infonet-economy

In this Master Thesis, we have analytically derived and numerically implemented three estimators of the Prediction Divergence Criterion (Avella-Medina et al., working paper) for Model Selection within the logistic regression framework. After the validation of these estimators by means of simulations, we have performed Model Selection both when the order of the variables was known in…

We investigate the impact of the quality of corporate social responsibility (CSR) reports on sell-side analyst forecast accuracy. The sample comprises 506 large companies that were selected according to the CSR-Sustainability Monitor, which was used to measure the quality of CSR reports issued in 2012 by the members listed in Fortune 500 US and the Global Index. Forecast error is…

Power-to-gas (P2G) is a technology that converts electrical power to gas fuels like methane for storage in the natural gas grid. Due to the low efficiency, the production of synthetic methane is only profitable if electricity is sufficiently cheap. However, P2G facilities are flexible consumers and can benefit from short-term price fluctuations on the electricity spot market. We use…

Understanding and reducing the incidence of accidental bycatch, particularly for vulnerable species such as sharks, is a major challenge for contemporary fisheries management worldwide. Bycatch data, most often collected by at-sea observers during fishing trips, are clustered by trip and/or vessel and typically involve a large number of zero counts and very few positive counts.…

In the first chapter, the problem of Bootstrap inference for the parameters of a GLMM is addressed. We formulate a bootstrapping strategy consisting on the random weighting of the contributions to the Joint Likelihood of Outcomes and Random Effects. Using the Laplace Approximation method for integrals on this function, yields a Random Weighted Log-Likelihood that produces the desired…

This paper develops and applies a novel estimation procedure for quantile regressions with time-varying coefficients based on a fully parametric, multifactor specification. The algorithm recursively filters the multifactor dynamic coefficients with a Kalman filter and parameters are estimated by maximum likelihood. The likelihood function is built on the Skewed-Laplace assumption. In…

Structural models for forward electricity prices are of great relevance nowadays, given the major structural changes in the market due to the increase of renewable energy in the production mix. In this study, we aim at understanding the dynamics of the risk premium (the drift in the dynamics) and the noise (non-Gaussian, stochastic volatility) in futures prices for electricity. We…

This thesis delivers a new framework for the robust parametric estimation of random fields and latent models through the use of the wavelet variance. By proposing a new M-estimation approach for the latter quantity and delivering results on the identifiability of a wide class of latent models, the thesis finally delivers a computationally efficient and statistically sound method to…

Generalized linear models (GLM) and generalized additive models (GAM) are popular statistical methods for modelling continuous and discrete data both parametrically and nonparametrically. In this general framework, we consider the problem of variable selection by studying a wide class of penalized M-estimators that are particularly well suited for high dimensional scenarios where the…