature of the problem, the study employs the non-linear autoregressive distributed lag (NARDL) model to evaluate data from 2001Q1 to 2019Q4.

The results show that both positive and negative shock to GPR does not produce results of equal magnitude. A positive shock to GPR has a more detrimental effect on foreign tourist arrivals (FTA) than a beneficial effect a negative shock produces. Besides this, the present study also looks at the effect of other macro-economic variables on FTA. An ascend in the real effective exchange rate (REER) i.e. appreciation of the domestic currency has an unfavourable impact on foreign visitor arrivals, while an increase in world gross domestic product amplify it. The results of the study are robust to alternative measures of the control variable.

The study is significant for policymakers in understanding the short and long-run implications of GPR on FTA in India. The present study can assist policymakers, and destination managers to manage the external and internal risks and minimise the consequences of geopolitical threats on the Indian tourism industry. Consequently, destination managers can utilise the study's findings in calibrating their operations and designing crisis marketing strategies within the geopolitical dynamics of the Indian state.

The study tries to find out how tourists may exhibit distinct reactions to positive and negative disturbances in GPR. The study provides first-hand evidence of how GPR impacts tourism demand. The paper also includes the existing body of literature related to GPR factors and their effect on tourist influx, specifically in the framework of the Indian tourism sector.

Our study aims to evaluate the impact of infrastructure and public investment on private investment in machinery and equipment in Brazil from 1947 to 2017. The contribution of our article to the existing literature lies in providing a more comprehensive understanding of the presence or absence of the crowding effect in the Brazilian economy by leveraging an extensive historical database. Our central argument posits that the recent decline in private capital accumulation over the last few decades can be attributed to shifts in economic policies – moving from a developmentalist orientation to nondevelopmental guidance since the early 1990s, which is reflected in the diminished levels of public investment and infrastructure since the 1980s.

We conducted a series of econometric regressions utilizing the autoregressive distributed lag (ARDL) model as our chosen econometric methodology.

Employing two different variables to measure public investment and infrastructure, our results – robust across various specifications – have substantiated the existence of a crowding-in effect in Brazil over the examined period. Thus, we have empirical evidence indicating that the state has influenced private capital accumulation in the Brazilian economy over the past decades.

Our article contributes to the existing literature by offering a more comprehensive understanding of the crowding effect in the Brazilian economy, utilizing an extensive historical database.

The purpose of this study is to analyze the nonlinear relationship between electricity consumption (EC) and electricity transmission losses (ETL) in Ghana. Also, we examined how ETL moderate the effect of EC on economic growth in Ghana from 1980 to 2021.

We used timeseries data from 1980 to 2021 within an autoregressive distributed lag framework to analyze the links among ETL, EC and economic growth in Ghana.

Findings show the existence of an asymmetric long-run relationship between EC and ETL. Also, the negative effects of ETL on EC are bigger in the long run. In addition, ETL and EC combine to reduce economic growth, in the long run, providing evidence for the energy-led growth theory in Ghana. Population and inflation were also found to have a significant effect on economic growth in Ghana.

We examined the nonlinear nexus of EC and ETL, which extant studies have ignored in discussing the link between EC and economic growth. Again, we showed that ETL reduces EC causing a reduction in economic growth.

Growing food insecurity is a leading cause of fatalities, particularly in developing nations like Sub-Saharan Africa and Southeast Asia. However, the rising energy consumption and carbon dioxide (CO2) emissions are mostly associated with food production. Balancing the trade-offs between energy intensity and food security remains a top priority for environmentalists. Despite the critical role of the environment in food security, there is a scarcity of substantial studies that explore the statistical connections among food security, CO2 emissions, energy intensity, foreign direct investment (FDI) and per capita income. Therefore, this study aims to provide more precise and consistent estimates of per capita CO2 emissions by considering the interplay of food security and energy intensity within the context of emerging economies.

To examine the long-term relationships between CO2 emissions, food security, energy efficiency, FDI and economic development in emerging economies, this study employs correlated panel-corrected standard error, regression with Newey–West standard error and regression with Driscoll–Kraay standard error models (XTSCC). The analysis utilizes data spanning from 1980 to 2018 and encompasses 32 emerging economies.

The study reveals that increasing food security in a developing economy has a substantial positive impact on both CO2 emissions and energy intensity. Each model, on average, demonstrates that a 1 percent improvement in food security results in a 32% increase in CO2 levels. Moreover, the data align with the Environmental Kuznets Curve (EKC) theory, as it indicates a positive correlation between gross domestic product (GDP) in developing nations and CO2 emissions. Finally, all experiments consistently demonstrate a robust correlation between the Food Security Index (FSI), energy intensity level (EIL) and exchange rate (EXR) in developing markets and CO2 emissions. This suggests that these factors significantly contribute to environmental performance in these countries.

This study introduces novelty by employing diverse techniques to uncover the mixed findings regarding the relationship between CO2 emissions and economic expansion. Additionally, it integrates energy intensity and food security into a new model. Moreover, the study contributes to the literature by advocating for a sustainable development goal (SDG)-oriented policy framework that considers all variables influencing economic growth.