arXiv subjects

Charisios Grivas

Publications and source records attributed to Charisios Grivas.

3 recordsLinked to original sources

arXiv2026

A Comparison of High-Dimensional Variable Selection Procedures for Electricity Spot Price Forecasting

The paper considers the problem of variable selection for forecasting electricity spot prices. High-dimensional methods such as LASSO and Elastic Net are widely used for this purpose, and while they exhibit strong predictive performance, their tendency to select over-parameterized models raises questions about interpretability. We evaluate the performance of six variable selection procedures, includingthe recently proposed Boosting Multiple Testing (BMT) method, using an extensive dataset from six regional electricity markets. We assess their performance in terms of both out-of-sample forecasting ac-curacy and model parsimony. We find that, although LASSO and Elastic Net achieve similar accuracy and outperform most screening alternatives, BMT matches their forecasting performance while using less than one-tenth as many variables. Our results reveal that BMT offers researchers and practitioners a substantially more interpretable and computationally efficient alternative to shrinkage methods, without any loss of forecasting accuracy. These findings suggest that the over-parameterization typically associated with regularization methods is not a necessary price for predictive accuracy in electricity price forecasting.

econ.EM↗

arXiv2026

Nonlinear Boosting with Multiple Testing in High-Dimensional Generalised Linear Models with Binary Responses

This paper proposes a nonlinear boosting with multiple testing (BMT) approach to variable selection in high-dimensional generalised linear models with binary responses. At each stage of the BMT procedure, the model is updated by adding only the most significant covariate, conditional on those already selected in previous stages, while taking into account the multiple testing nature of the problem. It is shown that, under the stated conditions, the BMT procedure selects all covariates whose true coefficients are nonzero, and no other covariates, with probability tending to one. Furthermore, the procedure enjoys an oracle property, in the sense that the post-BMT maximum likelihood estimator of the parameters of the model is asymptotically equivalent to an oracle estimator that knows the correct sparse model in advance. Monte Carlo experiments demonstrate that BMT outperforms competing methods, delivering high covariate-selection accuracy and low parameter estimation error. An empirical example illustrates that BMT delivers a predictive model for the probability that U.S. inflation exceeds a given threshold over a 12-month horizon which has very good out-of-sample performance.

econ.EM↗

arXiv2024

Robust estimation of carbon dioxide airborne fraction under measurement errors

This paper discusses the effect of measurement errors in the estimation of the carbon dioxide (CO$_2$) airborne fraction. We are the first to present regression-based estimates and standard errors that are robust to measurement errors for the extended model, the preferred specification to estimate the CO$_2$ airborne fraction. To achieve this goal, we add to the literature in three ways: $i)$ We generalise the Deming regression to handle multiple variables. $ii)$ We introduce a bootstrap approach to construct confidence intervals for Deming regression in both univariate and multivariate scenarios. $iii)$ Propose to estimate the airborne fraction using instrumental variables (IV), taking advantage of the variation of additional measurements, to obtain consistent estimates that are robust to measurement errors. IV estimates for the airborne fraction are 44.8%($\pm$ 1.4%; 1$σ$) for the simple specification, and 47.3%($\pm$ 1.1%; 1$σ$) for the extended specification. We show that these estimates are not statistically different from the ordinary least squares (OLS) estimates, while being robust to measurement errors without relying on additional assumptions. In contrast, OLS estimates are shown to fall outside the confidence interval of the Deming regression estimates.

stat.AP↗