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This paper studies the dynamics and elasticities of house prices in Spain and Portugal (Iberia) at the Metropolitan Statistical Area (MSA) level, addressing panel regression problems such as heterogeneity and cross-sectional dependence between MSA.

The authors develop a two steps study. First, five distinct estimation methodologies are applied to estimate the long-term house price equilibrium of the Iberian MSA house market: Mean Group (MG), Fully Modified Ordinary Least Square (FMOLS) MG (FMOLS-MG), FMOLS Augmented MG (FMOLS-AMG), Common Correlated Effects MG (CCEMG) and Dynamic CCEMG (DCCEMG). FMOLS-AMG is found to be the best estimator for the long-term model. Second, an additional five distinct estimation methodologies are applied to estimate the short-term house price dynamics using the long-term FMOLS-AMG estimated price in the error-correction term of the short-term dynamic house price model: OLS Fixed Effects (FE), OLS Random Effects (RE), MG, CCEMG and DCCEMG. DCCEMG is found to be the best estimator for the short-term model.

The results show that in the long run Iberian house prices are inelastic to aggregate income (0.227). This is a much lower elasticity than what was previously found in US MSA house price studies, suggesting that there are other factors explaining Iberian house prices. According to our study, coastal MSA presents an inelastic housing supply and a price to income elasticity close to one, whereas inland MSA are shown to have an elastic supply and a non-significant price to income elasticity. Spatial differences are important and cross-section dependence is prevalent, affecting estimates in conventional methodologies that do not account for these limitations, such as OLS-FE and OLS-RE. Momentum and mean reversion are the main determinants of short-term dynamics.

Recent econometric advances that account for slope heterogeneity and cross-section dependence produce more accurate estimates than conventional panel estimation methodologies. The results suggest that house markets should be analyzed at the metropolitan level, not at the national level and that there are significant differences between short-term and long-term house price determinants.

To the best of the authors' knowledge, this is the first study applying recent econometric advances to the Iberian MSA house market.

This chapter uses fully modified OLS principles to develop new methods for estimating and testing hypotheses for cointegrating vectors in dynamic panels in a manner that is consistent with the degree of cross sectional heterogeneity that has been permitted in recent panel unit root and panel cointegration studies. The asymptotic properties of various estimators are compared based on pooling along the ‘within’ and ‘between’ dimensions of the panel. By using Monte Carlo simulations to study the small sample properties, the group mean estimator is shown to behave well even in relatively small samples under a variety of scenarios.

In this chapter, we study the asymptotic distributions for ordinary least squares (OLS), fully modified OLS (FMOLS), and dynamic OLS (DOLS) estimators in cointegrated regression models in panel data. We show that the OLS, FMOLS, and DOLS estimators are all asymptotically normally distributed. However, the asymptotic distribution of the OLS estimator is shown to have a non-zero mean. Monte Carlo results illustrate the sampling behavior of the proposed estimators and show that (1) the OLS estimator has a non-negligible bias in finite samples, (2) the FMOLS estimator does not improve over the OLS estimator in general, and (3) the DOLS outperforms both the OLS and FMOLS estimators.

Researchers have long been interested in testing the validity of Okun’s law due to its macroeconomic policy implications. However, most of the studies have focused on testing the law using aggregate data on unemployment and output. In recent times, attention has been shifted to testing the law at the sectoral level. In light of this, the purpose of this study is to examine the response of unemployment to sectoral outputs in Nigeria using the data that covers a period from 1981-2020.

To test the validity of Okun’s law at the sectoral level, both difference and gap methods of specifying Okun’s law are used. Furthermore, the author also uses a series of estimation methods, which include ordinary least squares (OLS), dynamic OLS (DOLS), fully modified OLS (FMOLS) and canonical cointegration regression (CCR).

The results, based on the difference model, are mixed irrespective of estimation and data filter methods. For the gap model, Okun’s law holds for all sectors irrespective of estimation techniques (especially DOLS, FMOLS and CCR) when the Hodrick–Prescott filter method is used to filter data. However, the author discovers that the coefficients of Okun’s law vary across the sectors as the response of unemployment to services sector output is greater than the rest of the sectors. When the Hamilton filter method is used to filter data, the results appear to be mixed across the sectors. The results are almost ditto when all the sectoral variables are put in one model.

To the best of the author’s knowledge, this is the first study that investigates the validity of sectoral Okun’s law in Nigeria, the leading economy in Africa.

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 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 development of green economy is of academic and policy importance to governments and policymakers worldwide. In the light of the necessity of renewable energy to sustain green economic growth, this study aims to examine the relationship between renewable energy consumption and green economic growth, controlling for the impact of trade openness for Organization for Economic Co-operation and Development countries over the period 1990-2015, within a multivariate panel data framework.

To investigate the long-run relationship between variables, panel cointegration tests are performed. Panel Granger causality based on vector error correction models is adopted to understand the short- and long-run dynamics of the data. Furthermore, ordinary least square (OLS), dynamic OLS and fully modified OLS methods are used to confirm the long-run elasticity of green growth for renewable energy consumption and trade openness. Moreover, system generalized method of moment is applied to eliminate serial correlation, heteroscedasticity and endogeneity problems. The authors used the panel Granger causality test developed by Dumitrescu and Hurlin (2012) to infer the directionality of the causal relationship, allowing for both the cross-sectional dependence and heterogeneity.

The results suggest that renewable energy consumption and trade openness exert positive effects on green economic growth. The results of long-run estimates of green economic growth reveal that the long-run elasticity of green economic growth for trade openness is much greater than for renewable energy consumption. The estimated results of the Dumitrescu and Hurlin (2012) test reveal bidirectional causality between green economic growth and renewable energy consumption, providing support for the feedback hypothesis.

This paper provides strong evidence of the contribution of renewable energy consumption on green economy for a wide range of countries. Despite the costs of establishing renewable energy facilities, it is evident that these facilities contribute to the green growth of an economy. Governments and public authorities should promote the consumption of renewable energy and should have a support policy to promote an active renewable energy market. Furthermore, the regulators must constitute an efficient regulatory framework to favor the renewable energy consumption.

Many countries focus on increasing their GDP without taking the environmental impacts of the growth process into account. This paper shows that renewable energy consumption points to the fact that countries can still increase their economic growth with minimal damage to environment. Despite the costs of adopting renewable energy technologies, there is still room for economic growth.

This paper provides evidence on the contribution of renewable energy consumption on green economic growth for a wide range of countries. The paper focuses on the impact of renewable energy on economic growth by taking environmental degradation into consideration on a wide scale of countries.

This study aims to explore the impact of oil prices on housing prices using Pakistani annual data from 1973 to 2021.

The Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests were used for unit-root testing, whereas the johansen-juselius test was used for cointegration. For the short-run, the error correction model is used and the robustness of the model is checked using the dynamic ordinary least squares (DOLS) and fully modified OLS (FMOLS). The cumulative sum (CUSUM) and CUSUM of Squares tests were used to check the stability of the model, while parameter instability was confirmed by the Chow breakpoint test. Finally, the impulse response function was used for causality.

According to the findings, rising oil prices, among other things, have an impact on housing prices. Inflation is the single most important factor affecting not only the housing sector but also the entire economy. Lending and exchange rates have a significant impact on housing prices as well. The FMOLS and DOLS results suggest that the OLS results are robust. According to the variance decomposition model, housing prices and oil prices are bidirectionally related. The Government of Pakistan must develop a housing policy on a regular basis to develop the country’s urban housing supply and demand.

It is suggested that in Pakistan, the rising oil prices is a problem for the housing prices as well as many other sectors. The government needs to explore alternative ways of energy generation rather than the heavy reliance on imported oil.

Pakistan has been experiencing rising oil prices and housing prices with the rapid urbanisation and rural–urban migration. The contribution to the literature is that neither attempt (as to the best of the authors’ knowledge) has been made to check the impact of rising oil prices on housing sector development in Pakistan.

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.