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This chapter develops robust panel estimation in the form of trimmed mean group estimation for potentially heterogenous panel regression models. It trims outlying individuals of which the sample variances of regressors are either extremely small or large. The limiting distribution of the trimmed estimator can be obtained in a similar way to the standard mean group (MG) estimator, provided the random coefficients are conditionally homoskedastic. The authors consider two trimming methods. The first one is based on the order statistic of the sample variance of each regressor. The second one is based on the Mahalanobis depth of the sample variances of regressors. The authors apply them to the MG estimation of the two-way fixed effects model with potentially heterogeneous slope parameters and to the common correlated effects regression, and the authors derive limiting distribution of each estimator. As an empirical illustration, the authors consider the effect of police on property crime rates using the US state-level panel data.

This chapter extends the work of Baltagi, Bresson, Chaturvedi, and Lacroix (2018) to the popular dynamic panel data model. The authors investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, the authors consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner’s (1986) g-priors for the variance–covariance matrices. The authors propose a general “toolbox” for a wide range of specifications which includes the dynamic panel model with random effects, with cross-correlated effects à la Chamberlain, for the Hausman–Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using a Monte Carlo simulation study, the authors compare the finite sample properties of the proposed estimator to those of standard classical estimators. The chapter contributes to the dynamic panel data literature by proposing a general robust Bayesian framework which encompasses the conventional frequentist specifications and their associated estimation methods as special cases.

The goal of sustainable economic growth is achievable only when economic growth and development occur without environmental degradation. The Environmental Kuznets Curve (EKC) hypothesis explains the inverted U-shaped association between economic activity and environmental degradation. The primary objective of this study is to empirically test the truth behind the EKC hypothesis. In addition to that, the study is intended to analyze the variation in the shape of the EKC; that is, cross-country variation, as well as variation over time. In order to achieve the stated objectives, the study analyzed a long list of countries (75 countries) for a fairly long period of time (1960–2016, i.e., 57 years). The empirical literature in this area estimated the EKC using some form of a polynomial regression equation. This study also used a similar kind of modeling structure to understand cross-country as well as dynamic variation in the shape of the EKC. In this study, firstly the selected countries are grouped on the basis of the shape of the EKC. Secondly, the dynamic behavior of each parameter in the polynomial equation is analyzed to understand the degree of association between economic activity and environmental degradation. This study suggests a decline in degree of association between the two over time.

This paper develops a cross-sectionally augmented distributed lag (CS-DL) approach to the estimation of long-run effects in large dynamic heterogeneous panel data models with cross-sectionally dependent errors. The asymptotic distribution of the CS-DL estimator is derived under coefficient heterogeneity in the case where the time dimension (T ) and the cross-section dimension (N ) are both large. The CS-DL approach is compared with more standard panel data estimators that are based on autoregressive distributed lag (ARDL) specifications. It is shown that unlike the ARDL-type estimator, the CS-DL estimator is robust to misspecification of dynamics and error serial correlation. The theoretical results are illustrated with small sample evidence obtained by means of Monte Carlo simulations, which suggest that the performance of the CS-DL approach is often superior to the alternative panel ARDL estimates, particularly when T is not too large and lies in the range of 30–50.

This chapter empirically investigates the dynamic effects of globalisation on carbon emission in developing countries across the globe, experiencing a high-speed engine of globalisation over the last two decades. The allied existing literature discussed this issue mainly from the angles of economic expansions and integration of the global economy. However, some relevant factors like trade, financial, interpersonal and informational issues and cultural and politics should be highlighted in order to explore their possible influences on the high rate of carbon emission in the developing world under the modern epoch of globalisation. In this regard, this chapter utilises the World Bank World Development Indicators (WDI) (2020) and KOF Globalisation Index (2020) databases on selected 75 developing nations over the period of 2001–2018 to employ the dynamic panel econometric methods. The robust difference in generalised method of moments (GMM) estimates implies that trade is more harmful to high levels of carbon emissions in developing economies than all other components of globalisation.