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Classical unit root tests are known to suffer from potentially crippling size distortions, and a range of procedures have been proposed to attenuate this problem, including the use of bootstrap procedures. It is also known that the estimating equation’s functional form can affect the outcome of the test, and various model selection procedures have been proposed to overcome this limitation. In this chapter, the authors adopt a model averaging procedure to deal with model uncertainty at the testing stage. In addition, the authors leverage an automatic model-free dependent bootstrap procedure where the null is imposed by simple differencing (the block length is automatically determined using recent developments for bootstrapping dependent processes). Monte Carlo simulations indicate that this approach exhibits the lowest size distortions among its peers in settings that confound existing approaches, while it has superior power relative to those peers whose size distortions do not preclude their general use. The proposed approach is fully automatic, and there are no nuisance parameters that have to be set by the user, which ought to appeal to practitioners.

The modernization of the agro-based industry has encouraged the application of inorganic fertilizers to increase productivity. However, such fertilizer emissions may pose harmful environmental effects in the long run. This study aims to empirically explore the matter by applying the environmental Kuznets curve (EKC) hypothesis in the Indian agro-based industry.

The study builds two models considering nitrous oxide emission levels from inorganic (synthetic) and organic (manure) fertilizers to evaluate the safer option for the environment. The validity of an industry-specific EKC (IEKC) is tested for the models considering time series data from 1975 to 2019. Here, the autoregressive distributed lag model is applied for the 45 years long time series analysis to test the hypothesis with respect to inorganic and organic fertilizers emissions.

The existence of the IEKC is rejected by the inorganic fertilizer emissions model. Its U-shaped curve implies that applying such fertilizers will gradually cause degrading environmental effects. On the other hand, the organic fertilizer emissions model supports the existence of an inverted U-shaped IEKC. It proves that organic fertilizers are a better choice for safeguarding the environment in the long run.

Applying the EKC hypothesis on an industrial level can signify whether an industry worsens the environment in the long run. However, very few studies have explored such an application of the hypothesis in the past. Moreover, the literature could not find any previous study exploring the environmental effects of inorganic and organic fertilizers by analyzing the EKC hypothesis. The hypothesis can offer such insights with simplified empirical assessment.

Tourism–economy relationship in India has been studied extensively in the past literature using a single equation approach. However, the present paper diverted from this trend and examined the tourism–economy relationship using the specific to general modelling approach over the 1990–2018 time period. The study also accounts for the influence of merchandise trade, capital formation, foreign investment inflows and inflation on economic growth to achieve the robustness of the coefficient estimates.

To achieve the objective, the study utilised a specific to general modelling strategy. First, the regression equation includes only three core variables: gross domestic product (GDP), international tourist receipts and international tourist expenditures. Next, the authors include other control variables in the regression equation one by one, leading us to test five model types for investigating the cointegration among the variables. As for the estimation technique, the authors employed autoregressive distributed lag (ARDL) approach.

The paper's findings highlight that tourism receipts and expenditures exert a positively significant impact on economic growth. Moreover, including the additional independent variables does not substantially change the tourism and economic growth relationship. The existence of one-way causality from tourism expenditures to economic growth supports the tourism-led growth hypothesis. These findings highlight the rationale for intervention by the government and policymakers to promote tourism potential and facilities to accelerate the overall growth performance of the country. While the existence of one-way causal effect from economic growth to tourism revenues supports the growth-led tourism development hypothesis, implying that economic expansion is necessary for tourism development.

This research article tried to present a comprehensive picture of India's tourism–economy relationship. However, the present study is organised as an aggregate economy-level analysis. It assumed that the aggregate tourism sector is homogenous. However, different tourism sectors exert different levels of influence on the economy. The authors expect future research can take the disaggregated analysis of the tourism–economy relationship.

This study provides valuable insights into the tourism-led growth hypothesis in India. The study highlights comprehensive intervention by the government and policymakers for accelerating tourism development to invigorate the overall growth performance of the country over the long run. The principal recommendation emerging from the present research is that the tourism growth potential can be depended upon to stimulate the economic performance of the Indian economy.

The present study diverted from the previous empirical studies by following a specific to general modelling strategy. First, the regression model includes only three core variables such as economic growth, tourism receipts and tourism expenditure. Next, the authors include other control variables in the regression equation one by one, leading us to test five model types for investigating the cointegrating relationship among the variables. GDP growth rate is used as a dependent variable in all five specifications. The idea is to expand the model to capture every feature of the data generating process.

This study aims to predict stock market crashes identified by the CMAX approach (current index level relative to historical maximum) during periods of global and local events, namely the subprime crisis of 2008, the political and social instability of 2011 and the COVID-19 pandemic.

Over the period 2004–2020, a log-periodic power law model (LPPL) has been employed which describes the price dynamics preceding the beginning dates of the crisis. In order to adjust the LPPL model, the Global Search algorithm was developed using the “fmincon” function.

By minimizing the sum of square errors between the observed logarithmic indices and the LPPL predicted values, the authors find that the estimated parameters satisfy all the constraints imposed in the literature. Moreover, the adjustment line of the LPPL models to the logarithms of the indices closely corresponds to the observed trend of the logarithms of the indices, which was overall bullish before the crashes. The most predicted dates correspond to the start dates of the stock market crashes identified by the CMAX approach. Therefore, the forecasted stock market crashes are the results of the bursting of speculative bubbles and, consequently, of the price deviation from their fundamental values.

The adoption of the LPPL model might be very beneficial for financial market participants in reducing their financial crash risk exposure and managing their equity portfolio risk.

This study differs from previous research in several ways. First of all, to the best of the authors' knowledge, the authors' paper is among the first to show stock market crises detection and prediction, specifically in African countries, since they generate recessionary economic and social dynamics on a large extent and on multiple regional and global scales. Second, in this manuscript, the authors employ the LPPL model, which can expect the most probable day of the beginning of the crash by analyzing excessive stock price volatility.

Tourism development is critical for economic transformation, particularly in emerging economies. However, the growing spate of terrorism dissuades international tourists, reduces tourism receipts and ultimately hampers the tourism sector's performance. Thus, the government intervenes by altering its military spending to curtail terrorism. Against this backdrop, this study examines the moderating role of military spending in the terrorism–tourism nexus in Nigeria.

The study employs the dynamic ordinary least squares (DOLS) to investigate the moderating role of military spending in the terrorism–tourism nexus in Nigeria. The authors employ the data that cover the period 1995Q1–2019Q4.

The results reveal that terrorism has a catastrophic effect on tourism arrivals in Nigeria while military spending has a positive impact on tourism arrivals. The results further show the moderating role of military spending in the terrorism–tourism nexus is positive and statistically significant. However, the findings are subject to the measures of military spending, terrorism and tourism.

The practical implication of the findings is the need for deliberate and strategic budgeting for the Ministry of Defence to combat terrorism, which should not only focus on the procurement of arms and ammunition but also cover the welfare of the military personnel. Nigeria also needs to formulate and implement necessary tourism policies aimed at countering terrorism in a bid to create and maintain a positive image on the global tourist map.

Many studies, particularly in developing countries like Nigeria, had examined the effect of terrorism on tourism but none has examined the moderating role of military spending in the terrorism–tourism nexus. Hence, this study examines the moderating role of military spending in the relationship between terrorism and tourism in Nigeria, a terrorism-prone country with several tourist sites.

This study aims to examine the price and income elasticities of short- and long-run industrial electricity demand in Brazil between 2003 and 2020. The research also examines the impacts of COVID-19 in Brazil’s industrial electricity sector, including an analysis in states more and less industrialized.

Dynamic adjustments models in panel data are used to present robust estimates and analyze the impact of different methodologies on reported elasticities.

The short-run price elasticity is estimated at −0.448, while the long-run values are around −1.60. Regarding income elasticity, the value is 0.069 in the short-run and is concentrated in 0.25 in the long-run. The inelastic results of income show that the industrial demand for electric energy follows the trend of loss of competitiveness of the Brazilian industry in the past years. In addition, the price of natural gas, the level of employment, and, in specific cases, the level of imports also influence industrial electricity demand.

The research is a pioneer in the investigation of the industrial behavior of electricity of the Brazilian industrial branch, using as control variables, the average temperature, and the level of rainfall, this one, so important for a country whose main source is hydroelectric. In addition, to the best of the authors’ knowledge, it is the first study, which is prepared to analyze the effects of COVID-19 on electric consumption in the industrial sector, investigating these impacts, including in the states considered more and less industrialized. The estimates generated may help in the design of the Brazilian energy policy.

Most emerging economies have made conscious efforts through policy initiatives to attract foreign direct investment (FDI). However, a significant obstacle to FDI inflow has been the prevalence of corruption in the host country. This study, therefore, aims to examine whether there is an optimum corruption value that results in threshold effects of corruption on FDI.

To achieve this objective, this study used Hansen’s (1999) panel threshold regression (PTR) model by using a panel data of 30 sub-Saharan African (SSA) countries from 2000 to 2021.

This study finds that the nexus between corruption and FDI has a single threshold effect, with a 5.37% optimum corruption threshold value. At this threshold value, corruption affects FDI negatively. Any corruption value that is below the threshold value also elicits a negative corruption–FDI relationship. Despite having a negative relationship when the corruption value is above the optimum corruption threshold, it is not statistically significant.

The implication of the results is that it is deleterious to use corrupt practices to draw FDI to SSA nations.

To the best of the authors’ knowledge, this study is one of the first in the corruption–FDI nexus literature to use Hansen’s PTR model to estimate an optimal corruption threshold. The authors recommend that policymakers in the selected SSA countries reconsider the use of corruption to attract FDI because there is an optimal corruption threshold that could impact FDI in the host country.

Given the ever-growing fiscal commitments of Nigeria and her chequered history of electricity generation and distribution, the fortunes of the energy sector in the country have been affected by the prevalence of energy poverty. Government policies such as public capital expenditure (PCE) present a crucial option for reducing energy poverty in Nigeria, providing the purpose of this study.

To investigate the relationship between government capital spending and five distinct energy poverty proxies, this research applies the Bayer–Hanck cointegration system and the auto-regressive distributed lag (ARDL) bound test.

The findings indicate that public capital spending in Nigeria worsens energy poverty by reducing access to electricity, urban electrification, renewable energy consumption and renewable electricity generation, with a positive but insignificant influence on rural electrification.

This inquiry presents a pioneering investigation of the nexus between PCE and energy poverty in Nigeria. Also, aside from the variables of energy poverty adopted by existing studies, this study incorporates renewable energy consumption and renewable electricity output with implications for energy poverty and sustainable development.

The purpose of this study is to empirically investigate the asymmetric effects of financial risk on Sukuk market development for a sample of Malaysian countries over the period of 2010–2021.

This study refers to the International Country Risk Guide (ICRG) in determining the financial risk factors to be studied in addition to the Malaysia financial stress index (FSI) to capture changes in financial risk level. The authors use the nonlinear autoregressive distributed lag (NARDL) model to tackle the nonlinear relationships between identified financial risk variables and Sukuk market development.

The results suggest the existence of a long-run relationship between foreign debt service stability, international liquidity stability (ILS), exchange rate stability (ERS) and financial stress level with the Sukuk market development in Malaysia. Indeed, higher ILS and ERS will boost Sukuk market size, whereas higher foreign debt services and financial stress are negatively related to Sukuk market development. Findings also indicate that the long-run positive and negative impacts of identified financial risk components on Sukuk market development are statistically different. Taking into account the role of the Sukuk market in facilitating Malaysia’s economic growth, the country should aim to keep the foreign debt-to-GDP ratio at a sustainable level.

This study points to three possible directions for future research. The first is the differential impact of financial risk components on Sukuk issuance for different Sukuk structures. As more data becomes available in the future, this area could be further explored by conducting the above analysis for different combinations of Sukuk structures and currency denominations. In addition, future researchers could also consider exploring the variability of financial risk impacts through comparative studies of the leading Sukuk-issuing countries to account for differences in regulatory frameworks and supporting infrastructure.

This study provides valuable practical and policy implications for strengthening the growth of the Sukuk market. While benefiting from the diversification benefits of funding sources to finance private or government projects and developments, Malaysia should remain vigilant to global economic conditions, foreign exchange markets and financial stress levels, as all of these factors may significantly influence investor sentiment and the rate of return offered by Sukuk issuance.

The use of the NARDL approach, which investigates the long-run effects of financial risk factors on Sukuk market development in Malaysia, makes this study a valuable addition to the literature, as there has been little research into the asymmetric effects of those variables on Sukuk market development using samples from emerging Asian markets.

The purpose of the paper is to explore the long-run impact of board governance and bank performance on executive remuneration. More specifically, the study addresses two objectives. First, the authors investigate the long-run relationship between pay and performance hold for the Indian banking industry. Second, the authors explore the moderating role of the board in explaining the relationship between executive pay and performance.

The study uses multivariate panel co-integration approaches, i.e. fully modified and dynamic ordinary least square, to explain the co-integrating relationship between executive pay, governance and performance of Indian banks. The analysis is conducted for the period from 2005 to 2018.

The results of co-integration tests reveal a long-run relationship between executive pay, board governance and bank performance. The long-run estimates produce evidence in favour of the dynamic agency theory, suggesting that the implications of asymmetric information can be mitigated by associating the current executive pay with the bank performance in the previous periods. The finding of this study reveals that improvements in the board quality serve as a monitoring tool to constrain excessive pay and moderate the executives’ pay. Furthermore, the interaction of performance and board governance negatively impacts pay, supporting a substitution approach. It implies that setting optimal pay packages for executives necessitates enhanced and efficient board governance practices.

The study recommends significant policy implications for regulators and the board of directors that executive pay significantly responds to the bank’s performance and good board governance practices in the long run.

This paper provides novel evidence of long-run pay-performance-governance relation using a panel co-integration approach.