Research

My research lies in econometrics, with a focus on resampling methods for hypothesis testing and high-dimensional model selection.

Broadly speaking, I am interested in the interaction between statistics, nonparametric methods, and applied economics. My work includes data-driven tuning of nonparametric methods, and the use of state-of-the-art econometric and machine learning techniques to study financial, macroeconomic, and climate data.

Journal Publications

  1. C. Grivas, Z. Psaradakis, Automated Bandwidth Selection for Inference in Linear Models with Time-Varying Coefficients, Journal of Time Series Analysis (2025). DOI.
  2. C. Grivas, J. E. Vera Valdés, Robust Estimation of Carbon Dioxide Airborne Fraction Under Measurement Errors, Environmental Research Communications (2025). DOI.
  3. C. Grivas, An Automatic Portmanteau Test For Nonlinear Dependence, Econometrics and Statistics (2023). DOI.

Working Papers

  1. C. Grivas, G. Kapetanios, Z. Psaradakis, V. Sarafidis, M. Vàvra, A. Ventouri, Nonlinear Boosting with Multiple Testing for High-Dimensional Generalised Linear Models, submitted to Review of Economic Studies, arXiv:2607.22440.
  2. C. Grivas, Testing for Time-Varying Exogeneity: A Bootstrap Approach, in preparation, SSRN.
  3. C. Grivas, Z. Psaradakis, M. Vávra, Bootstrap-Assisted Tests for Skewness, Kurtosis, and Normality Under Unspecified Short or Long Memory, Revise and Resubmit, Journal of Business & Economic Statistics.