Collaborations

Research Culture and Environment

Dr. Christis Katsouris research interests lie broadly in econometrics, particularly, time series econometrics, panel data econometrics and macroeconometrics. His research focuses on econometric theory and methods with economically relevant applications. 

• Research Software: For the implementation of econometric methods used in estimation and inference procedures and for the reproducibility of research results, high level computer languages and software such as R and Matlab are employed. 

• Research Supervision:  I would be happy to supervise excellent postgraduate students with strong programming and quantitative skills on topics close to my research areas of expertise. From the time series econometrics literature topics include: robust econometric estimation and inference, forecast evaluation and equal predictive accuracy tests as well as variable and model selection in the presence of multiple structural breaks. From the macroeconometrics literature topics include: identification, estimation and inference of structural parameters in macro models. From the econometrics literature topics include: identification strategies, estimation and inference with economically relevant applications. 

The above topics correspond to dissertation supervision for both undergraduate (BSc) and postgraduate taught students (MSc). Research supervision for Postgraduate Research students (PGRs) and Doctoral Researchers who are affiliated with Graduate Schools at the MRes and PhD level on closely related aspects, in due time. 

Research Impact and Engagement

As an Early Career Researcher my overarching goal is to sustain a high-quality research pipeline and to get published in leading academic journals in economics and econometrics. My current research focuses on developing robust econometric methods for time series and panel data.

Specifically, for the validity of proposed econometric methods we aim to develop asymptotic theory which characterizes the limiting behaviour of model estimators and test statistics. Simulation studies can be used to examine the efficacy and robustness of methods to time series properties that violate classical econometric assumptions. In particular, conventional estimation and inference techniques can be affected by features such as serial correlation, heteroscedasticity, nonstationary volatility and heavy-tailedness. Asymptotic theory of estimators in time series regressions with persistent regressors can different substantially under heteroscedasticity and weak dependence vis-à-vis under homoscedasticity. Tackling finite-sample bias using techniques such as variable transformations, bias-adjustments and  bootstrap resampling, allows to obtain asymptotically valid inference inference and improved estimation. For example, a solution to the presence of asymptotic bias in inference procedures for Local Projections is the implementation of data-dependent selection rules for specifying VAR(h) approximations to VAR (∞) models. Depending on the econometric model valid inferences can hold without bias corrections. Therefore, the development of estimation and inference methods that are robust against features such as the incidental parameter problem and serial correlation in error terms, allow to address economic-policy driven questions.