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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.

The main purpose of this study is to investigate the impact of housing price on mortgage debt accumulation while considering the structural break effects associated with the Global Financial Crisis (GFC).

To determine the existence of a long run relationship among the variables, this study used a Johansen cointegration test. The long run model was then estimated using the fully modified ordinary least square method and reported for both the model with and without a structural break associated with the GFC.

The findings demonstrate a moderate positive relationship between housing price and mortgage debt, with the impact of the GFC is positive but insignificant. The household’s lack of responsiveness to the GFC may be attributed to their optimistic expectations and confidence in the Malaysian housing market.

Findings of this study provide some guidance to policymakers and the banking sector in predicting household borrowing behavior during future economic crises.

The increase in housing prices and mortgage debt after the GFC has been a concern for many countries, including Malaysia. This study contributes to the literature by investigating the relationship between housing prices and mortgage debt in Malaysia and sheds light on the impact of the GFC on household borrowing behavior. The study’s contributions include providing new evidence to the underexplored topic, enhancing the robustness and reliability of the empirical results and providing insights into the importance of testing for structural breaks in time series analysis.

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.

The objective is to assess the relationship between financial inclusion and bank profitability in emerging economies, i.e. “Bangladesh, Egypt, Indonesia, Mexico, Nigeria, Pakistan, Philippines, and Vietnam”.

The second-generation econometrics of panel data has been applied to examine the cross-section independence and control the heterogeneity between cross sections. Additionally, the authors employ the following tests for the analysis: “the unit root test, Westerlund's (2007) bootstrap cointegration, Pedroni cointegration, fully modified ordinary least square (FMOLS), and heterogeneous panel causality techniques”. The annual data consist of the period from 2000 to 2019.

The findings reveal that financial inclusion fosters bank profitability. Therefore, easier access to financial services and products will maximize banks' profitability. Additionally, the association between financial inclusion and bank profitability is unidirectional.

This research is a first attempt to bring a novel contribution to the subject of emerging economies by investigating the association between financial inclusion and bank profitability. Another unique addition to the literature is the use of a novel financial inclusion index. At last, a panel cointegration technique, FMOLS and heterogeneous panel non-causality tests are taken into consideration for the in-depth analysis.

Within an effort of European Union (EU) policy to achieve carbon-neutral agriculture, the present study intends to explore the impact of carbon emissions generated by different sources related to agriculture namely energy used in farming, by enteric fermentation and by fertilizers on agricultural income in 25 countries from EU.

In order to evaluate the environmental – economic performance linkage for EU agriculture, we employ a couple of different widely used panel unit root tests explicitly Levin, Li and Chu, Im, Pesaran and Shin, ADF and PP Fisher Chi-square test cointegration test (Pedroni and Kao cointegration tests) and model estimation methodologies namely the FMOLS and DOLS and ARDL – PMG models.

All the cointegration techniques employed namely Pedroni, Kao test and Johansen Pesaran cointegration tests validate the existence of long run relationships. The most significant finding is the model estimation based on three different methodologies namely FMOLS, DOLS and ARDL/PMG models. No convergence in the results was found by different estimation models. For the short term coefficients and more specifically for the case of carbon emissions generated by energy the impact on agricultural income seems to be decreasing with a decreasing trend, a result that validates the little effort made by farmers to limit carbon emissions along with the limited efficacy of the implementing policy. The same findings are valid for the first two estimation models while for the case of the third model the reversed relationship is validated. For the carbon emissions generated by enteric fermentation, the inverted-U pattern is validated with DOLS and ARDL/PMG model while for the case of fertilizers only the third model confirms the validity of inverted-U- pattern.

Based on the obtained empirical results, a list of policy implications is unveiled with multiple impacts on the strategy and practices adopted by farmers in order for the objective of eco efficieny to be achieved.

The conducted research is focusing on the environmental – economic performance linkages for EU agriculture and examines the role of agri – environmental policy in the evolution of the particular relationship for different sources of environmental pollution in agricultural activity.

The main purpose of this study is to explore the presence of the EKC hypothesis in emerging economies. Additionally, the present study also explores the existence of the “resource curse hypothesis” (RCH), and the causal relationship among the variables that are considered for testing the presence of EKC and RCH hypothesis for a panel of selected emerging economies for the time period between 1990 and 2014.

The authors performed unit root test followed by cointegration test to test the existence of cointegrating relationship among the variables. Dynamic ordinary least square (DOLS) and fully modified ordinary least square (FMOLS) methods are used to obtain long-run estimates of considered variables, and the Granger causality test is performed to test the directional causality.

The long-run estimates obtained from DOLS and FMOLS techniques support the presence of the EKC (inverted U-shape) and the RCH.

To the best of the authors’ knowledge, the present work is the pioneer study for EKC and RCH investigation in the context of emerging economies. The policy implication is that these economies should look forward to drafting new policies to reduce environmental degradation and promote sustainable development.

The purpose of this paper is to investigate the possible co-integration and the direction of causality between financial development and economic growth in South-Asian Association for Regional Cooperation (SAARC) countries using annual data from 1994 to 2013.

The Carrion-i-Silvestre et al. (2005) stationarity test with structural breaks is used to check the stationarity. The Westerlund (2006) panel co-integration test is employed to examine the long-run relationship among the variables. To carry out tests on the co-integrating vectors, fully modified ordinary least squares (FMOLS) and PDOLS techniques are used and panel Granger causality test is used to examine the direction of the causality.

The Westerlund (2006) panel co-integration test confirms the existence of the long-run relationship between financial development and economic growth for SAARC countries. The coefficients of FMOLS and DOLS indicate that index of financial development (IFD) and trade openness supports economic growth in SAARC region. In the short-run, there is unidirectional causality running from IFD to economic growth.

In the view of these findings it is recommended that countries in the region should adopt policies geared toward financial sector development to attain high economic growth.

To the best of the author’s knowledge, no studies have looked into SAARC countries to study the relationship between financial development and economic growth, this study is the first of its kind.

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.

The purpose of this paper is to investigate the macroeconomic determinants of foreign direct investment (FDI) for the top five South Asian economies, namely, Bangladesh, India, Pakistan, Sri Lanka, and Nepal, and to examine whether these factors are the same for each.

This study employs fully modified ordinary least squares and two-stage least squares estimation methods.

This study shows that South Asian economies have a number of FDI determinants in common. For example, market size and human capital are the two most common factors attracting FDI in each country (except for Nepal, which revealed a negative correlation between FDI and market size). Other factors, such as infrastructure, domestic investment, lending rates, exchange rates, inflation, financial stability/crisis, and stock turnover entered into regression with both positive and negative signs, thereby indicating that the underlying theories on FDI do not provide a clear prediction of the direction of the effect of a particular variable on FDI.

This paper studied the effects of demand-side factors on FDI. A comparative study of the supply-side factors may add further knowledge.

This paper provides evidence to show that the determinants of FDI are indeed country-specific. Thus, to design a suitable FDI policy, it would not be wise to solely rely on other economies’ FDI experiences.

This paper provides updated evidence on factors that are essential to promoting or deterring FDI in South Asian economies.

The persistent rise in the global discharges of carbon (CO₂) emissions and the likely undesirable consequences of this practice on the global atmosphere attracts the attention of policy makers and researchers to argue on the causes and perpetrators of CO₂ emissions at international level. The purpose of this paper is to examine the relationship between economic growth, energy production, capital formation, foreign direct investment (FDI) and CO₂ emissions in the LMI and Middle East and North African (MENA) countries for the period 1980–2011.

Two separate autoregressive distributed lag (ARDL) models were estimated for both the LMI and MENA countries, for the period 1980–2011. Furthermore, a fully modified OLS (FMOLS) was estimated for the two regions over the same period.

The results indicated that for the lower-middle income countries, there is a positive significant relationship between energy production and CO₂ emissions. In the long run while in the short run, FDI and EGP are positively related to CO₂ emissions while gross capital formation (GCF) has a negative impact on the CO₂ emissions in the short run over the same period. Similarly, for the MENA countries, there is a positive relationship between EGP, GCF and CO₂ emissions in both the short run and the long run. Furthermore, the estimated group mean FMOLS indicated that apart from GDP, all other variables have significant positive impact on CO₂ emissions.

The study covers only the period 1980–2011. This was because of limited available data during the study.

The study recommended the adoption of green technology by FDI firms and also in the process of energy production such as in crude oil production.

The study carried out a complex analysis where simultaneously all the countries of LMI and MENA regions where considered. Furthermore, separate analysis where conducted for each of the LMI and MENA regions using ARDL model. Variable representing energy production was included in the analysis which was not considered by previous studies. Lastly, FMOLS was estimated for the pooled of LMI and MENA countries which further distinguished the study from the relevant previous studies.