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This study aims to examine the relationship between the environment and tourism flows, as well as the economic variables of the 32 states of Mexico for the period 1999–2019 based on data availability. The related literature studying tourism and environmental impacts is scarce at a national level, with most of them being local case studies. Some international studies find that if the relationship exists, it is weak or nonexistent, using CO₂ as a proxy in most cases.

The present study uses panel data and cointegration panel methodologies, while also using geographic information systems to observe the distribution of variables at a state level between tourism and environmental variables.

The findings of the study are as follows: state gross domestic product, the inertia of environmental variables (i.e. volume of water treatment and solid waste), occupied rooms (proxy variable for tourism activity) and average temperature have an impact on the contemporary evolution of environmental variables; national and international tourist variables have no impact on the environment; the panels are integrated in such a way that there is a long-term equilibrium between states and some environmental care variables; and no conclusive evidence is found regarding the impact of tourism activity on the considered environmental variables.

The main limitations and areas of opportunity of the work refer to the amount of data available over time and the precision of the measurement of the variables. The availability, temporality and frequency of the data are also limitations of the research. An example of this is the nonexistence of CO₂ emissions at the state level. Additionally, studying other countries and regions for which there are limitations of data and applied studies is also a challenge.

The results are important for economies (in growth) and societies whose economic growth depends on tourism flows and have done little to reverse the damage that tourism has on the environment.

The models can contribute to study the relation between tourism and environmental variables and could be extended to regions, states and provinces for decision-making on actions to be taken for the present and future.

The originality of the research is innovative for the region: Mexico, Central and Latin America. There are no works that have studied these problems with this methodology and these variables. In terms of originality, the classic models of panel data and cointegration of panel data are useful and easily replicable for others to use for different countries. The results are relevant because there is apparently no relationship between tourism and some environmental variables in the short run, but there exists a weak and strong long-run relation between some of them.

本研究采用面板数据和协整面板模型方法, 同时利用地理信息系统(gis)观察州一级层面旅游和环境方面的变量分布。

本研究根据数据可用性, 研究了墨西哥32个州1999–2019年期间环境与旅游流量及经济变量之间的关系。在国家层面上研究旅游与环境影响的相关文献很少, 而且大多是地方的个案研究。一些国际研究发现, 即使有这种关系, 大多数案例中使用二氧化碳作为替代变量, 这种关系也是很弱或不存在。

i)国家国内生产总值, 环境变量的惯性(即水处理量和固体废物量), 占用的房间(旅游活动的代理变量)和平均温度对环境变量的现有演化有影响。ii)国内和国际旅游变量对环境没有影响。iii)面板数据以这样一种方式集成, 即国家和一些环境变量之间存在一种长期平衡。iv)关于旅游活动对所考虑的环境变量的影响没有确凿的证据。

这项工作的主要局限和机会领域是指随着时间的推移可获得的数据量和变量测量的精度。数据的可用性、时效性和频率也是本研究的局限性。这方面的一个例子是在州一级不存在二氧化碳排放。此外, 由于数据和应用研究的局限, 研究其他国家和地区也是一个挑战。

研究结果对经济增长依赖旅游业流量的经济体和社会具有重要意义, 这些经济体和社会对扭转旅游业对环境的破坏方面做得还不够。

这些模型有助于研究旅游业与环境变量之间的关系, 并可推广到地区、州和省, 以制定当前和未来的行动决策。

这项研究的原创性对该地区(墨西哥、中美洲和拉丁美洲)来说是具有创新性的。没有人用这种方法和这些变量研究过这些问题。就原创性而言, 面板数据和面板数据协整的经典模型是有用的且易于复制, 可供其他国家使用。 研究结果具有一定的相关性, 因为旅游业与部分环境变量在短期内不存在明显的相关性, 但在它们中的一些变量在长期内存在着或强或弱的相关性。

Se examina la relación entre medio ambiente y flujos turísticos, así como variables económicas de los 32 estados de México para el período 1999-2019 basado en la disponibilidad de datos. La literatura relacionada que estudia el turismo y los impactos ambientales es escasa a nivel nacional, siendo la mayoría de ellos estudios de casos locales. Estudios internacionales encuentran que, si la relación existe, es débil o inexistente, utilizando el CO2 como un indicador en la mayoría de los casos.

Se utilizaron metodologías de datos de panel y cointegración de panel, además sistemas de información geográfica para observar la distribución de variables a nivel estatal.

i) El Producto Interno Bruto Estatal, la inercia de las variables ambientales (es decir, volumen de tratamiento de agua y residuos sólidos), habitaciones ocupadas (proxy de la actividad turística) y temperatura promedio tienen un impacto en la evolución contemporánea de las variables ambientales, ii) las variables turísticas nacionales e internacionales no tienen un impacto en el medio ambiente, iii) los paneles están integrados de tal manera que existe un equilibrio a largo plazo entre turismo, crecimiento económico y algunas variables ambientales, y iv) no se encuentra evidencia concluyente con respecto al impacto de la actividad turística en las variables ambientales consideradas.

Las principales limitaciones y áreas de oportunidad del trabajo se refieren a la cantidad de datos disponibles en el tiempo y a la precisión de la medición de las variables. La disponibilidad, temporalidad y frecuencia de los datos también son limitaciones de la investigación. Un ejemplo de ello es la inexistencia de emisiones de CO2 a nivel estatal. Además, el estudio de otros países y regiones para los que existen limitaciones de datos y estudios aplicados también es un reto.

Los resultados son importantes para las economías (en crecimiento) y las sociedades cuyo crecimiento económico depende de los flujos turísticos y que han hecho poco por invertir los daños que el turismo produce en el medio ambiente.

Los modelos pueden contribuir a estudiar la relación entre el turismo y las variables medioambientales y podrían extenderse a regiones, estados y provincias para la toma de decisiones sobre las acciones a emprender para el presente y el futuro.

El artículo proporciona un análisis innovador y exploratorio hacia una perspectiva futura que agrega valor al turismo y la planificación para la sostenibilidad. La relación entre turismo y medio ambiente se ha estudiado durante varios años. La UNTWO ha abordado las consecuencias del turismo en el medio ambiente, particularmente, más basura, mayor consumo de agua, emisiones de CO2 y otros aspectos. Pocos trabajos estudian la relación entre estas variables.

La originalidad de la investigación es innovadora para la región: México, América Central y América Latina. No existen trabajos que hayan estudiado estos problemas con esta metodología y estas variables.

En términos de originalidad, los modelos clásicos de datos de panel y cointegración de datos de panel son útiles y fácilmente replicables para que otros los utilicen en diferentes países.

Los resultados son relevantes porque aparentemente no hay una relación entre el turismo y algunas variables ambientales a corto plazo, existe una relación débil y fuerte a largo plazo entre algunas de ellas.

The purpose of this paper is to examine whether the purchasing power parity (PPP) holds for countries in the West African Monetary Zone (WAMZ).

The author uses time series and panel data techniques.

Overall, the evidence is inconclusive. The time series and panel unit root tests rejected the PPP. The time series cointegration test supported it. The panel cointegration tests are, however, inconclusive.

The inconclusive evidence implies that the appropriateness of the PPP-based policies which have been implemented in the WAMZ may be difficult to assess. Moreover, the question of whether the WAMZ agenda may face trade obstacles is still widely open. Perhaps fractional unit root and cointegration techniques may help pin down conclusive evidence. Future studies may consider this direction.

The paper is original in the sense that it is the first to utilize a mixture of time series and panel data techniques to examine the PPP hypothesis for these countries.

This study aims to measure the relationship between demand and exchange rate shocks in the tourism industry.

A panel data set is constructed covering the period between 1995 and 2017, and the data set includes the top 26 countries that host 10 million tourists and above in the world as of 2017. The standard errors of the series are used as an indicator of shocks. First, the cross-sectional dependency, stationarity and the homogeneity of the series are examined; second, a panel cointegration analysis is implemented; third, long-term panel cointegration coefficients are analyzed with Dynamic Common Correlated Effects (DCCE) approach; and, finally, Dumitrescu and Hurlin’s (2012) Granger non-causality test is used to detect the causality.

The preliminary analyses show that the variables are cross-sectional dependent and heterogeneous and are stationary in their first difference; hence, the effects of the shocks are temporary. On the other hand, as a result of the panel cointegration analysis, it is found that both series are cointegrated over the long-term. However, the long-term coefficients estimated with the DCCE approach are found not to be statistically significant. Finally, as a result of the Dumitrescu and Hurlin’s (2012) Granger non-causality test, it is concluded that there is a causality running from exchange rate shocks to demand shocks.

To the best of the authors’ knowledge, the cointegration between the tourism demand shocks and exchange rates shocks has not been investigated before, and therefore, this study is considered to be a pioneering study that will contribute to the literature.

The purpose of this paper is to assess the contribution of Islamic finance to economic growth in countries that were early adopters of Islamic banking: Malaysia, Indonesia and the Gulf Cooperation Council (GCC) countries.

Through panel cointegration analysis, variance decompositions (VDCs) and impulse response functions, this study investigates the Islamic finance and growth nexus.

Islamic banking is found to contribute to economic growth both in the long run and the short run for both GCC countries and the selected East Asia (EA) countries. In the short run however, Islamic banking contributes more to economic growth in Malaysia and Indonesia compared to the GCC countries.

The results lend support to the view that Islamic intermediation not only leads to economic benefits but also; increases managers' entrepreneurial skills through the involvement of the lender in the decision making and the partnership like relationship between the fund provider and the entrepreneur and also; reduces agency costs which produces positive impact on both the economy and the development of the society. This serves as a motivation for other countries to continuously promote Islamic finance.

To assess the importance of Islamic finance to economic growth, this study compares two main regional Islamic financial hubs, the GCC and EA countries. Another novel aspect of this study is in the methodology; it employs panel cointegration analysis, VDCs and impulse response functions on the set of annual data for period of 2000-2009.

The study aims to determine the socio-cultural variables that affect Turkey's tourism demand. The study proposes how important socio-cultural determinants as well as economic determinants affect tourism demand.

The study examined a sample of 19 countries sending the most visitors to Turkey between 1996 and 2017 by using panel unit root, panel cointegration tests and cointegration estimator methods. The data set consists of variables such as GDP per capita (lnGDPP), total population number (lnPOP), urbanization level, information and communication technology (lnICT), human development index (lnHDI), education level and death rates (lnDTH).

The findings from the analysis provide evidence that the variables in the models show the expected effects on tourism demand. The findings show that apart from economic variables, socio-cultural variables also have an important effect on tourism demand.

The socio-cultural models used in the study were created using variables that can be quantified. The study results are valid for the countries included in the analysis.

The findings of this study will contribute to policymakers in determining the market for Turkish tourism. The results show that the policies to be prepared by considering the socio-cultural characteristics of countries can increase the tourism demand.

The study is significant in that it focuses on socio-cultural variables rather than economic variables commonly used in the literature. The study is original in terms of both the study sample and the model and considers cross-sectional dependency (CD) and homogeneity.

The purpose of this paper is to examine the nature of causal relations between banking sector maturity, stock market maturity, and four aspects of performance and operation of the economy: economic growth, inflation, openness in trade, and the degree of government involvement in the economy.

The authors look for possible links between the variables by conducting panel cointegration and causality tests, using a large sample of Asian countries over the period 1960-2011. Novel panel data estimation methods allow for robust estimates, using both variation between countries and variation over time.

The study identifies interesting causal links among the variables deriving uniquely from our innovations. In particular, The paper finds that for all regions considered, banking sector maturity and stock market maturity are causally linked, sometimes in both directions. Furthermore, stock market maturity may lead to economic growth, both directly and indirectly through indicators such as inflation and trade openness. The findings also support the notion that economic growth affects the maturity of the stock market in most regions.

The results lend support to the notion that a mature financial sector is a key contributor to generating economic growth. Furthermore, economic growth itself has the potential to bring about maturity in the financial sector.

The paper uses sophisticated principal-component analysis, panel cointegration, and Granger causality tests, methods not used in this literature before. The method was applied to recent data pertaining to 35 Asian countries – a group of countries that has previously not been adopted in this literature.

Textile, listed as one of the highly environmentally sensitive goods, its trade is susceptible to be influenced by the implementation of stringent environmental policies. This paper aims to investigate the long-run relationship between revealed comparative advantage (RCA) and Environmental Policy Stringency Index (EPSI) for textile exports of G20 countries in panel data setup.

Apart from trend analysis, the authors have employed Pedroni and Westerlund panel cointegration method and fully modified ordinary least square (FMOLS) method to study the long-run relationship between RCA and EPSI in presence of cross-sectional dependence.

A strong link between trade and environmental stringency is observed for textile in the present study. For G20 countries, slight evidence of the Pollution Haven Hypothesis has also been witnessed in the study. Correspondingly, the results reveal the presence of long-run association between the variables under study, implying that stringent environmental policies reduce RCA for some countries, whereas some countries witness the Porter hypothesis.

The results imply that policy formulation should not aim at limiting the efforts of connecting RCA to environmental stringency but to set trade policies in a wider framework, considering environmental concerns, as these are inseparable subjects. However, this study also provides relevant real-world implications that can support further research.

The present study has important implications for textile exporters such as green innovations. The Porter hypothesis can be a beneficial tool for G20 exporters in enhancing their export performance, especially for the ones dealing in environmentally sensitive goods. This study offers relevant policy implications and provides directions for future research on global trade and environment nexus.

This study deals in a debatable area of research that evaluates the interlinkages between environmental stringency and global trade flows in the G20 countries. An important observation of the study is the asymmetrical nature of policy stringency across different countries and its impact on trade. The unavailability of updated data is the limitation of the present study.

The purpose of this paper is to re-examine the relationship between a country’s aggregate stock market and general economic development for 14 emerging economies for the period from 1995 to 2014.

The methodological approach of the paper is multifold. First, the authors use cointegration analysis to determine the simple dynamics among the variables. Second, the authors utilize vector autoregression analysis to study the dynamics among the variables for the 14 countries. Third, the authors employ panel analysis to determine common variations among the variables and across countries.

When examining the linkage between the stock market and economic development, proxied by gross domestic product growth or with gross fixed capital formation growth, the authors did not find a meaningful relationship between them. However, when the authors included additional control variables strong, dynamic interactions between the two magnitudes surfaced. Specifically, it was found that the stock market is positively and robustly correlated with contemporaneous and future real economic development and, thus, it directly contributed to a country’s economic development either through the production of goods and services or the accumulation of real capital. Thus, it can be inferred that the stock market alone is not capable of boosting economic development in these countries unless being part of a comprehensive financial system (which includes banks) as well as investment in real capital.

The policy implications are clear. Government authorities must recognize that the stock market alone is not a driver of economic development and that a sound, efficient financial system (which includes banks) must be present in order to contribute and foster economic development.

The study is original in the sense that it examines various financial and economic variables to determine the degree of (or dynamic interactions among) the stock market and the real economy for each and all emerging markets in the sample.

Productivity bias hypothesis claims that deviation of the PPP based exchange rate from the equilibrium rate is mainly due to productivity differentials among countries. Early studies that employed cross‐sectional data in testing the hypothesis provided mixed results. A few time‐series studies have provided some support for the hypothesis. This paper relies on panel data and recent advances in panel unit‐root and panel cointegration analysis and provides strong results supporting the hypothesis. The results are not sensitive to classification of the world by regions.

This paper develops methods of Bayesian inference in a cointegrating panel data model. This model involves each cross-sectional unit having a vector error correction representation. It is flexible in the sense that different cross-sectional units can have different cointegration ranks and cointegration spaces. Furthermore, the parameters that characterize short-run dynamics and deterministic components are allowed to vary over cross-sectional units. In addition to a noninformative prior, we introduce an informative prior which allows for information about the likely location of the cointegration space and about the degree of similarity in coefficients in different cross-sectional units. A collapsed Gibbs sampling algorithm is developed which allows for efficient posterior inference. Our methods are illustrated using real and artificial data.