We study how the shadow economy affects pollution and how this effect depends on corruption levels in public administration. Production in the shadow economy allows firms to avoid environmental regulation policies; a large informal sector may be accompanied by higher pollution levels. Our...

This paper argues that skill formation is a life-cycle process and develops the implications of this insight for Scottish social policy. Families are major producers of skills, and a successful policy needs to promote effective families and to supplement failing ones. Targeted early...

This paper evaluates whether the level of public corruption influences COVID-19 case fatality rates. Using cross-section data, including 64 countries and multiple regression techniques, we find that the level of corruption is positively and significantly associated with COVID-19 human costs....

This study examines the economic globalization and the shadow economy nexus in Egypt. Using time series data from 1976 to 2013, the impulse response analysis shows that the response of the shadow economy in Egypt to positive shocks in economic globalization is negative and statistically...

We study the association between oil rents and tax revenues, highlighting the importance of the shadow economy as a mediating factor. We present a simple theoretical model demonstrating that decreasing oil rents are likely to be positively associated with the tax revenues in a country with a...

Stochastic Volatility (SV) models are widely used in financial applications. To decide whether standard parametric restrictions are justified for a given dataset, a statistical test is required. In this paper, we develop such a test based on the linear state space representation. We provide a...

For over a decade, nonparametric modelling has been successfully applied to study nonlinear structures in financial time series. It is well known that the usual nonparametric models often have less than satisfactory performance when dealing with more than one lag. When the mean has an additive...

We develop inference tools in a semiparametric regression model with missing response data. A semiparametric regression imputation estimator and an empirical likelihood based one for the mean of the response variable are defined. Both the estimators are proved to be asymptotically normal, with...

Additive modelling has been widely used in nonparametric regression to circumvent the "curse of dimensionality", by reducing the problem of estimating a multivariate regression function to the estimation of its univariate components. Estimation of these univariate functions, however, can suffer...