Teaching Evaluations

TEACHING REFERENCES

Representative Teaching Presentation

Teaching Presentation in Econometrics

University of Helsinki

Prof. Markku Lanne (Econometrics Group)
Prof. Mika Meitz (Econometrics Group)
Prof. Niku Määttänen (Macroeconomics Group)
Prof. Roope Uusitalo (Labour Economics Group)

University of Exeter

Dr. Sebastian Kripfganz (Econometrics Group)
Dr. Xiaohui Zhang (Econometrics Group)
Dr. Edwin Ip (Applied Microeconomics Group)

University of Southampton

Prof. Peter W. F. Smith (Department of Social Statistics)
Prof. Tassos Magdalinos (Department of Economics)
Dr. Antonella Ianni (Department of Economics)

Southampton Statistical Sciences Research Institute

Prof. Zudi Lu (School of Mathematical Sciences)

Photo Credit: © Christis Katsouris (2011)

TEACHING PHILOSOPHY

Pillar 1. Enhancement of high-quality teaching that focuses on developing analytical and quantitative skills through effective teaching methods.

Pillar 2. Commitment in cultivating richness of thought and perspectives by encouraging classroom participation and teamwork through design thinking.

Pillar 3. Promoting inclusive teaching through an inclusive learning environment using transformative pedagogy and evidence-based assessment strategies.

"I find it exciting to teach and work at the intersection of econometrics and statistics, as well as time series econometrics and macroeconometrics, as these fields are increasingly interconnected. In my teaching, I focus on real-world problems, explore practical solutions, and emphasise the links between theoretical and applied econometrics. In my pedagogy, I encourage students to ask questions, to collaborate both inside and outside the classroom as well as to develop adaptable and transferable skills."

TEACHING TRAINING

RECENT TEACHING TRAINING

Academic Professional Programme [Master Level Module 2] (University of Exeter, 2023).
Academic Professional Programme [Master Level Module 1] (University of Exeter, 2022).
Learning and Teaching in Higher Education (University of Exeter, 2022).

PAST TEACHING TRAINING

Preparing and delivering seminars (UEA online workshop)

Doctoral College, University of Southampton, UK (2020).

Workshop for Graduate Teaching Assistants in economics.

The Economics Network, Royal Holloway University of London, UK (2019).

Orientation to Teaching and Demonstrating (Step 1 and Step 2).

Doctoral College, University of Southampton, UK (2019).

Workshop for effective teaching practices in higher education.

Teaching and Learning Centre, University of Cyprus, Cyprus (2016).

Workshop for mathematics tutors.

Mathematics Resource Centre, University of Bath, UK (2010).

SELECTED FEEDBACK

University of Exeter

Lecturer in Economics during the 2022/2023 academic year.

Undergraduate Teaching

(Department of Economics, University of Exeter Business School, Academic year: 2022/2023).

“The enthusiasm of Christis - He always takes the time to explain all the concepts and ensure that all the students have a solid understanding of the subject. It is a fairly difficult module, in terms of the volume of content to be covered, but Christis has been very thorough in covering all aspects of the course. Furthermore, Christis always has time for his students and is very willing to provide feedback and help his students achieve their best possible grade.”
“Overall the concepts and topics were explained with great mathematical rigor. The lecturer of the module provided a very extensive knowledge of the subject-matter and there were attempts to engage with attendees. Topics are well explained and demonstrated during the seminars as well.”
“I think that the module was structured very well and introduced and developed my understanding on statistics and probability topics well.”
“The lecture slides were useful as they successfully summarized the topics. We were also given a good range of questions to attempt, for example in seminars and for homework.”
“Ι wish to thank my personal tutor Dr. Christis Katsouris, for his unwavering support during my first year as an undergraduate student at the University of Exeter Business School. He devoted significant amount of time during meetings and via email to provide advice regarding module choices in relation to my degree as well as with respect to my future employment prospects”.

Postgraduate Teaching

“The Applied Econometrics II module was very well organized and improved our understanding in the implementation of econometric methods using economic data. Both Dr. Julia Dyer and Dr. Christis Katsouris provided detailed explanation of the material during the lectures and the Computer Lab sessions. They also answered questions in a timely manner and provided guidance on issues related to econometric analysis.” (MS.c. in Financial Economics student, May 2023).

Postgraduate Dissertation Supervision

(Department of Economics, University of Exeter Business School, Academic year: 2022/2023).

“First and foremost, I extend my heartfelt gratitude to my supervisor, Dr. Christis Katsouris. His guidance, support, and expertise have been invaluable throughout this research process. From the initial stages of defining the research question to the final stages of writing, Dr. Katsouris has been an unwavering source of inspiration and intellectual rigor. His constructive feedback and encouragement have shaped this dissertation in countless ways, and for that, I am deeply grateful.” (MS.c. in Financial Economics student, September 2023).
“I would like to thank my supervisor, Dr. Christis Katsouris. He is a very responsible professor who gave me great help and guidance and played an important role in my learning path.” (MS.c. in Financial Economics student, September 2023).

Feedback from Academic Staff

“Christis provided valuable assistance with the teaching of the course throughout the term, such as running very popular master classes and computer lab sessions, regularly updating the blackboard page of the module to provide timely feedback to students' assignments and replying to student's enquirers via email and during office hours”. (University of Southampton, December 2019).
“Thank you Christis for the help with the teaching activities of the course. You did a lot more than I would have expected and the students really appreciated the guidance and support.” (Dr. Yves Berger, Associate Professor, University of Southampton, December 2020).
“Thank you Christis for suggesting potential seminar speakers for our Statistics Seminar Series. One of the academics you suggested agreed to give a talk during one of the upcoming sessions.” (S3RI's Seminar Organizer, University of Southampton, September 2021).
“Thank you Christis for helping with the co-organization of the Time Series and Machine Learning Reading Group during the past 2 years. You have provided many interesting suggestions on related statistical aspects found in the literature. Also your questions and useful remarks have motivated further discussions during the presentation sessions.” (Dr. Chao Zheng, Lecturer in Statistics, University of Southampton, June 2024).
“Dr Christis Katsouris, taught on modules from our undergraduate and postgraduate programmes and enthusiastically provided high-quality teaching support to our students, exhibiting academic leadership while enhancing the student experience.”

Photo Credit: © Christis Katsouris (2013)

Inclusive Education - Excellent feedback - Student Support - Expanding Opportunities

The role of lectures in the learning process of undergraduate students

Lectures play an important role in facilitating student's learning process through knowledge transmission. From student’s perspectives certain lecturers’ characteristics can ensure the effectiveness of teaching practices and enhance the learning process. The research and teaching experience of a lecturer as well as the prior acquisition of a diverse set of skills drawn from participation to teaching workshops and leadership activities are extremely valuable for creating and fostering a positive learning environment. Second, lectures are instrumental not only for the communication of conceptual knowledge but also for encouraging the discussion of ideas and concepts, motivating students' self-efficacy in learning, directing focus as well as emphasising key learning objectives. In addition, encouraging students to attend lectures and write their own notes during the delivery of teaching sessions helps students to improve their understanding and further contribute to their learning process. Using engaging and innovative teaching practices allows to maximise students' learning outcomes, especially when incorporating research-led approaches. In particular, an underline mechanism associated with learning is memory retention which can be optimised by understanding the role of content's variability and the intervals between learning sessions. Recently, studies have shown that spaced repetition enhances memory for identical information over long intervals, whereas variability in content improves recall of isolated features. Understanding these mechanisms allows the use of effective teaching methods that support student learning.

Literature Review:

Baker, L. A., and Spencely, C. (2023). "Is Hybrid Teaching Delivering Equivalent Learning for Students in Higher Education?". Journal of Further and Higher Education, 47(5), 674-686.
Frick, M., Iijima, R., and Ishii, Y. (2023). "Learning Efficiency of Multiagent Information Structures". Journal of Political Economy, 131(12), 3377-3414.
Tomlinson, A., Simpson, A., and Killingback, C. (2023). "Student Expectations of Teaching and Learning when Starting University: A Systematic Review". Journal of Further and Higher Education, 47(8), 1054-1073.
Andres, H. P. (2019). "Active Teaching to Manage Course Difficulty and Learning Motivation". Journal of Further and Higher Education, 43(2), 220-235.
Deming, D. J. (2017). "The Growing Importance of Social Skills in the Labor Market". Quarterly Journal of Economics, 132(4), 1593-1640.

The role of motivation and main challenges in university teaching

I. Assessment and Evaluation in Higher Education

Providing timely feedback and constructive comments for further improvements on both formative and summative assessments. Overall good feedback has to follow the following principles:

(i) be constructive; feedback should not just be about benchmarking students against the assessment criteria. Good feedback should also always be feedforward, identifying both the strengths and the weakness of the work and make clear what students can do to improve.

(ii) be specific; good feedback is specific to the students, clearly linked to the assessment task, the marking criteria and the ILOs for the module.

(iii) be consistent; good feedback is consistent across the whole module, ensuring individual markers are giving similar types of feedback and levels of detail to all students.

Encouraging class participation and attendance with weekly reminders on the main topics to be covered during lecturers (e.g., via Blackboard) or summarising the main discussions and key learning objectives that were covered in a given week.

Overall, high-quality teaching and learning activities should provide effective advising for undergraduate and graduate students by (i) dedicating time to one-to-one meetings, (ii) building research study groups with students and faculty members, (iii) teaching quantitative methods for the econometric analysis of macro and financial time series data, (iv) supporting students' professional development.

Literature Review:

Bearman, M., et al. (2024). "Enhancing Feedback Practices within PhD Supervision: A Qualitative Framework Synthesis of the Literature". Assessment & Evaluation in Higher Education, 1-17.
Chugh, R., Macht, S., and Harreveld, B. (2022). "Supervisory Feedback to Postgraduate Research Students: A Literature Review". Assessment & Evaluation in Higher Education, 47(5), 683-697.
Hirschberg, J., and Lye, J. (2016). "The Influence of Student Experiences on Post-Graduation Surveys". Assessment & Evaluation in Higher Education, 41(2), 265-285.

II. Motivation Approaches in Higher Education

This component is indeed crucial for enhancing inclusion in learning and for equipping students with the necessary analytical and quantitative skills. Therefore, improving students learning outcomes for courses which have as learning objectives the use of econometric software requires effectively combining motivation methods with assessment and evaluation methods (e.g., the type of questions in assessments, assignment tasks that require the use of econometric software for the econometric analysis of macro and financial data). In addition, encouraging students to structure their assignments based on standard practices used in academic papers, can enhance their presentation skills, as well as their ability to explain rigorously and clearly economic concepts and econometric methodologies.

What we did: e.g., we developed a mixed-frequency time series regression to analyse the impact of information from macroeconomic aggregates sampled at different periods on financial markets.
How we did it: e.g., what econometric methods were used and what is the novelty/contribution to the literature.
Why we did it: e.g., why its imporant and what are the main findings of the study.

Literature Review:

Cotton, C. S., et al. (2026). "Why Don’t Struggling Students Do Their Homework? Disentangling Motivation and Study Productivity as Drivers of Human Capital Formation". Journal of Political Economy, 134(1), 86-149.
Dey, I. (2026). "Impact of Discussion Sessions on Students’ Learning Outcomes in an Econometrics Class". The American Economist, 71(1), 15-31.
Zhou, P. (2024). "Make Lectures Match How We Learn: The Nonlinear Teaching Approach to Economics". Education Sciences, 14(5), 509.
Erden, S. (2023). "Enhancing Learning Outcomes in Econometrics: A 12-year study". Education Sciences, 13(9), 913.
Cladera, M. (2021). "Assessing the Attitudes of Economics Students Towards Econometrics". International Review of Economics Education, 37, 100216.
Mendez-Carbajo, D. (2019). "Experiential Learning in Macroeconomics through FREDcast". International Review of Economics Education, 30, 100137.

III. Main Challenges in Teaching Econometrics and Statistics

One of the main challenges when teaching quantitative modules (such as econometrics, statistics, financial econometrics, applied econometrics) at the university level, is the presence of a heterogeneous student population with different technical standing. Therefore, to ensure high-quality teaching and learning environment in higher education environments, is crucial to employ effective teaching methods in order to facilitate better understanding. Specific examples include the effective teaching of estimation methods for model parameters using approaches such as maximum likelihood estimation, GMM, two-step estimators that involve different technical skills both from the theoretical and the computational perspective.

Another example is the challenge in ensuring the normaliSation of students' understanding in important notions and approaches commonly used in statistical decision theory (i.e., in econometric inference); especially with respect to the comparison of statistical approaches and empirical results obtained from methods based on different assumptions and conditions. For example, if we are interested to assess whether the specification of the conditional prior distribution is driving posterior inference in practice it requires to assess whether changes in this prior change the posterior. In other words, the informativeness of the prior distribution can only be assessed by evaluating the sensitivity of the posterior distribution to the choice of the prior rather by comparing prior and posterior distributions.

Literature Review:

Abadie, A., Angrist, J., Frandsen, B., and Pischke, J. S. (2025). "Harvesting Differences-in-Differences and Event-Study Evidence". NBER Working Paper (No. w34550). Available at nber/w34550.
Fay, M. P., et al. (2022). "Interpreting 𝑝-Values and Confidence Intervals Using Well-Calibrated Null Preference Priors". Statistical Science, 37(4), 455-472.
Giacomini, R., Kitagawa, T., and Read, M. (2022). "Narrative Restrictions and Proxies: Rejoinder". Journal of Business & Economic Statistics, 40(4), 1438-1441.
Rice, K., and Ye, L. (2022). "Expressing Regret: A Unified View of Credible Intervals". The American Statistician, 76(3), 248-256.
Thomas, S., and Tu, W. (2021). "Learning Hamiltonian Monte Carlo in R". The American Statistician, 75(4), 403-413.
Hendry, D. F., and Mizon, G. E. (2016). "Improving the Teaching of Econometrics". Cogent Economics & Finance, 4(1), 1170096.
Angrist, J. D., and Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.

The role of AI in university teaching

The experience and expertise of lecturers are important elements in conveying learning objectives and explaining the underline economic theory. In particular, for the effective dissemination of knowledge in class, the characteristic of lecturers are the main drivers of the learning process; in other words a kind, charismatic, accessible, encouraging and inspiring lecturer cannot be replaced by any AI-type avatar. The use of AI tools can be employed for the purpose of facilitating a positive learning environment, which is interlinked with current trends in the labor market. For example, the availability of streaming data motivated the development of statistical inference methods, particularly useful to practitioners and policymakers.

Recent advances in AI and machine learning provide opportunities to incorporate AI in education activities for the purpose of improving and enhancing the teaching and learning both for educators as well as for students. Nevertheless, despite the capabilities that these models offer, especially in organising learning processes, we should be aware of their pitfalls and limitations with respect to the role of educators in fostering human interactions, dialogue and transmission of knowledge (e.g., see, Gan et al, 2023). Moreover, examination methods (e.g., summative and formative assessments) should be designed in such a way to ensure that educators are able to assess understanding of course material and critical (statistical) thinking.

Literature Review:

Bach, P., Chernozhukov, V., Klaassen, et al. (2026). "Adventures in Demand Analysis using AI". Preprint arXiv:2501.00382.
Battaglia, L., Christensen, T., Hansen, S., and Sacher, S. (2025). "Inference for Regression with Variables Generated by AI or Machine Learning". Preprint arXiv:2402.15585.
Li, X., et al. (2025). "A Statistical Framework of Watermarks for Large Language Models: Pivot, Detection Efficiency and Optimal Rules". Annals of Statistics, 53(1), 322-351.
Hansen, A. L., et al. (2025). "Simulating the Survey of Professional Forecasters". Available at SSRN 5066286.
Qian, S., Mehra, A., and Liu, D. (2025). "The Economics of AI Supply Chain Regulation". Available at SSRN 5935134.
Xie, J., Yan, X., Jiang, B., and Kong, L. (2025). "Statistical Inference for Smoothed Quantile Regression with Streaming Data". Journal of Econometrics, 249, 105924.
Carriero, A., Pettenuzzo, D., and Shekhar, S. (2024). "Macroeconomic Forecasting with Large Language Models". Preprint arXiv:2407.00890.
Gan, W., Qi, Z., Wu, J., and Lin, J. C. W. (2023). "Large Language Models in Education: Vision and Opportunities". In 2023 IEEE International Conference on Big Data (BigData) (pp. 4776-4785). IEEE.