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The focus of research in this book is to develop the ways of making semiparametric/nonparametric techniques accessible to applied economists. While the papers by H. Bierens, J. Horowitz and R.C. Tiwari et al. deal with semiparametric techniques, the papers by Y. Hong and A. Pagan, J.S. Marron, G. Moschini et al., B. Raj and P.L. Siklos, D. Scott an H.P. Schmitz and A. Ullah deal with the nonparametric techniques. It is hoped that this issue will stimulate fruitful discussions and further research in the area of semiparametric and nonparametric econometrics

PurposeThe purpose of this paper is to investigate the nonlinear relationship between shadow economy and income inequality and determine whether the size of shadow economy can influence the level of income inequality.Design/methodology/approachBoth parametric (panel OLS) and nonparametric/semiparametric regression suggested by Robinson (1988) will be used to capture the dynamic nonlinear relationship between these variables using unbalanced panel data of 154 countries from 2000 to 2007. Additionally, the relationship between income inequality and shadow economy on both developed and developing countries will be analyzed and compared.FindingsFirst, semiparametric analysis and nonparametric analysis are significantly different than parametric analysis and better in nonlinear analysis between income inequality and shadow economy. Second, income inequality and shadow economy resemble an inverted-N relationship. Third, the relationship between income inequality and shadow economy is different in developed countries (OECD countries) and developing countries, where OECD countries have similar inverted-N relationship as before. However, for developing countries, income inequality and shadow economy show an inverted-U relationship, similar to the original Kuznets hypothesis.Practical implicationsThis study suggests that there is a possible trade-off between income inequality and shadow economy and helps policy makers in solving both problems effectively.Originality/valueDespite the growing importance of income inequality and shadow economy, literature linking the two variables is scarce. To the best of the authors' knowledge, there is no literature that nonlinearly links these two variables. Furthermore, the dynamics of the relationship between these two variables in developed countries and developing countries will be explored as well.

This volume contains the latest research on nonparametric and semiparametric econometrics and statistics. Chapters by leading international econometricians and statisticians highlight the interface between econometrics and statistical methods for nonparametric and semiparametric procedures. They provide a balanced view of new developments in the analysis and modeling of applied sciences with cross-section, time series, panel, and spatial data sets.

In this article a novel approach to analyze clustered survival data that are subject to extravariation encountered through clustering of survival times is proposed. This is accomplished by extending the Cox proportional hazard model to a frailty model where the cluster‐specific shared frailty is modeled nonparametrically. We assume a nonparametric Dirichlet process for the distribution of frailty. In such a semiparametric setup, we propose a hybrid method to draw model‐based inferences. In the framework of the proposed hybrid method, the estimation of parameters is performed by implementing Monte Carlo expected conditional maximization algorithm. A simulation study is conducted to study the efficiency of our methodology. The proposed methodology is, thereafter, illustrated by a real‐life data on recurrence time to infections in kidney patient.