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This paper aims to make an attempt to identify the determinants of dividend policy by analyzing 125 real estate companies, which are selected on the basis of consistent dividend distribution throughout the study period. Most of these companies either listed with Bombay Stock Exchange or National Stock Exchange.

This paper applies three alternative methods to verify and validate the results obtained from each other method, namely, fully modified ordinary least square (FMOLS), dynamic ordinary least square and generalized method of moments (GMM). Data collected of the selected companies’ post-recession period i.e. 2009-2017. The selected companies have age either 5 years old or more when data are retrieved from the above-mentioned sources. Due to much volatility in the recession period in the real estate firms at the global level, no data have been taken of the firms before March 2009. Moreover, for arriving at good analysis and an adequate number of observations for the study more recent data have been taken.

Empirical findings of this research paper depict that firm previous dividend, firm risk and liquidity are strong predictors of future dividend payout ratios (DPRs). The results indicate that firm risk as measured through price-earnings ratio (PE ratio) has a positive association with a DPR of selected real estate firms. Lagged DPR used in the GMM test as an exogenous variable is showing positive significant association with DPR. Firm’s growth is found significant in FMOLS and GMM techniques. On the other firm’s size is found significant according to cointegration techniques.

The present study shall be useful to different stakeholders of real estate companies. Various significant determinants as identified can be used by management for designing optimum dividend policy and providing maximum benefits to existing shareholders. Similarly existing and prospective shareholders may predict the future payment of dividend and accordingly they may take investment decisions in these firms, as the future fund’s requirement of a firm depends upon dividend payment and retention ratio.

As per the authors’ knowledge, there is no single study carried in the post-recession period to predict determinants of dividend policy of real estate sector using three alternatives of methods to verify and validate the results obtained from each other method. The study is carried out after exploring determinant from a diverse range of period of studies (oldest one to latest one).

The purpose of this paper is to empirically examine the effectiveness of foreign aid in improving economic growth prospects in the South Asian region from 1996 to 2017.

A sample of eight South Asian countries for the period 1996–2017 is being considered for this study. This study uses various econometrics tools such as Pedroni and Johansen–Fisher panel cointegration test, panel fully modified ordinary least square and panel dynamic ordinary least square (PDOLS) to ascertain the long-run and short-run dynamics among the variables under consideration.

The empirical results found that long-run, as well as the short-run relationship, exist among foreign aid, economic growth, investment, financial deepening, price stability and trade openness of the South Asian economies. The authors also found unidirectional causality running from foreign aid to economic growth. Both the long-run relationship as well as short-run causality between foreign aid and economic growth is unequivocally positive.

This study uses a dynamic macroeconomic modeling framework to assess the impact of aid flows on economic growth in South Asian economies. Taking into account the diversity of level of growth experienced by the eight countries in the Asian region, this study uses an appropriate regression technique, i.e. PDOLS whose results are robust. Therefore, the policymakers in these countries are well-advised to implement suitable policy measures to ensure optimum utilization of foreign capital resources garnered by way of receipt of foreign aid and build on for stronger future economic growth.

This paper examines the impact of political instability on the investors' behavior, measured by Google search queries, and on the dynamics of stock market returns.

First, by using the DCC-GARCH model, the authors examine the effect of investor sentiment on the Tunisian stock market return. Second, the authors employ the fully modified dynamic ordinary least square method (FMOL) to estimate the long-term relationship between investor sentiment and Tunisian stock market return. Finally, the authors use the wavelet coherence model to test the co-movement between investor sentiment measured by Google Trends and Tunisian stock market return.

Using the dynamic conditional correlation (DCC), the authors find that Google search queries index has the ability to reflect political events especially the Tunisian revolution. In addition, empirical results of fully modified ordinary least square (FMOLS) method reveal that Google search queries index has a slightly higher effect on Tunindex return after the Tunisian revolution than before this revolution. Furthermore, by employing wavelet coherence model, the authors find strong comovement between Google search queries index and return index during the period of the Tunisian revolution political instability. Moreover, in the frequency domain, strong coherence can be found in less than four months and in 16–32 months during the Tunisian revolution which show that the Google search queries measure was leading over Tunindex return. In fact, wavelet coherence analysis confirms the result of DCC that Google search queries index has the ability to detect the behavior of Tunisian investors especially during the period of political instability.

This study provides empirical evidence to portfolio managers that may use Google search queries index as a robust measure of investor's sentiment to select a suitable investment and to make an optimal investments decisions.

The important research question of how political instability affects stock market dynamics has been neglected by scholars. This paper attempts principally to fill this void by investigating the time-varying interactions between market returns, volatility and Google search based index, especially during Tunisian revolution.

This research investigates the bond between stock market development and agricultural growth in African emerging economies from 1990 to 2020.

Agricultural value added to the gross domestic product measures agricultural growth and market capitalization and stock value traded measure stock market development.

The findings disclose that market capitalization negatively affects agricultural growth while stock value traded positively affects agricultural growth in the fully modified and dynamic ordinary least square techniques. The findings unveil bidirectional causality between labour and agricultural value added with unidirectional causality flow from agricultural value added to market capitalization and stock value traded.

The governments should promote agricultural growth initiatives which stimulate stock market development. Effective methods required to encourage credit flow to the agricultural enterprises through the stock markets' intermediation should be promoted using aggressive policies which eliminate credit flow bottlenecks. Policy makers and regulatory authorities should implement policies which attract investors to the agricultural sector and encourage companies' listing in the stock markets. The capital market funding should be expanded to boost economic growth through agricultural value added.

Literature reveals divergent results on the relationship between stock market development and agricultural growth. Earlier studies provide conflicting findings on the bond between stock market development and agricultural growth. Some findings indicate positive link between stock market development and agricultural growth, while others show a negative association. Studies' results reveal opposing directions of causality between stock market development and agricultural growth.

Africa's population is the second largest and fastest growing in the world after Asia, and this puts African governments under great stress in terms of increased public expenditure and is faced with a low revenue generation. Hence, the need for this study. The purpose of this paper is to examine the socio-economic determinants of public expenditure in Africa by assessing the influence of population age structure using a sample of the top ten most populous countries in Africa covering period of 1989 to 2018.

The study employed panel fully modified ordinary least square (OLS) in estimating the relevant relationship between the variables in the model. The dynamic ordinary least square (DOLS) model was also used to check the robustness of the fully modified ordinary least square (FMOLS) results.

The findings revealed that the major population age structure that influences the growth of public expenditure in Africa are population ages (0–14) and population ages (15–64), but the former poses a stronger significant influence than the latter while population ages (65 and above) has a negative and insignificant influence. Also, in terms of other socio-economic factors, self-employment has a reducing and significant influence on public expenditure. GDP per capita has a negative and insignificant influence while foreign aid and unemployment rate has an increasing influence. Finally, inflation rate and control of corruption (CC) has a negative relationship with public expenditure.

The study argues that an increase in the young and working population will put enormous pressure on the government in the provision of more jobs and other public infrastructures such as health care and education. In the context of African economy with a low revenue generation, public expenditure will be low and the desperately poor masses will be denied of these public infrastructures.

Several studies (Jibir and Aluthge, 2019; Tayeh and Mustafa, 2011; Okafor and Eiya, 2011; Obeng and Sakyi, 2017; Ofori-Abebrese, 2012) have investigated the determinants of public expenditure using total population as a variable. However, this study is unique as it focused on the influence of population age structure on public expenditure in Africa. Also, the study incorporated other socio-economic determinants of public expenditure such as self-employment, standard of living, inflation rate, unemployment rate, foreign aid and corruption in its analytical model. To the best of our knowledge, some of these variables have not been employed in previous studies.

The purpose of this research is to examine the validity of the agriculture-induced environmental Kuznets curve (EKC) hypothesis with evidence from an autoregressive distributed lag (ARDL) approach with a structural break including real income and energy consumption in the model for Ghana over the period 1980–2014.

The ARDL approach with a structural break was used to analyze the agriculture-induced EKC model which has not been studied in Ghana. The dynamic ordinary least squares (DOLS), canonical cointegration regression (CCR) and fully modified ordinary least squares (FMOLS) econometric methods were further used to validate the robustness of the estimates, and the direction of the relationship between the study variables was also clarified using the Toda–Yamamoto Granger causality test.

The ARDL results revealed that GDP, energy consumption and agricultural value added have significant positive effects on CO₂ emissions, while GDP² reduces CO₂ emissions. The Toda-Yamamoto causality test results show a bidirectional causality running from GDP and energy consumption to CO₂ emissions whereas a unidirectional long-term causality runs from GDP² and agriculture value-added to CO₂ emissions.

This finding validated the presence of the agriculture-induced EKC hypothesis in Ghana in both the short run and long run, and the important role of agriculture and energy consumption in economic growth was confirmed by the respective bidirectional and unidirectional causal relationships between the two variables and GDP. Thus, a reduction in unsustainable agricultural practices is recommended through specific policies to strengthen institutional quality in Ghana for a paradigm shift from rudimentary technology to modern sustainable agrarian technologies.

This study is novel in the EKC literature in Ghana, as no study has yet been done on agriculture-induced EKC in Ghana, and the other EKC studies also failed to account for structural breaks which have been done by this study. This study further includes a causality analysis to examine the direction of the relationship which the few EKC studies in Ghana failed to address. Finally, dynamic ordinary least squares (DOLS), canonical cointegration regression (CCR) and fully modified ordinary least squares (FMOLS) methods are used for robustness check, unlike other studies with single methodologies.

The present study aims to examine the moderating impact of governance quality on the tourism poverty nexus using a panel of six South Asian Association for Regional Cooperation (SAARC) countries during the period 2002 to 2019.

For the soundness of the results, fully modified ordinary least square (FMOLS) and dynamic ordinary least square (DOLS) econometric models were applied to determine the long-run relationship.

The findings confirmed the positive and significant impact of tourism development (international tourism arrival) and governance quality (effectiveness of governmental services) on poverty (per capita household consumption) reduction. Interestingly results confirm that governance quality and tourism development have complementary impacts on poverty reduction.

The present study has twofold contributions; First, despite the high potential of SAARC tourism, research remains limited in studies examining the role of tourism and governance quality on poverty reduction within the SAARC region. As a result, the present paper presents critical insights into the impact of tourism inflow and governance quality on poverty reduction in South Asian countries. Second, to the best of the author's knowledge, this is the first attempt to conduct an econometric analysis to examine the role of governance quality on the relationship between tourism inflow and poverty reduction in SAARC countries.

The purpose of this paper is to introduce an empirical model for house price spillovers between real estate markets. The model is presented by using data from the US-UK and London-New York housing markets over a period of 1975Q1-2016Q1 by employing both static and dynamic methodologies.

The research analyzes long-run static and dynamic spillover elasticity coefficients by employing three methods, namely, autoregressive distributed lag, the fully modified ordinary least square and dynamic ordinary least squares estimator under a Kalman filter approach. The empirical method also investigates dynamic correlation between the house prices by employing the dynamic control correlation method.

The paper shows how a dynamic spillover pricing analysis can be applied between real estate markets. On the empirical side, the results show that country-level causality in housing prices is running from the USA to UK, whereas city-level causality is running from London to New York. The model outcomes suggest that real estate portfolios involving US and UK assets require a dynamic risk management approach.

One of the findings is that the dynamic conditional correlation between the US and the UK housing prices is broken during the crisis period. The paper does not discuss the reasons for that break, which requires further empirical tests by applying Markov switching regime shifts. The timing of the causality between the house prices is not empirically tested. It can be examined empirically by applying methods such as wavelets.

The authors observed a unidirectional causality from London to New York house prices, which is opposite to the aggregate country-level causality direction. This supports London’s specific power in the real estate markets. London has a leading role in the global urban economies residential housing markets and the behavior of its housing prices has a statistically significant causality impact on the house prices of New York City.

The house price co-integration observed in this research at both country and city levels should be interpreted as a continuity of real estate and financial integration in practice.

The paper is the first research which applies a dynamic spillover analysis to examine the causality between housing prices in real estate markets. It also provides a long-term empirical evidence for a dynamic causal relationship for the global housing markets.

This paper aims to assess the moderating role of foreign direct investment (FDI) on the effect of economic complexity on carbon emissions, considering other drivers of carbon emissions such as renewable energy use and economic growth, using data set spanning between 1990 and 2018 in BRICS nations.

This research aims to fill the gap in ongoing literature. Cross-sectional IPS and cross-sectional augmented Dickey–Fuller tests, fully modified ordinary least square, dynamic ordinary least square, fixed effect ordinary least square, Westerlund cointegration and method of moments quantile regression (MMQR) econometric approaches are applied.

The Westerlund cointegration outcomes disclosed long-run interconnectedness between carbon emissions and its drivers. Furthermore, MMQR outcomes disclosed that in each tail (0.1–0.90), economic growth and economic complexity contribute to upsurge in carbon emissions while in each quantile (0.1–0.90) renewable energy abate carbon emissions. Furthermore, we affirmed the pollution-haven and environmental Kuznets curve hypotheses across all quantiles (0.1–0.90). Finally, at all quantiles (0.1–0.90), the joint effect of both FDI inflows and economic complexity reduced carbon emissions. Furthermore, the panel causality outcomes disclosed that all the exogenous variables can predict carbon emissions. Based on the findings, BRICS nation’s policymakers should place a greater emphasis on FDI inflows because it aids in abating environmental degradation.

To the best of the authors’ knowledge, this is the first research to test the moderating role of FDI on the effect of economic complexity on carbon emissions. Hence, this research aims to fill the gap in ongoing literature.

The purpose of this study is to investigate the impact of foreign capital inflows and economic growth on stock market capitalization in Pakistan by using the annual time series data from the period of 1976 to 2011.

The autoregressive distributed lag bound testing cointegration approach, the error correction model and the rolling window estimation procedures have been performed to analyze the long run, short run and behavior of coefficients, respectively.

Results indicate that foreign direct investment (FDI), workers’ remittances and economic growth have significant positive relationship with the stock market capitalization in long run as well as in short run. Results of the dynamic ordinary least square and the fully modified ordinary least square suggest that the initial results of long-run coefficients are robust. Results of variance decomposition test show the bidirectional causal relationship of FDI and economic growth with stock market capitalization. However, unidirectional causal relationship is found in between workers’ remittances and stock market capitalization.

It is suggested that in Pakistan, investors can make their investment decisions through keeping an eye on the direction of the considered foreign capital inflows and economic growth.

This paper makes a unique contribution to the literature with reference to Pakistan, being a pioneering attempt to investigate the effects of foreign capital inflows and economic growth on stock market by using long time series data and applying more rigorous techniques.