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Advanced Econometrics and Machine Learning Methods
Syllabus
The 'Advanced Econometrics with Machine Learning Methods' seminars focus on theoretical and methodological issues in high-dimensional econometrics.
In particular, we review recent theoretical and methodological developments from the econometrics and machine learning literature, covering aspects of estimation and inference for both cross-section data and time series data. We discuss regularized estimation techniques for time series regressions with weakly dependent errors in high-dimensional environments. For example, Caner & Knight (2013, JSPI) propose a model choice criterion which allows to distinguish between stationary and nonstationary covariates in Lasso-based time series regression models. The shrinkage method proposed by these authors simultaneously selects the optimal lag length and the unit root regressors. Recently, Reinschlüssel & Arnold (2024, arXiv:2402.16580) and Arnold & Reinschlüssel (2024, arXiv:2404.06205), propose adaptive Lasso inference procedures which have desirable statistical properties. Assumptions related to model selection consistency in Lasso regressions with possibly nonstationary data can be found in Kock (2016, Econometric Theory).
Moreover, properties found in statistical learning theory are often embedded in Machine Learning estimation techniques to facilitate robustness for data features, based on algorithmic honesty, accuracy and fairness principles (statistical guarantees). Fundamental principles used in the statistical learning of parameters address statistical problems such as the problem where only the minimal partition ratio is known during training with the goal to diminish prejudice on underrepresented subgroups via inaccessible information (e.g., worst-case fairness vis-a-vis worst-case bias formulations). An example from econometrics is the case of worst-case bias of the Robust-F statistic when used as a test for weak instruments. Moreover, statistical theory and causal inference techniques are constructed via the Neymann-Orthogonalization based on conditions such as the notion of statistical exchangeability. A relevant example from economics is the distribution of desirable characteristics of candidate professionals randomly drawn from a list that is entered in a selection process mechanism under the presence of imperfect information.
Seminar 1 (Statistical Learning Theory: Neyman-Orthogonalization and Statistical Guarantees)
Seminar 2 (Sparse Causal Effect Estimation and Inference Methods)
Seminar 3 (Debiased Lasso Approach: Bootstrap Methods and Statistical Tests)
Seminar 4 (Computational Aspects and Uniform Inference Methods)
Seminar 5 (Estimation and Inference in High-Dimensional IV Regressions: Instrument Validity, Tests for Overidentifying Restrictions, Honest Confidence Intervals)
Seminar 6 (Adaptive Lasso Estimation and Model Consistency Properties with Stationary and Nonstationary Processes)
Seminar 7 (Further Machine Learning techniques for Time Series Regressions: Oracle inequalities and rate-optimal estimation)
Seminar 8 (Forecasting Time Series using Machine Learning techniques and Local Projections in HDTS)
(Reading List, updated December 2024)
Bibliography:
Chan, F., and Mátyás, L. (2022). Econometrics with Machine Learning. Springer Press.
Wainwright, M. J. (2019). High-Dimensional Statistics: A Non-Asymptotic Viewpoint. Cambridge University Press.
James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer Press.
Bühlmann, P., and van De Geer, S. (2011). Statistics for High-Dimensional Data: Methods, Theory and Applications. Springer Science & Business Media.
Massart, P. (2007). Concentration Inequalities and Model Selection. Ecole d'Eté de Probabilités de Saint-Flour XXXIII-2003. Berlin, Heidelberg: Springer.
van der Vaart, A. W. (2000). Asymptotic Statistics. Cambridge University Press.
Davidson J. (2000). Econometric Theory. Wiley-Blackwell.
I. Machine Learning Methods for Time Series Data
Arnold, M.C., and Reinschlüssel, T. (2024). "Adaptive Unit Root Inference in Autoregressions using the Lasso Solution Path". Preprint arXiv:2404.06205.
Arnold, M.C., and Reinschlüssel, T. (2024). "Bootstrap Adaptive Lasso Solution Path Unit Root Tests". Preprint arXiv:2409.07859.
Reinschlüssel, T., and Arnold, M.C. (2024). "Information-Enriched Selection of Stationary and Non-Stationary Autoregressions using the Adaptive Lasso". Preprint arXiv:2402.16580.
Adamek, R., Smeekes, S., and Wilms, I. (2024). "Local Projection Inference in High Dimensions". The Econometrics Journal, utae012.
Babii, A., Ghysels, E., and Striaukas, J. (2022). "Machine Learning Time Series Regressions with an Application to Nowcasting". Journal of Business & Economic Statistics, 40(3), 1094-1106.
Masini, R.P., Medeiros, M.C., and Mendes, E.F. (2022). "Regularized Estimation of High‐Dimensional Vector Autoregressions with Weakly Dependent Innovations". Journal of Time Series Analysis, 43(4), 532-557.
Wang, F. (2022). "Maximum Likelihood Estimation and Inference for High Dimensional Generalized Factor Models with Application to Factor-Augmented Regressions". Journal of Econometrics, 229(1), 180-200.
Dovì, M.S. (2021). "Inference on the New Keynesian Phillips Curve with Very Many Instrumental Variables". Preprint arXiv:2101.09543.
Ghosh, S., Khare, K., and Michailidis, G. (2021). "Strong Selection Consistency of Bayesian Vector Autoregressive Models based on a Pseudo-Likelihood Approach". Annals of Statistics, 49(3), 1267-1299.
Wong, K.C., Li, Z., and Tewari, A. (2020). "Lasso Guarantees for β-mixing Heavy-Tailed Time Series". Annals of Statistics, 48(2), 1124-1142.
Barigozzi, M., Cho, H., and Fryzlewicz, P. (2018). "Simultaneous Multiple Change-Point and Factor Analysis for High-Dimensional Time Series". Journal of Econometrics, 206(1), 187-225.
Medeiros, M.C., and Mendes, E. F. (2016). "ℓ1-regularization of High-Dimensional Time-Series Models with Non-Gaussian and Heteroskedastic Errors". Journal of Econometrics, 191(1), 255-271.
Kock, A.B. (2016). "Consistent and Conservative Model Selection with the Adaptive Lasso in Stationary and Nonstationary Autoregressions". Econometric Theory, 32(1), 243-259.
Kock, A.B., and Callot, L. (2015). "Oracle Inequalities for High Dimensional Vector Autoregressions". Journal of Econometrics, 186(2), 325-344.
Han, F., Xu, S., and Liu, H. (2015). "Rate-Optimal Estimation of a High-Dimensional Semiparametric Time Series Model". Working paper, University of Washington.
Rho, Y., and Shao, X. (2015). "Inference for Time Series Regression Models with Weakly Dependent and Heteroscedastic Errors". Journal of Business & Economic Statistics, 33(3), 444-457.
Caner, M., and Knight, K. (2013). "An Alternative to Unit Root Tests: Bridge Estimators Differentiate between Nonstationary versus Stationary Models and Select Optimal Lag". Journal of Statistical Planning and Inference, 143(4), 691-715.
II. Machine Learning Methods for Cross-Sectional Data
Windmeijer, F. (2025). "The Robust F-Statistic as a Test for Weak Instruments". Journal of Econometrics, 247, 105951.
Ding, J., Guo, X., Shi, Y., and Wang, Y. (2025). "Inference of High-Dimensional Weak Instrumental Variable Regression Models without Ridge-Regularization". Preprint arXiv:2504.20686.
Dovì, M.S., Kock, A.B., and Mavroeidis, S. (2024). "A Ridge-Regularized Jackknifed Anderson-Rubin Test". Journal of Business & Economic Statistics, 42(3), 1083-1094.
Londschien, M., and Bühlmann, P. (2024). "Weak-Instrument-Robust Subvector Inference in IV Regression: A Subvector Lagrange Multiplier Test & Properties of Subvector Anderson-Rubin Confidence Sets". Preprint arXiv:2407.15256.
Huang, S., Pfister, N., and Bowden, J. (2024). "Sparse Causal Effect Estimation using Two-Sample Summary Statistics in the Presence of Unmeasured Confounding". Preprint arXiv:2410.12300.
Shi, H., Zhang, X., Guo, X., He, B., and Wang, C. (2024). "Testing Overidentifying Restrictions on High-Dimensional Instruments and Covariates". Annals of the Institute of Statistical Mathematics, 1-22.
Fan, J., Masini, R.P., and Medeiros, M.C. (2023). "Bridging Factor and Sparse Models". Annals of Statistics, 51(4), 1692-1717.
Han, D., Huang, J., Lin, Y., and Shen, G. (2022). "Robust Post-Selection Inference of High-Dimensional Mean Regression with Heavy-Tailed Asymmetric or Heteroskedastic Errors". Journal of Econometrics, 230(2), 416-431.
Pfister, N., and Peters, J. (2022). "Identifiability of Sparse Causal Effects using Instrumental Variables". In Uncertainty in Artificial Intelligence (pp. 1613-1622). PMLR.
Belloni, A., Hansen, C., and Newey, W. (2022). "High-Dimensional Linear Models with Many Endogenous Variables". Journal of Econometrics, 228(1), 4-26.
Gautier, E., and Rose, C. (2022). "Fast, Robust Inference for Linear Instrumental Variables Models using Self-Normalized Moments". Preprint arXiv:2211.02249.
Guo, Z., Ćevid, D., and Bühlmann, P. (2022). "Doubly Debiased Lasso: High-Dimensional Inference under Hidden Confounding". Annals of Statistics, 50(3), 1320.
Sun, Q., and Zhang, H. (2021). "Targeted Inference involving High-Dimensional Data using Nuisance Penalized Regression". Journal of the American Statistical Association, 116(535), 1472-1486.
Babii, A. (2020). "Honest Confidence Sets in Nonparametric IV Regression and other ill-posed Models". Econometric Theory, 36(4), 658-706.
Breunig, C., Mammen, E., and Simoni, A. (2020). "Ill-posed Estimation in High-Dimensional Models with Instrumental Variables". Journal of Econometrics, 219(1), 171-200.
Gold, D., Lederer, J., and Tao, J. (2020). "Inference for High-Dimensional Instrumental Variables Regression". Journal of Econometrics, 217(1), 79-111.
Windmeijer, F., Farbmacher, H., Davies, N., and Smith, G.D. (2019). "On the Use of the Lasso for Instrumental Variables Estimation with some Invalid Instruments". Journal of the American Statistical Association, 114(527), 1339-1350.
Rinaldo, A., Wasserman, L., and G’Sell, M. (2019). "Bootstrapping and Sample Splitting for High-Dimensional, Assumption-Lean Inference". Annals of Statistics, 47(6), 3438-3469.
Battey, H., Fan, J., Liu, H., Lu, J., and Zhu, Z. (2018). "Distributed Testing and Estimation under Sparse High Dimensional Models". Annals of Statistics, 46(3), 1352.
Jeng, X.J., Peng, H., and Lu, W. (2018). "Post-Lasso Inference for High-Dimensional Regression". Preprint arXiv:1806.06304.
Gautier, E., and Rose, C. (2011). "High-Dimensional Instrumental Variables Regression and Confidence Sets". Preprint arXiv:1105.2454.
Huang, J., Horowitz, J.L., and Ma, S. (2008). "Asymptotic Properties of Bridge Estimators in Sparse High-Dimensional Regression Models". Annals of Statistics, 36(2), 587-613.
III. Statistical Learning Theory
Ballinari, D., and Wehrli, A. (2024). "Semiparametric Inference for Impulse Response Functions using Double/Debiased Machine Learning". Preprint arXiv:2411.10009.
Bonhomme, S., Jochmans, K., and Weidner, M. (2024). "A Neyman-Orthogonalization Approach to the Incidental Parameter Problem". Preprint arXiv:2412.10304.
Wang, Z., Wang, L., and Lian, H. (2024). "Double Debiased Transfer Learning for Adaptive Huber Regression". Scandinavian Journal of Statistics.
Liu, Y., and Molinari, F. (2024). "Inference for an Algorithmic Fairness-Accuracy Frontier". Preprint arXiv:2402.08879.
Li, J., et al. (2024). "Alpha and Prejudice: Improving alpha-sized Worst-Case Fairness via Intrinsic Reweighting". Preprint arXiv:2411.03068.
Chang, J., Chen, X., and Wu, M. (2024). "Central Limit Theorems for High Dimensional Dependent Data". Bernoulli, 30(1), 712-742.
Barber, R.F., Candes, E.J., Ramdas, A., and Tibshirani, R.J. (2023). "Conformal Prediction Beyond Exchangeability". Annals of Statistics, 51(2), 816-845.
Bellec, P.C., and Zhang, C.H. (2023). "Debiasing Convex Regularized Estimators and Interval Estimation in Linear Models". Annals of Statistics, 51(2), 391-436.
Foster, D.J., and Syrgkanis, V. (2023). "Orthogonal Statistical Learning". Annals of Statistics, 51(3), 879-908.
Ganguly, A., and Sutter, T. (2023). "Optimal Learning via Moderate Deviations Theory". Preprint arXiv:2305.14496.
Lin, Z., Trivedi, S., and Sun, J. (2022). "Conformal Prediction Intervals with Temporal Dependence". Preprint arXiv:2205.12940.
Gouic, T.L., Loubes, J.M., and Rigollet, P. (2020). "Projection to Fairness in Statistical Learning". Preprint arXiv:2005.11720.
Egan, M.L., Matvos, G., and Seru, A. (2018). "Arbitration with Uninformed Consumers". National Bureau of Economic Research Working paper (No. w25150).
Zhang, J., and Spirtes, P. L. (2012). "Strong Faithfulness and Uniform Consistency in Causal Inference". Preprint arXiv:1212.2506.
IV. High-Dimensional Randomization Tests
Tuvaandorj, P. (2024). "Robust Permutation Tests in Linear Instrumental Variables Regression". Journal of the American Statistical Association, 1-11.
Zhang, Y., and Zhao, Q. (2023). "What is A Randomization Test?". Journal of the American Statistical Association, 118(544), 2928-2942.
Liu, M., Katsevich, E., Janson, L., and Ramdas, A. (2022). "Fast and Powerful Conditional Randomization Testing via Distillation". Biometrika, 109(2), 277-293.
Wang, W., and Janson, L. (2022). "A High-Dimensional Power Analysis of the Conditional Randomization Test and Knockoffs". Biometrika, 109(3), 631-645.
Wang, R., and Xu, X. (2019). "A Feasible High-Dimensional Randomization Test for the Mean Vector". Journal of Statistical Planning and Inference, 199, 160-178.
McKeague, I. W., and Qian, M. (2015). "An Adaptive Resampling Test for Detecting the Presence of Significant Predictors". Journal of the American Statistical Association, 110(512), 1422-1433.
V. Optimal Subsampling Techniques
Wu, X., Huo, Y., Ren, H., and Zou, C. (2024). "Optimal Subsampling via Predictive Inference". Journal of the American Statistical Association, 119(548), 2844-2856.
Chi, C.M., Vossler, P., Fan, Y., and Lv, J. (2022). "Asymptotic Properties of High-Dimensional Random Forests". Annals of Statistics, 50(6), 3415-3438.
McGrath, S., and Mukherjee, R. (2022). "Nuisance Function Tuning and Sample Splitting for Optimal Doubly Robust Estimation". Preprint arXiv:2212.14857.
Lei, J., and Wasserman, L. (2014). "Distribution-free Prediction Bands for Non-parametric Regression". Journal of the Royal Statistical Society Series B, 76(1), 71-96.
Rudolph, K.E., Díaz, I., Rosenblum, M., and Stuart, E.A. (2014). "Estimating Population Treatment Effects from a Survey Subsample". American Journal of Epidemiology, 180(7), 737-748.
Handouts on Time Series Econometrics and Machine Learning Methods
Handouts on Time Series Econometrics and Machine Learning Methods
Photo Credit: © Christis Katsouris (2014)
Time Series Econometrics
An Introduction to Time Series Econometrics (Graduate, Spring 2022)
Syllabus (Personal Notes)
Lecture 1 (Univariate Models: AR, MA, ARMA, GARCH processes)
Lecture 2 (Multivariate Models: VAR processes)
Lecture 3 (Resampling Methods for time series regression models)
Lecture 4 (Applications: Quantile Autoregression processes)
Supplementary: Appendix I Appendix II
Bibliography:
Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press.
Hendry, D. F. (1995). Dynamic Econometrics. Oxford University Press.
Csörgő, M. (1983). Quantile Processes with Statistical Applications. Society for Industrial and Applied Mathematics.
An Introduction to Probability & Statistics (Undergraduate, Fall 2021)
Syllabus (Personal Notes)
Part I: Distribution Theory Lecture Slides
Part II: Statistical Theory Lecture Slides
Software: Introduction to R
Bibliography:
van der Vaart, A., Jonker, M., and Bijma, F. (2017). An Introduction to Mathematical Statistics. Amsterdam University Press.
Wackerly, D.D. Mendenhall, W. and Scheaffer, R.L (2007). Mathematical Statistics with Applications. Cengage Learning.
An Introduction to Econometrics (Undergraduate, Fall 2017 UCY)
Syllabus (Personal Notes)
Revision Econometrics Notes (in English)
Revision Econometrics Notes (in Greek)
Bibliography:
Wooldridge J. (2020). Introductory Econometrics: A Modern Approach (7th ed.). South-Western College Publishing.
Stock, J. H., and Watson, M. W. (2019). Introduction to Econometrics (4th ed.). Pearson.
Photo Credit: © Christis Katsouris (2013)
Graduate Teaching Assistant
(University of Southampton, 2018 to 2021)
STAT 3010: Statistical Methods in Insurance
(Academic year: 2020/2021)
Module leader: Professor Peter WF Smith
Responsibilities: R Induction, problem sets tutorials.
Computer Workshop in R [Slides]
Problem Set 2: Solutions
Problem Set 3: Solutions
Problem Set 6: Solutions Part 1 Solutions Part 2
Problem Set 7: Solutions
Problem Set 8: Solutions Part 1
STAT 6093: Survey Fundamentals (Academic year: 2020/2021)
Module leader: Associate Professor Yves Berger
Responsibilities: Stata workshops and problem sets tutorials.
Photo Credit: © Christis Katsouris (2010)
ECON 2026: Introduction to Econometrics
(Academic year: 2019/2020)
Module leader: Professor Anastasios Magdalinos
Syllabus: Module Outline
Responsibilities: Delivery of master classes, Stata workshop, office hours, grading.
Stata Workshop: Session 2 (Section A: Stata Examples)
Photo Credit: © Christis Katsouris (2014)
ECON 3015: Principles of Finance
(Academic year: 2019/2020)
Module leader: Associate Professor Antonella Ianni
Syllabus: Module Outline
Responsibilities: Delivery of master classes, office hours, grading.
Chapter 2: Investment Choices under Certainty
Related Literature:
Wenzelburger, J. (2010). "The Two-Fund Separation Theorem Revisited". Annals of Finance, 6, 221-239.
Ng, C. K. (2001). "Similarity Between The Fisher Separation Theorem And The Two-Fund Separation Theorem". Journal of Financial Education, 10-15.
DeAngelo, H. (1981). "Competition and Unanimity". American Economic Review, 71(1), 18-27.
Hirshleifer, J. (1958). "On the Theory of Optimal Investment Decision". Journal of Political Economy, 66(4), 329-352.
Modigliani, F.* (1944). "Liquidity Preference and the Theory of Interest and Money". Econometrica, 12(1), 45-88. * Laureate of the Nobel Memorial Prize in Economics 1985.
Irving, F. (1930). "The Theory of Interest (Fisher's Separation Theorem)".
Chapter 3: Investment Choices under Uncertainty
Related Literature:
Grenadier, S. R., and Wang, N. (2007). "Investment under Uncertainty and Time-inconsistent Preferences". Journal of Financial Economics, 84(1), 2-39.
Yaari, M. E. (1987). "The Dual Theory of Choice under Risk". Econometrica, 55(1), 95-115.
Abel, A. B. (1983). "Optimal Investment under Uncertainty". American Economic Review, 73(1), 228-233.
Machina, M. J. (1982). "Expected Utility Analysis without the Independence Axiom". Econometrica, 50(2), 277-323.
Ross, S. A. (1981). "Some Stronger Measures of Risk Aversion in the Small and the Large with Applications". Econometrica, 49(3), 621-638.
Kahneman, D.* (1979). "Prospect Theory: An Analysis of Decisions under Risk". Econometrica, 47(2), 263-292. * Laureate of the Nobel Memorial Prize in Economics 2002.
Kreps, D. M., and Porteus, E. L. (1978). "Temporal Resolution of Uncertainty and Dynamic Choice Theory". Econometrica, 46(1), 185-200.
Henry, C. (1974). "Investment Decisions under Uncertainty: The Irreversibility Effect". American Economic Review, 64(6), 1006-1012.
Lucas Jr, R. E.*, and Prescott, E. C.** (1971). "Investment under Uncertainty". Econometrica, 39(5), 659-681.
* Laureate of the Nobel Memorial Prize in Economics 1995.
** Laureate of the Nobel Memorial Prize in Economics 2004.
Malinvaud, E. (1969). "First Order Certainty Equivalence". Econometrica, 37(4), 706-718.
Harsanyi, J. C.* (1955). "Cardinal Welfare, Individualistic Ethics, and Interpersonal Comparisons of Utility". Journal of Political Economy, 63(4), 309-321. * Laureate of the Nobel Memorial Prize in Economics 1994.
Chapter 4: Portfolio Theory and Principles of Asset Pricing
Bibliography:
Cochrane, J. H. (2009). Asset Pricing: Revised Edition. Princeton University Press.
Related Literature:
Parker, J. A., and Julliard, C. (2005). "Consumption Risk and the Cross Section of Expected Returns". Journal of Political Economy, 113(1), 185-222.
Campbell, J. Y. (1996). "Understanding Risk and Return". Journal of Political Economy, 104(2), 298-345.
Dow, J., and da Costa Werlang, S. R. (1992). "Uncertainty Aversion, Risk Aversion, and the Optimal Choice of Portfolio". Econometrica, 60(1), 197-204.
Allingham, M. (1991). "Existence Theorems in the Capital Asset Pricing Model". Econometrica, 59(4), 1169-1174.
Green, R. C. (1986). "Benchmark Portfolio Inefficiency and Deviations from the Security Market Line". Journal of Finance, 41(2), 295-312.
Mankiw, N. G., and Shapiro, M. D. (1984). "Risk and Return: Consumption versus Market Beta". NBER Working paper. Available at 10.3386/w1399.
Chamberlain, G. (1983). "A Characterization of the Distributions that imply Mean—Variance Utility Functions". Journal of Economic Theory, 29(1), 185-201.
Chamberlain, G. (1983). "Funds, Factors, and Diversification in Arbitrage Pricing Models". Econometrica, 51(5), 1305-1323.
Goldman, M. B., and Beja, A. (1979). "Market Prices vs. Equilibrium Prices: Returns' Variance, Serial Correlation, and the Role of the Specialist". Journal of Finance, 34(3), 595-607.
Baron, D. P. (1977). "On the Utility Theoretic Foundations of Mean-Variance Analysis". Journal of Finance, 32(5), 1683-1697.
Fama, E. F.*, and MacBeth, J. D. (1973). "Risk, Return, and Equilibrium: Empirical Tests". Journal of Political Economy, 81 (3), 607-636. * Laureate of the Nobel Memorial Prize in Economics 2013.
Merton, R. C.* (1973). "An Intertemporal Capital Asset Pricing Model". Econometrica, 41(5), 867-887. * Laureate of the Nobel Memorial Prize in Economics 1997.
Fama, E. F.* (1970). "Efficient Capital Markets: A Review of Theory and Empirical Work". Journal of Finance, 25(2), 383-417. * Laureate of the Nobel Memorial Prize in Economics 2013.
Sharpe, W. F.* (1964). "Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk". Journal of Finance, 19(3), 425-442. * Laureate of the Nobel Memorial Prize in Economics 1990.
Markowitz, Harry M.* (1952). "Portfolio Selection". Journal of Finance, 7(1), 71-91. * Laureate of the Nobel Memorial Prize in Economics 1990.
Chapter 5: Factor Models of Risk and Arbitrage Pricing
Bibliography:
Campbell, J. Y. (2017). Financial Decisions and Markets: A Course in Asset Pricing. Princeton University Press.
Related Literature:
Fama, E. F.*, and French, K. R. (2015). "A Five-Factor Asset Pricing Model". Journal of Financial Economics, 116(1), 1-22. * Laureate of the Nobel Memorial Prize in Economics 2013.
Chen, L., and Zhang, L. (2010). "A Better Three-Factor Model that Explains More Anomalies". Journal of Finance, 65(2), 563-595.
Hansen, L. P.* , and Jagannathan, R. (1997). "Assessing Specification Errors in Stochastic Discount Factor Models". Journal of Finance, 52(2), 557-590. * Laureate of the Nobel Memorial Prize in Economics 2013.
Fama, E. F.*, and French, K. R. (1996). "Multifactor Explanations of Asset Pricing Anomalies". Journal of Finance, 51(1), 55-84. * Laureate of the Nobel Memorial Prize in Economics 2013.
Fama, E. F.*, and French, K. R. (1992). "The Cross‐Section of Expected Stock Returns". Journal of Finance, 47(2), 427-465. * Laureate of the Nobel Memorial Prize in Economics 2013.
Gibbons, M. R., Ross, S. A., and Shanken, J. (1989). "A Test of the Efficiency of a Given Portfolio". Econometrica, 57(5), 1121-1152.
Fama, E. F.*, and French, K. R. (1988). "Dividend Yields and Expected Stock Returns". Journal of Financial Economics, 22(1), 3-25. * Laureate of the Nobel Memorial Prize in Economics 2013.
Lehmann, B. N., and Modest, D. M. (1988). "The Empirical Foundations of the Arbitrage Pricing Theory". Journal of Financial Economics, 21(2), 213-254.
Engle, R. F.*, Lilien, D. M., and Robins, R. P. (1987). "Estimating Time Varying Risk Premia in the Term Structure: The ARCH-M Model". Econometrica, 55(2), 391-407. * Laureate of the Nobel Memorial Prize in Economics 2003.
Ferson, W. E., Kandel, S., and Stambaugh, R. F. (1987). "Tests of Asset Pricing with Time‐Varying Expected Risk Premiums and Market Betas". Journal of Finance, 42(2), 201-220.
Connor, G., and Korajczyk, R. A. (1986). "Performance Measurement with the Arbitrage Pricing Theory: A New Framework for Analysis". Journal of Financial Economics, 15(3), 373-394.
Trzcinka, C. (1986). "On the Number of Factors in the Arbitrage Pricing Model". Journal of Finance, 41(2), 347-368.
Chamberlain, G. (1983). "Funds, Factors, and Diversification in Arbitrage Pricing Models". Econometrica, 51(5), 1305-1323.
Scholes, M. S.*, and Williams, J. (1977). "Estimating Betas from Nonsynchronous Data". Journal of Financial Economics, 5(3), 309-327. * Laureate of the Nobel Memorial Prize in Economics 1997.
Further Reading
Esponda, I., and Vespa, E. (2024). "Contingent Thinking and the Sure-Thing Principle: Revisiting Classic Anomalies in the Laboratory". Review of Economic Studies, 91(5), 2806-2831.
Ju, N., and Miao, J. (2012). "Ambiguity, Learning, and Asset Returns". Econometrica, 80(2), 559-591.
Pflug, G. C., Pichler, A., and Wozabal, D. (2012). "The 1/N Investment Strategy is Optimal under High Model Ambiguity". Journal of Banking & Finance, 36(2), 410-417.
Persson, T., and Tabellini, G. (2009). "Democratic Capital: The Nexus of Political and Economic Change". American Economic Journal: Macroeconomics, 1(2), 88-126.
Epstein, L. G., and Schneider, M. (2007). "Learning under Ambiguity". Review of Economic Studies, 74(4), 1275-1303.
Klibanoff, P., Marinacci, M., and Mukerji, S. (2005). "A Smooth Model of Decision Making under Ambiguity". Econometrica, 73(6), 1849-1892.
Irwin, D. A., and Klenow, P. J. (1994). "Learning-by-Doing Spillovers in the Semiconductor Industry". Journal of Political Economy, 102(6), 1200-1227.
Preparing for the delivery of the first masterclass for the module 'Principles of Finance', @ Cenetary Building, University of Southampton, October 2019.
Computer Lab Demonstrator (University of Southampton)
Induction to Stata: Workshop for Postgraduates (G), Fall 2018, 2019
STAT 2009: Research Methods in the Social Sciences (U), Fall 2018, 2019
RESM 6007: Quantitative Methods II (G), Spring 2019, 2020
Undergraduate Open Days: Social Sciences Experimental Laboratory (Spring 2018, 2019) with Dr. Zacharias Maniadis and Dr. Helen Paul.
Bibliography (Experimental Laboratory)
Berg, J., Dickhaut, J., & McCabe, K. (1995). Trust, reciprocity, and social history. Games and Economic Behavior, 10(1), 122-142.
Farrell, J., & Maskin, E. (1989). Renegotiation in repeated games. Games and Economic Behavior, 1(4), 327-360.
During the teaching assistant work at UoS I also helped with the marking of exams and assignments for the following modules:
STAT 1003: Introduction to Quantitative Methods (U), Spring 2019
ECON 1008: Mathematics for Economics (U), Fall 2018
ECON 1003: Principles of Microeconomics (U), Fall 2019
ECON 1018: Economics with Experiments (U), Fall 2020
ECON 6016: International Trade (G), Spring 2020
ECON 6044: Corporate Finance (G), Spring 2020
as well as with exam invigilation during the Spring/Fall 2019 examination periods.
Contributions to Teaching (University of Southampton)
Education is instrumental in enabling upward socioeconomic mobility and alleviating poverty traps. In particular, to make scientific processes more transparent, inclusive and democratic (open science) it requires the promotion of interdisciplinary education practices. I am passionate about strengthening further my education leadership using innovative approaches to teaching and curriculum development with potential growing impact both at the national as well as at the international level. Also, I aspire to become Fellow of the Academy of Higher Education which is an essential certificate that demonstrates capabilities and skills of the holder with respect to professionalism in teaching and learning in higher education settings.
A. Selected Feedback for Teaching Activities
Position: Graduate Teaching Assistant in the Department of Economics at the University of Southampton during academic years 2018 to 2021.
Selected Feedback from Undergraduate students during the academic year 2019/2020:
Student 1: “Thank you so much for helping me and everyone else with the Introduction to Econometrics module, this semester. The tutorials were really good and I appreciate all the extra time you spent after class and on email.” (BS.c. in Economics student).
Student 2: “I would like to thank Christis for his tremendous input in reviewing our mock assessment solutions. Moreover, his careful written and specific feedback on my work helped me to understand which areas I need to improve to perform better in the final assessment”. (BS.c. in Economics student).
B. Contributions to Teaching and Learning Environment
(i). Contributions to Teaching Environment:
During my time as a PhD student (January 2018 to July 2022) at the School of Economic, Social and Political Sciences of the University of Southampton I have enthusiastically helped in various teaching activities (teaching, marking, lab demonstrations) and shown academic leadership with my pedagogical style. I provided suggestions on aspects related to student assessment as well as encouraged the transmission of innovative ideas for further restructuring the Course Syllabuses to bridge the gap between the scopes of economic and data sciences. Specifically, I fostered a positive learning environment through research-driven teaching while I actively engaged with students to acquire continuous feedback on my teaching effectiveness for timely manner improvements. I provided constructive written feedback to students assignments making sure that assessment criteria are fairly applied. Overall, I positively contributed in the improvement of students' understanding of the teaching material by giving emphasis on learning objectives while encouraging the development of skills necessary in the modern workplace such as building collaborative teams. Lastly, I participated to focus groups related to lessons learned on effective online-based teaching practices as well as on improving website design effectiveness and functionality.
(ii). Contributions to Teaching Pedagogy:
During my time as a Graduate Teaching Assistant at the University of Southampton, when teaching the master classes of the module 'Introduction to Econometrics' as well previously to the responsibilities of this role, as a Teaching Assistant at the Department of Economics of the University of Cyprus, for the delivery of the Stata tutorials of the equivalent module, I gave emphasis on combining both traditional statistical methods as well as emphasizing the important of applying correctly computational and algorithmic methods for analyzing datasets, which is a teaching practice that contributes in the direction of bridging the gap between the educational objectives and long-term scopes of economic and data sciences.
(iii). Contributions to Students' Recruitment:
During my time as a Graduate Teaching Assistant at the Department of Economics of the University of Southampton, I have actively participated in activities related to Students' Recruitment such as the Economic Open Days for Undergraduate students (Experimental Labs). Furthermore, by enthusiastically helping with students' questions throughout their degree I positively contributed to successful implementation of the Student Life Cycle.
(iv). Contributions to Research:
During my time as a PhD student at the Department of Economics of the University of Southampton, I have actively and enthusiastically participated in both internal as well as external Econometrics workshops and Departmental Seminar Series. Furthermore, throughout my PhD studies I have made several suggestions to the supervisory team, through the preparation of various empirical studies and research proposals, regarding issues in the literature worth further investigation.
Photo Credit: © Christis Katsouris (2016)
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