Spatial Models and Tests for Cross-Sectional Dependence

This paper studies the validity of Gibrat’s law for the growth of Slovenian farms between 2007 and 2015 using Farm Accountancy Data Network datasets. Cross-sectional dependence test and four different groups of panel unit root tests are applied to study the relationship between farm size and the farm size growth. It revealed evidence of cross-sectional dependence in farm sizes. Both input (land and labour) and output (economic) sizes of variables as proxy for the measures of farm size are applied. The results suggest that Gibrat’s law is valid for Slovenian farms independently from the measures of farm size and types of panel unit root tests. Slovenian smaller farms are not growing faster than larger ones and thus all farm sizes tend to contribute to an increase in average farm size in generally relatively small- to medium-size farm structures.

Proponents of larger governments argue that government programmes provide essential public goods like education and infrastructure, which, in turn, stimulate the economy. On the other hand, individuals who advocate for more limited government assert that an increase in public spending will be detrimental to economic expansion due to the fact that many functions of government are inefficient and not in the public interest. Secondary data from World development indicator variables spanning from 1985 to 2021 were analysed. The dependent variable for the study was GDP per capita and the independent variables were institutional quality, government expenditure, capital stock and trade openness. The model used for the study are Pearson correlation, Pesaran's , Friedman's and Frees' cross sectional dependency test, IPS and CIPS unit root test, Pooled Mean Group and Dynamic Fixed Effect regression to test for the long-run cointegration and short-run relationship in 32 African countries. The results of the study suggest that there exists a long-run and short-run relationship between government expenditure and economic growth. Additionally, the study emphasises the importance of institutional quality as a significant determinant of this relationship. Therefore, it is recommended that all accessible government funds be allocated towards the objective of establishing durable and self-sufficient infrastructure.

Environmental issues are increasing as human activities damage ecosystems and trigger climate change. This increasing pressure has led to a concentration on development that is not only economically and socially but also environmentally oriented. In line with the spirit of the WCED in 1987, this study aims to assess the direct and spillover impacts of development performance on environmental quality in Sumatra Island. Observations from 2015 to 2022. The data used is panel data derived from the Indonesian government authorities. The research method uses a spatial autoregressive model with one nearest neighbor weighting. Previously, a cross-section dependence test was conducted to ensure the presence of spatial effects in the model. The result showed that increasing the proportion of highly educated workers will improve environmental quality in the observation province and improve the environment in the nearest neighboring province. Democracy also has strong positive direct and spillover effects. An increase in the dependency burden of workers will improve environmental quality in the observation province, with weak spillover effects. Conversely, an increase in real GRDP per capita decreases environmental quality and affects environmental quality in neighboring regions. Four determinants have a strong total effect. Meanwhile, population density and regional expenditure have no significant effect. The implication of this study is the need for intervention from the authorities to change the perspective on economic activity and regional expenditure as a whole so that economic value added does not come at the expense of the environment.

This article provides an overview of the existing literature on panel data models with error cross-sectional dependence (CSD). We distinguish between weak and strong CSD and link these concepts to the spatial and factor structure approaches. We consider estimation under strong and weak exogeneity of the regressors for both T fixed and T large cases. Available tests for CSD and methods for determining the number of factors are discussed in detail. The finite-sample properties of some estimators and statistics are investigated using Monte Carlo experiments.

This paper studies the relationship between GDP fluctuations and long-run economic growth through using macro-panel approach (with small N and large T) in a panel of five selected South Asian countries (SSAC) including Bangladesh, India, Nepal, Pakistan and Sri Lanka over a period of 1980-2010. For this purpose, modern non-stationary panel techniques such as cross section dependence test, unit root test under cross sectional dependence, panel cointegration and Group Mean Fully Modified OLS (GM-FMOLS) estimation are applied. This study finds a significant long-run cointegrating relationship between GDP fluctuations and long-run growth in the SSAC and GM-FMOLS estimates show that this link is negative. It indicates that GDP fluctuations have a significant negative impact on long-run growth in the SSAC and these fluctuations of GDP may be detrimental for long-run growth in developing countries. Therefore, the governments of such countries shouldn’t rely on growth-oriented policies only but should equally focus on managing these fluctuations in GDP to achieve sustained and stable growth rate.

The current study examines the relationship between GDP fluctuations and private investment by using macro panel approach in a panel of five selected South Asian countries (SSAC) including Bangladesh, India, Nepal, Pakistan and Sri Lanka for the period of 1980-2010. The study applies modern non-stationary panel techniques such as cross section dependence test, unit root test under cross sectional dependence, panel cointegration and Group Mean Fully Modified OLS (GM-FMOLS) estimation.The study finds a long-run co-integrating relationship between GDP fluctuations and private investment in the SSAC. GM-FMOLS estimates show that this link is negative. Thus, the results indicate that GDP fluctuations have a significant negative impact on private investment in SSAC as GDP volatility gives a negative signal to private investors. The study also suggests that GDP volatility may be harmful for private investment in developing countries and negative effect on private investment will also be transferred to growth as the investment is a key determinant of growth. So, the governments of developing countries should equally focus on managing the volatility of GDP to increase private investment along with other measures for creating an investment-friendly environment. Additionally, an increase in private investment will further help in maintenance of stability.

This study aims at exploring the determinants of GDP fluctuations using macro panel approach in a panel of five selected South Asian countries (SSAC) including Bangladesh, India, Nepal, Pakistan and Sri Lanka over the period of 1980-2010. For this purpose, modern non-stationary panel techniques such as cross section dependence test, unit root test under cross sectional dependence, panel co-integration and Group Mean Fully Modified OLS (GM-FMOLS) estimation are applied.The results of the Group Mean FMOLS estimates show that aid dependence (AIDGDP), trade openness (OPEN), volatility in the price level (PRIVOL), reliance on agriculture (AGRGDP) and political stability (POLSTB) are the significant determinants of the GDP fluctuations. Thus, it is suggested that these determinants may be managed to reduce the volatility in GDP growth rate.

BackgroundThe COVID-19 pandemic has highlighted the need for the betterment of health status, while also considering health expenditure, energy, and environmental issues. This paper examines the nexus between health status and health expenditure (both public and private), energy consumption and environmental pollution in the SAARC-BIMSTEC region.MethodsWe utilized the panel autoregressive distributed lag (ARDL) model, the heterogeneous panel causality test, the cross sectional dependence test, the cointegration test and the Pesaran cross sectional dependent (CADF) unit root test for obtaining estimated results from data over 16 years (2002–2017).ResultsOur results authorize the cointegration among the variables used, where the coefficients of energy consumption, public and private health expenditures, and economic growth are 0.027, 0.014, 0.030, and 0.029, respectively, and indicating positive and statistically significant effects. The coefficient of environmental pollution is − 0.085, implying significant negative effect on the health status of these regions in the long-run. However, no panel wise significant impact is found in the short-run. Bidirectional and unidirectional causal links between the studied variables and the health status are also identified..ConclusionsThe improved health status in the SAARC-BIMSTEC region needs to be protected by articulating the effective policies. The attained results are theoretically and empirically consistent, and have important policy implications in the health sector.

In panel data analysis, the cross-sectional dependence (CD) test has been extensively used to test the cross-sectional dependence. However, this traditional CD test does not take serial correlation into consideration, which commonly occurs in many fields. To solve this problem, we propose an adjusted CD test which is able to effectively handle serial correlation. More specifically, the serial correlation can be of arbitrary form in our work. Furthermore, we establish the theoretical properties of the proposed adjusted CD test. Our extensive Monte Carlo experiments show that the traditional CD test cannot work well under serial correlation, while the proposed adjusted CD test does provide rather satisfactory performance.

In recent literature, cross-sectional dependence among individuals is a concern when n is large. As serial correlation in time-series analysis, the cross-sectional of dependence/correlation could invalidate inference. In some cases, it could even render inconsistent estimation. This chapter discusses testing procedures in the fixed effects panel data models, including static and dynamic cases…

It is well known that the standard Breusch and Pagan (1980) LM test for cross-equation correlation in a SUR model is not appropriate for testing cross-sectional dependence in panel data models when the number of cross-sectional units (n) is large and the number of time periods (T) is small. In fact, a scaled version of this LM test was proposed by Pesaran (2004) and its finite sample bias was corrected by Pesaran et al. (2008). This was done in the context of a heterogeneous panel data model. This paper derives the asymptotic bias of this scaled version of the LM test in the context of a fixed effects homogeneous panel data model. This asymptotic bias is found to be a constant related to n and T, which suggests a simple bias corrected LM test for the null hypothesis. Additionally, the paper carries out some Monte Carlo experiments to compare the finite sample properties of this proposed test with existing tests for cross-sectional dependence.

In recent years, the interrelationships between the environment, energy, and health have received a growing amount of attention due to their substantial impact on the health of humans. By examining what influences Eastern Europeans' longevity between 1990 and 2021, this study hopes to contribute to this field of study. Energy consumption, health expenditure, pollution, institutional quality index (IQI), financial development, and other attributes profoundly impact human health. Because of the extensive network of commerce, tourism, education, religion, and treaties connecting East European countries, tests for cross-sectional dependence (CSD) and slope heterogeneity (SH) are utilized. After verifying the CSD and SH issues, the study uses the second generation's unit root and cointegration tests. As the previous test indicates, a new panel method, the cross-sectional autoregressive distributive lag (CS-ARDL) model, is required, as conventional estimations are inappropriate. The Quantile Regression (QR) method is also applied to check robustness. This study indicated that increased health expenditure, renewable energy consumption, and IQI improves health outcomes in Eastern European nations. There was a good connection between renewable energy consumption and health benefits, the study concluded. Though financial development positively impacts life expectancy, the impact is insignificant. On the other hand, the study also shows that CO2 emissions and fossil fuel consumption decrease life expectancy. These results are consistent with those obtained using the QR method. To enhance health outcomes, it is necessary to take measures to raise health spending, increase the use of renewable energy, and foster financial development over the long term. On the other hand, Eastern European nations need to shift their attention from fossil fuels to renewable energy.

This study examines the effects of health expenditure, energy consumption, CO2 emissions, population size, and income on health outcomes in 46 Asian nations between 1997 and 2019. Cross-sectional dependence (CSD) and slope heterogeneity (SH) tests are utilized due to the close linkages between Asian nations as a result of commerce, tourism, religion, and international agreements. The research uses unit root and cointegration tests of the second generation after validating CSD and SH issues. Due to the results of the CSD and SH tests, it is clear that conventional methods of estimation are inappropriate, so a new panel method, the inter autoregressive distributive lag (CS-ARDL) model, is used instead. In addition to CS-ARDL, the study's results were checked with a common correlated effects mean group (CCEMG) method and an augmented mean group (AMG) method. According to the CS-ARDL study, higher rates of energy use and healthcare spending lead to better health outcomes for Asian countries over the long run. CO2 emissions are shown to be harmful to human health, according to the study. The influence of a population's size on health outcomes is shown to be negative in the CS-ARDL and CCEMG, but favorable in the AMG. Only the AMG coefficient is significant. In most instances, the results of the AMG and CCEMG corroborate the results of the CS-ARDL. Among all the factors influencing life expectancy in Asian countries, healthcare spending is the most influential. Hence, to improve health outcomes, Asian countries need to take the required actions to boost health spending, energy consumption, and long-term economic growth. To achieve the best possible health outcomes, Asian countries should also reduce their CO2 emissions.

This study empirically investigated financial development moderating task on the connection allying various types of financial inflows (foreign direct investment, foreign portfolio investment, foreign debt flows, and remittances) and economic growth in the West Africa sub-region, while accounting for cross sectional dependence existence. The analysis is motivated by the need to understand how financial development level influences financial inflow effectiveness in economic growth promotion, given the interconnectedness and potential spillover effects among countries in the region. Drawing from a panel dataset spanning 1990-2022 for five West African countries, a range of econometric techniques was employed to address cross-sectional dependence and slope heterogeneity, among which includes: slope homogeneity tests, cross-sectional dependence tests, second-generation panel unit root tests, and estimation methods robust to cross-sectional dependence, such as Common Correlated Effects Mean Group (CCEMG) and Augmented Mean Group (AMG) estimators. Using the CCEMG estimator, financial development exerted a positive and significant influence on economic growth, and its interaction with foreign portfolio investment was also positive and significant. The AMG estimator provided additional insights, showing remittances had a positive impact on growth, while foreign portfolio investment negatively affected growth. Notably, the interaction between financial development and remittances was negative and significant under the AMG model. These mixed results suggest that while financial development generally promotes growth, its interactions with different types of capital flows can have varying and even opposing effects depending on the specific combination and empirical approach used. The study concludes that financial development engages in a crucial moderating task in the connection allying financial inflows and economic growth in the West Africa sub-region, with varying effects across different types of financial inflows. The findings underscore the relevance of accounting for cross-sectional dependence and heterogeneity in panel data analysis and have policy implications for harnessing the potential gains of financial inflows while promoting sustainable growth in the West Africa sub-region.

This study examined the predictors of carbon emissions in member countries of the North American Free Trade Agreement (NAFTA). Panel models robust to cross-sectional dependence and slope heterogeneity were used for the study. From the heterogeneity and cross-sectional dependence tests, the studied panel was heterogeneous and cross-sectionally dependent. Also, the unit root and cointegration tests established the series to be first differenced stationary and cointegrated in the long run. Additionally, results of the CCEMG regression estimator in the whole panel affirmed economic growth (GDP) to be a significantly positive predictor of CO2 emissions, while foreign direct investments (FDI) and population growth (POP) were trivial determinants of CO2 emissions. The discoveries were however diverse in the individual countries. Finally, there was no causality between GDP and CO2 emissions and between POP and CO2 emissions. However, there was a one-way causality from CO2 emissions to FDI. Policy recommendations are further discussed.