Research background: The heterogeneity in the factors that affect demand for environmental quality implicates a diverse set of policies and actions aimed at achieving cleaner production to address the challenges posed by pollution and damage to the natural environment. Even though this topic has been widely addressed, mainly from the traditional perspective of the Environmental Kuznets Curves hypothesis (EKC), it has been assumed that the environment is a luxury good with an income elasticity greater than unity. However, it has recently been recognized that the relationship between income and demand for cleaner energy may be more complex and that further inquiry may be needed for a better understanding. Purpose of the article: This research work, employing a panel of European countries, offers direct explicit parameters for the elasticity of income-environmental quality demand for Greenhouse Gases (GHG), as well as its relationship with other important factors. It provides quantitative novel insights into the complex relationship between income and the preferences for cleaner energy. Methods: A hierarchical regression equations approach is used to analyze the evolution of the elasticity of income-environmental quality demand with the inclusion of further co-variates that are relevant for the preferences side of the EKC, such as consumption, R+D investment and BERD (Business Enterprise Research and Development). The data for the empirical study comes from a panel of 16 European countries for the period from 2010 to 2020. Findings & value added: The results show robust evidence that the elasticity of environmental quality demand, which although positive and significant, does not exceed one. To obtain an elasticity above unity, two more variables are needed, namely the R+D expenditure of business enterprises and the exposure of citizens to air pollution. These two factors have a similar or even higher effect on the preferences of agents for cleaner energy, which also means that the preferences of the citizens are endogenous to technological development. At the theoretical level, this work shows that the technological and preferences arguments are not substitute explanations of the EKC, but that technological development exerts a positive effect on the preferences of inhabitants, whose demand for environmental quality is heavily conditioned by their capabilities to see pollution, even more than by their income level. This also means that public policies directed to improve environmental awareness should be directed first towards those regions where the exposure of the citizens to pollution is lower.
Vector autoregressions (VARs) and their multiple variants are standard models in economic and financial research due to their power for forecasting, data analysis and inference. These properties are a consequence of their capabilities to include multiple variables and lags which, however, turns into an exponential growth of the parameters to be estimated. This means that high-dimensional models with multiple variables and lags are difficult to estimate, leading to omitted variables, information biases and a loss of potential forecasting power. Traditionally, the existing literature has resorted to factor analysis, and specially, to Bayesian methods to overcome this situation. This paper explores the so-called machine learning regularization methods as an alternative to traditional methods of forecasting and impulse response analysis. We find that regularization structures, which allow for high dimensional models, perform better than standard Bayesian methods in nowcasting and forecasting. Moreover, impulse response analysis is robust and consistent with economic theory and evidence, and with the different regularization structures. Specifically, regarding the best regularization structure, an elementwise machine learning structure performs better in nowcasting and in computational efficiency, whilst a componentwise structure performs better in forecasting and cross-validation methods.
This paper focuses on GARCH modelling of the nominal short-term interest rates of the Spanish government three-year bonds. This methodology allows an ex-ante approximation to this variable which proves to be a valuable alternative against econometric specifications that imply a homoscedastic error term. Then, real short-term interest rates are estimated by employing the reduced Fisher equation. Eventually, the results obtained are compared with the observed values of the real time-series in order to measure their accuracy.
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