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Dr Christis Katsouris
BS.c. (Hons, Bath), MS.c. (Warwick), MBA (UCY), MS.c. (UCY), Ph.D. (Southampton); Postdoc at the University of Helsinki.
© Christis G. Katsouris Institute of Econometrics and Data Science
“Our Research Laboratory at the Christis G. Katsouris Institute of Econometrics and Data Science focuses on interdisciplinary research collaborations. We conduct academic research and collaborate across research themes driven by shared values and beliefs such as team curiosity, passion for problem-solving and commitment to pioneering positive impact through the development of novel econometric theory and methods.”
Dr. Christis Katsouris Ph.D., University of Southampton
Photo Credit: © Christis Katsouris (2012)
The Research Laboratory for Econometric Theory and Methods at The Christis G. Katsouris Institute of Econometrics and Data Science was founded by Dr. Christis Katsouris in July 2024.
A full list of our research collaborators can be found in our Collaborations page.
Research Objectives of Econometrics Laboratory
The Research Lab's objective is the development, implementation and evaluation of novel econometric methods for economic and statistical models. We develop econometric estimation and inference procedures using machine learning tools for applications in time series econometrics, panel data econometrics, macroeconometrics and applied macroeconomics. Our Research Lab's research interests are focused on identification and estimation of linear and nonlinear time series and panel data models, structural identification in macroeconometrics, parametric and semiparametric estimation and inference methods for stationary and nonstationary time series and heterogeneous panel data, simulation-based econometric methods as well as causal inference and microdata methods. Further topics include macroeconomic forecasting and machine learning econometrics for dynamic causal effects analysis. We work collaboratively and focus on contribution to the advancement of econometric theory and methods using real and simulated data, with a focus on addressing economic policy-relevant research questions.
Research Areas of Econometrics Laboratory
Econometric Theory and Methods
Time Series and Panel Data Econometrics
Macroeconometrics and Bayesian Econometrics
Causal Inference and High-Dimensional Econometrics
Working Papers of Econometrics Laboratory
[1] Research Article (August 2024)
Title: "Improved Uniform Confidence Interval Estimation for Autoregressive Parameter under Nonstationary Volatility".
Co-author: Dr. Xuewen Yu, Assistant Professor, Fudan University.
[2] Research Article (September 2024)
Title: "Testing for Unobserved Heterogeneity in Dynamic Panel Data Regressions under Diagonal Bilinear Dependence".
Co-author: Dr. Aziz Lmakri, Assistant Professor, Hassan II University of Casablanca.
[3] Research Article (October 2024)
Title: "Shrinkage Estimation in High-Dimensional Panel Data Models with Multiple Structural Breaks under Regressors Sparisty".
Co-author: Dr. Benjamin Poignard, Associate Professor, Keio University.
Research Areas of Econometrics Laboratory
Aspects of Econometric Theory and Asymptotic Statistics
© Christis G. Katsouris Institute of Econometrics and Data Science
A fundamental research domain which facilitates the study of the asymptotic and non-asymptotic properties of machine learning estimators is the so-called statistical learning theory. Suppose we are interested in estimating an unknown univariate function constructed from nonparametric regression models using dependent data. To establish large sample theory we consider the convergence rate of the mean integrated square error of the nonparametric density function estimator, as well as the minimax optimality property of the MISE, which tell us how fast the population mean squared distance between the estimator and the unknown function f shrinks to zero uniformly, when this function ranges over a smoothness class, as the sample size increases.
Statistical learning theory with dependent data is a growing body of research with many applications in the structural econometrics and nonparametric econometrics literature. Further aspects of interest include establishing econometric theory for machine learning estimators, high-dimensional and nonlinear optimisation techniques, and numerical methods for solving heterogeneous agent models in continuous-time settings. Therefore, studying the finite and large sample properties of these econometric methods worth further study. Particular interest is the case when inference is fragile.
Literature Review:
> Statistical Theory and Asymptotic Statistics
Schafgans, M., and Zinde-Walsh, V. (2026). "Multivariate Kernel Regression in Vector and Product Metric Spaces". Journal of Econometrics, 253, 106168.
Amorino., C., Brownlees, C., and Ghosh, A. (2025). "Concentration Inequalities for Suprema of Empirical Processes with Dependent Data via Generic Chaining with Applications to Statistical Learning". Preprint arXiv:2511.00597.
Derumigny, A., Girard, L., and Guyonvarch, Y. (2025). "Can We Have It All? Non-asymptotically Valid and Asymptotically Exact Confidence Intervals for Expectations and Linear Regressions". Preprint arXiv:2507.16776.
Cattaneo, M. D., Cox, G.F., Jansson, M., and Nagasawa, K. (2025). "Continuity of the Distribution Function of the Argmax of a Gaussian Process". Preprint arxiv:2501.13265.
Tan, F., Guo, X., and Zhu, L. (2025). "Weighted Residual Empirical Processes, Martingale Transformations, and Model Specification Tests for Regressions with Diverging Number of Parameters". Journal of Econometrics, 252, 106113.
Yuan, Z., and Spindler, M. (2025). "Bernstein-type Inequalities and Nonparametric Estimation under Near-Epoch Dependence". Journal of Econometrics, 251, 106054.
Cordoni, F., and Sancetta, A. (2024). "Consistent Causal Inference for High-Dimensional Time Series". Journal of Econometrics, 246(1-2), 105902.
Zhu, Y. (2024). "Phase Transitions in Nonparametric Regressions". Journal of Econometrics, 105640.
Chandrasekhar, A. G., Jackson, M. O., McCormick, T. H., and Thiyageswaran, V. (2023). "General Covariance-based Conditions for Central Limit Theorems with Dependent Triangular Arrays". Preprint arXiv:2308.12506.
Kolesár, M., Müller, U. K., and Roelsgaard, S. T. (2023). "The Fragility of Sparsity". Preprint arXiv:2311.02299.
Katsouris, C. (2023). "High dimensional Time Series Regression Models: Applications to Statistical Learning Methods". Preprint arXiv:2308.16192.
> Statistical Theory and Methods
Lan, H., et al. (2025). "The Bias-Variance Trade-off in Data-Driven Optimization: A Local Misspecification Perspective". Preprint arXiv:2510.18215.
Shen, Z., Wang, C., Wang, S., and Yan, Y. (2025). "High-Dimensional Differentially Private Quantile Regression: Distributed Estimation and Statistical Inference". Preprint arXiv:2508.05212.
Li, S., and Zhang, L. (2024). "Fairm: Learning Invariant Representations for Algorithmic Fairness and Domain Generalization with Minimax Optimality". Preprint arXiv:2404.01608.
Bilodeau, B., Negrea, J., and Roy, D. M. (2023). "Relaxing the iid Assumption: Adaptively Minimax Optimal Regret via Root-Entropic Regularization". Annals of Statistics, 51(4), 1850-1876.
Chzhen, E., and Schreuder, N. (2022). "A Minimax Framework for Quantifying Risk-Fairness Trade-Off in Regression". Annals of Statistics, 50(4), 2416-2442.
Chen, S. X., and Peng, L. (2021). "Distributed Statistical Inference for Massive Data". Annals of Statistics, 49(5), 2851-2869.
Jordan, M. I., Lee, J. D., and Yang, Y. (2019). "Communication-Efficient Distributed Statistical Inference". Journal of the American Statistical Association, 114(526), 1-39.
Aspects of Structural Econometrics for Macroeconomics and Finance
© Christis G. Katsouris Institute of Econometrics and Data Science
A second fundamental research domain in econometrics which focuses on the development of econometric theory and methods for structural econometric models relies on the so-called information-theoretic approach. Relevant research aspects include the optimality properties of GMM estimators, sequentially iterated GMM, empirical GMM, their convergence rates and the efficiency of optimal procedures such as simulation-based estimators for density functions (e.g., see the simulation-based estimator of the quantile density function proposed by Liao, Li & Fan (2024, arXiv:2410.03557)).
Recently, there is a growing interest in econometric methods for data-rich environments. A suggestion to extend the GMM with gaussian kernel functionals in the case of infinite-dimensional regressors was discussed during the presentation of C. Katsouris (2024). For example, model selection procedures rely on non-nested testing for competitive models against alternative models by evaluating the functional form specification using a sequence of local alternatives. Typically econometric inference is based on non-nested conditional moment restrictions. Moreover, estimation methods for the parameters of econometric models rely on empirical moment matching with suitably defined orthogonal moment conditions.
Literature Review:
Cho, J. S., and Phillips, P.C.B. (2025). "GMM Estimation with Brownian Kernels Applied to Income Inequality Measurement". Journal of Econometrics, 252, 106110.
Hong, H., and Li, J. (2025). "Rate-Adaptive Bootstrap for Possibly Misspecified GMM". Econometric Theory, 41(2), 421-471.
Katsouris C. (2024). "Econometrics Presentation with an Application to Estimating Heterogeneous Treatment Effects at the Quantiles". Interview for the position of Lecturer in Econometrics, Department of Economics, University of Manchester, (28th May 2024).
Liao, X., Li, X., and Fan, Q. (2024). "Robust Bond Risk Premia Predictability Test in the Quantiles". Preprint arXiv:2410.03557.
Chernozhukov, V., Newey, W.K., and Santos, A. (2023). "Constrained Conditional Moment Restriction Models". Econometrica, 91(2), 709-736.
Kleibergen, F., Kong, L., and Zhan, Z. (2023). "Identification Robust Testing of Risk Premia in Finite Samples". Journal of Financial Econometrics, 21(2), 263-297.
Sueishi, N. (2016). "A Simple Derivation of the Efficiency Bound for Conditional Moment Restriction Models". Economics Letters, 138, 57-59.
Aspects of Macroeconometrics and Bayesian Econometrics
© Christis G. Katsouris Institute of Econometrics and Data Science
A third research area of interest is the econometric identification and estimation of structural dynamic economic models, with a focus on methods for structural macroeconometrics. This approach has two important benefits: first it allow us to obtain suggestive explanations of economic phenomena rather than to validate or falsify existing theories, and second such a statistical decision theory approach, provides the means for developing econometric methodologies which are robust to data-driven stylized facts. For example, the classical Gaussian assumption has been found an inadequate mechanism for identifying Structural Vector Autoregressive models, especially when using time series data with heavy tails.
Developing identification and estimation methods that incorporate the properties of non-Gaussian time series provides a way to conduct pragmatic empirical research such as modelling the impact of climate shocks to the economy. Moreover, the identification of macroeconomic new shocks, such as inflation expectations, using the non-Gaussianity property can provide a mechanism for robust estimation of counterfactual opinion forecasts.
Literature Review:
Dzikowski, D., and Jentsch, C. (2025). "Structural Periodic Vector Autoregressions". Journal of Econometrics, 252, 106099.
Collard, F., Feve, P., and Guay, A. (2024). "Believe it or Not, it's All About the Beliefs!". CEPR Discussion Paper (No. 19103). Available at https://hdl.handle.net/10419/301938.
Goto, Y., and Hallin, M. (2024). "A Model-free Test of the Time-Reversibility of Climate Change Processes". Preprint arXiv:2411.11248.
Hoesch, L., Lee, A., and Mesters, G. (2024). "Locally Robust Inference for Non‐Gaussian SVAR Models". Quantitative Economics, 15(2), 523-570.
Braun, R., and Brüggemann, R. (2023). "Identification of SVAR Models by Combining Sign Restrictions with External Instruments". Journal of Business & Economic Statistics, 41(4), 1077-1089.
Krampe, J., Paparoditis, E., and Trenkler, C. (2023). "Structural Inference in Sparse High-Dimensional Vector Autoregressions". Journal of Econometrics, 234(1), 276-300.
Katsouris, C. (2023). "Structural Analysis of Vector Autoregressive Models". Preprint arXiv:2312.06402.
Lanne, M., and Luoto, J. (2021). "GMM Estimation of Non-Gaussian Structural Vector Autoregression". Journal of Business & Economic Statistics, 39(1), 69-81.
Lanne, M., Meitz, M., and Saikkonen, P. (2017). "Identification and Estimation of Non-Gaussian Structural Vector Autoregressions". Journal of Econometrics, 196(2), 288-304.
Aspects of Simulation-based Econometrics and Machine Learning
© Christis G. Katsouris Institute of Econometrics and Data Science
This research theme concentrate on econometric estimation and inference procedures using machine learning tools. In particular, robust estimation and inference in the presence of missing data, requires the development of novel methods which account for possibly complex missing patterns and dependencies in the data. For example, econometric inference for data-rich environments (such as large cross sections with firm fundamentals), imply that conventional imputation methods, that typically work well in i.i.d settings are no longer valid, rendering classical inference for panel data with cross sectional dependence as well as inference in efficient-frontier panel models unreliable, when the data missingness is not addressed using econometric techniques.
Literature Review:
A. Simulation-based Econometric Estimation and Inference
Chen, X., et al. (2025). "SGMM: Stochastic Approximation to Generalized Method of Moments". Journal of Financial Econometrics, 23(1), nbad027.
Kelly, R. P., et al. (2025). "Simulation-based Bayesian Inference under Model Misspecification". Preprint arXiv:2503.12315.
Forneron, J. J. (2024). "Estimation and Inference by Stochastic Optimization". Journal of Econometrics, 238(2), 105638.
Geweke, J., and Durham, G. (2019). "Sequentially Adaptive Bayesian Learning Algorithms for Inference and Optimization". Journal of Econometrics, 210(1), 4-25.
Forneron, J. J., and Ng, S. (2018). "The ABC of Simulation Estimation with Auxiliary Statistics". Journal of Econometrics, 205(1), 112-139.
Bianchi, F. (2012). "Regime Switches, Agents' Beliefs, and Post-World War II U.S. Macroeconomic Dynamics". Review of Economic Studies, 80(2), 463-490.
Altissimo, F., and Mele, A. (2009). "Simulated Non-Parametric Estimation of Dynamic Models". Review of Economic Studies, 76(2), 413-450.
Storesletten, K., Telmer, C. I., and Yaron, A. (2004). "Cyclical Dynamics in Idiosyncratic Labor Market Risk". Journal of Political Economy, 112(3), 695-717.
B. Econometric Methods with Machine Learning
> Generalized Linear Models
Caner, M. (2023). "Generalized Linear Models with Structured Sparsity Estimators". Journal of Econometrics, 236(2), 105478.
Chen, X., Li, H., and Zhang, J. (2023). "Complete Subset Averaging Approach for High-Dimensional Generalized Linear Models". Economics Letters, 226, 111084.
> Double Machine Learning Estimation and Inference
Baybutt, A., and Navjeevan, M. (2026). "Doubly-Robust Inference for Conditional Average Treatment Effects with High-Dimensional Controls". Journal of Econometrics, 253, 106180.
Zhang, L. Z. (2025). "Continuous Difference-in-Differences with Double/Debiased Machine Learning". Econometrics Journal, utaf024.
Bia, M., Huber, M., and Lafférs, L. (2024). "Double Machine Learning for Sample Selection Models". Journal of Business & Economic Statistics, 42(3), 958-969.
Liu, L., Mukherjee, R., and Robins, J. M. (2024). "Assumption-Lean Falsification Tests of Rate Double-Robustness of Double-Machine-Learning Estimators". Journal of Econometrics, 240(2).
Rafi, A. (2023). "Efficient Semiparametric Estimation of Average Treatment Effects under Covariate Adaptive Randomization". Preprint arXiv:2305.08340.
> Causal Inference with Non-Response/Attrition/Censoring/Missing Data
Ober-Reynolds, D. (2026). "Robustness to Missing Data: Breakdown Point Analysis". Journal of Econometrics, 253, 106151.
Bellégo, C., Benatia, D., and Dortet-Bernadet, V. (2025). "The Chained Difference-in-Differences". Journal of Econometrics, 248, 105783.
Fan, Y., Pass, B., and Shi, X. (2025). "Partial Identification in Moment Models with Incomplete Data via Optimal Transport". Preprint arXiv:2503.16098.
Gobillon, L., Magnac, T., and Roux, S. (2025). "Life-Cycle Wages and Human Capital Investments: Selection and Missing Data". Review of Economic Studies, rdaf099.
DellaVigna, S., Heining, J., Schmieder, J. F., and Trenkle, S. (2022). "Evidence on Job Search Models from a Survey of Unemployed Workers in Germany". Quarterly Journal of Economics, 137(2), 1181-1232.
Van den Berg, G. J., and Vikström, J. (2022). "Long‐Run Effects of Dynamically Assigned Treatments: A New Methodology and an Evaluation of Training Effects on Earnings". Econometrica, 90(3), 1337-1354.
Fricke, H., Frölich, M., Huber, M., and Lechner, M. (2020). "Endogeneity and Non‐Response Bias in Treatment Evaluation–Nonparametric Identification of Causal Effects by Instruments". Journal of Applied Econometrics, 35(5), 481-504.
Abrevaya, J. (2019). "Missing Dependent Variables in Fixed-Effects Models". Journal of Econometrics, 211(1), 151-165.
Nguimkeu, P., Denteh, A., and Tchernis, R. (2019). "On the Estimation of Treatment Effects with Endogenous Misreporting". Journal of Econometrics, 208(2), 487-506.
Zhao, P., Zhao, H., Tang, N., and Li, Z. (2017). "Weighted Composite Quantile Regression Analysis for Nonignorable Missing Data using Nonresponse Instrument". Journal of Nonparametric Statistics, 29(2), 189-212.
Chen, X., Chernozhukov, V., Lee, S., and Newey, W. K. (2014). "Local Identification of Nonparametric and Semiparametric Models". Econometrica, 82(2), 785-809.
Kline, P., and Santos, A. (2013). "Sensitivity to Missing Data Assumptions: Theory and an Evaluation of the US Wage Structure". Quantitative Economics, 4(2), 231-267.
Graham, B. S. (2011). "Efficiency Bounds for Missing Data Models with Semiparametric Restrictions". Econometrica, 79(2), 437-452.
C. Estimation and Inference with Missing Time Series Data
Duan, J., and Pelger, M. (2025). "Imputation-Powered Inference for Missing Covariates". NBER Working Paper (No. w34535). Available at nber/w34535.
Su, L., and Wang, F. (2025). "Inference for Large Dimensional Factor Models under General Missing Data Patterns". Journal of Econometrics, 250, 106022.
Bryzgalova, S., Lerner, S., Lettau, M., and Pelger, M. (2024). "Missing Financial Data". Available at SSRN 4106794.
Cahan, E., Bai, J., and Ng, S. (2023). "Factor-based Imputation of Missing Values and Covariances in Panel Data of Large Dimensions". Journal of Econometrics, 233(1), 113-131.
Chan, J. C., Poon, A., and Zhu, D. (2023). "High-Dimensional Conditionally Gaussian State Space Models with Missing Data". Journal of Econometrics, 236(1), 105468.
Athey, S., Bayati, M., Doudchenko, N., Imbens, G., and Khosravi, K. (2021). "Matrix Completion Methods for Causal Panel Data Models". Journal of the American Statistical Association, 116(536), 1716-1730.
Bai, J., and Ng, S. (2021). "Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data". Journal of the American Statistical Association, 116(536), 1746-1763.
Blasques, F., Gorgi, P., and Koopman, S. J. (2021). "Missing Observations in Observation-Driven Time Series Models". Journal of Econometrics, 221(2), 542-568.
Rho, S. H., and Vogelsang, T. J. (2019). "Heteroskedasticity Autocorrelation Robust Inference in Time Series Regressions with Missing Data". Econometric Theory, 35(3), 601-629.
Coding Examples
Time Series Econometrics
Applied Econometrics
Bibliography: Econometric and Statistical Theory
Time Series Analysis
James D. Hamilton: Time Series Analysis.
Wayne A. Fuller: Introduction to Statistical Time Series.
Shumway and Stoffer: Time Series Analysis and its Applications.
Douc, Moulines and Stoffer: Nonlinear Time Series: Theory, Methods and Applications.
Econometric Theory
Hayashi, F.: Econometrics.
James Davidson: Econometric Theory.
Davidson R. and MacKinnon J.G: Econometric Theory and Methods.
Phoebus J. Dhrymes: Mathematics for Econometrics.
Halbert White: Asymptotic Theory for Econometricians.
Badi H. Baltagi (Edited by): Theoretical Econometrics.
George G. Judge et al: The Theory and Practice of Econometrics.
Michio Hatanaka: Time-Series Based Econometrics.
Lars P. Hansen and Thomas J. Sargent: Rational Expectations Econometrics.
Phillips P.C.B: Lecture Notes on Nonstationary Time Series (1988, 1995).
Statistical Theory
Hastie, Tibshirani and Friedman: The Elements of Statistical Learning.
James, Witten, Hastie, Tibshirani: An Introduction to Statistical Learning.
Lucien Le Cam: Asymptotic Methods in Statistical Decision Theory.
Bickel and Doksum: Mathematical Statistics.
van der Vaart A.W.: Asymptotic Statistics.
van de Geer S. and Bühlmann: Statistics for High-Dimensional Data.
Johnson and Wichern: Applied Multivariate Statistical Analysis.
Reinsel and Velu: Multivariate Reduced-Rank Regression.
Robert J. Serfling: Approximation Theorems of Mathematical Statistics.
Horvth Lajos and Piotr Kokoszka: Inference for Functional Data with Applications.
Aneiros, Horová, Hušková, and Vieu: Functional and High-Dimensional Statistics and Related Fields.
Probability Theory
Chow and Teicher: Probability Theory.
Billingsley P.: Probability and Measure, Convergence of Probability Measures.
Pollard D.: Convergence of Stochastic Processes.
Stout W.F.: Almost sure convergence.
Ward Whitt: Stochastic Process Limits.
Davidson J.: Stochastic Limit Theory.
Hall and Heyde: Martingale Limit Theory and its Application.
Van der Vaart A.W. and Wellner: Weak Convergence and Empirical Processes.
Häusler and Luschgy: Stable Convergence and Stable Limit Theorems.
Csörgö and Révész: Strong Approximations in Probability and Statistics.
Csörgö and Horváth. Limit Theorems in Change-Point Analysis.
Jacod and Shiryaev: Limit Theorems for Stochastic Processes.
Hans Crauel: Random Probability Measures on Polish Spaces.
Andreas E. Kyprianou: Fluctuations of Lévy Processes with Applications.
Gennady Samorodnitsky: Stochastic Processes and Long Range Dependence.
Applied Probability Theory
Haan and Ferreira: Extreme Value Theory.
Embrechts, Klüppelberg and Mikosch: Modelling Extremal Events.
Stuart Coles: An Introduction to Statistical Modeling of Extreme Values.
Snyder and Miller: Random Point Processes in Time and Space.
Sidney I. Resnick: Heavy-Tail Phenomena. Probabilistic and Statistical Modeling.
John P.Nolan: Univariate Stable Distributions: Models for Heavy Tailed Data.
Bibliography: Stochastic Calculus
Stochastic Finance and Financial Engineering Theory
Dana and Jeanblanc: Financial Markets in Continuous time.
Karatzas and Shreve: Brownian Motion and Stochastic Calculus.
Cont and Tankov: Financial Modelling with Jump Processes.
Philip E. Protter: Stochastic Integration and Differential Equations.
Ikeda and Watanabe: Stochastic Differential Equations and Diffusion Processes.
Ruppert and Matterson: Statistics and Data Analysis for Financial Engineering.
Resampling Methods
Chernick: Bootstrap Methods.
Good: Permutation, Parametric, and Bootstrap Tests of Hypotheses.
A.D.Davison and D.V.Hinkley: Bootstrap Methods and their Applications.
Peter Hall: The Bootstrap and Edgeworth Expansion.
Economic, Financial and Actuarial Indicators
The VIX indicator tracks expected volatility of the US stock market and is a measure of investor sentiment and asset pricing dynamics. The index is constructed using option pricing and market micro-structure theories and modelling techniques.
The presence of volatility fluctuations has motivated researchers to investigate the macroeconomic factors which explain these fluctuations as well as to propose novel ways to predict and test the predictive accuracy of forecasts obtained from econometric models conditional on the volatility uncertainty paradigm.
The Economic Policy Uncertainty Index has been developed by Baker et al. (2009, QJE). The index is constructed based on machine learning techniques and textual analysis which captures sentiment regarding the uncertainty in economic policy making.
The Economic Policy Uncertainty Index has been developed by Baker et al. (2009, QJE). The index is constructed based on machine learning techniques and textual analysis which captures sentiment regarding the uncertainty in economic policy making.
The EUI has motivated researchers to investigate the transmission mechanism (i.e., spillover effects and causal linkage) of uncertainty shocks to macroeconomic variables. Recent studies have documented the presence of nonlinear dynamics and asymmetric effects over the business cycle.
BTC Price Index
BTC Price Index
The Bitcoin price index monitors the price of cryptocurrencies, a worldwide payment system which enables two willing online parties to transact directly with each other without relying on a financial intermediary. Transactions are recorded in a publicly distributed ledger called a blockchain.
The Bitcoin price index monitors the price of cryptocurrencies, a worldwide payment system which enables two willing online parties to transact directly with each other without relying on a financial intermediary. Transactions are recorded in a publicly distributed ledger called a blockchain.
The strong volatility experienced by cryptocurrency exchanges has motivated researchers to investigate the existence of speculative bubbles, defined as a “persistent, systematic divergence of market price of a security from its fundamental value”.
The strong volatility experienced by cryptocurrency exchanges has motivated researchers to investigate the existence of speculative bubbles, defined as a “persistent, systematic divergence of market price of a security from its fundamental value”.
References:
Katsouris, C. (2021). "Forecast evaluation in Large Cross-Sections of Realized Volatility". Preprint arXiv:2112.04887.
Bekaert, G., and Hoerova, M. (2014). "The VIX, the Variance Premium and Stock Market Volatility". Journal of Econometrics, 183(2), 181-192.
Paye, B. S. (2012). "‘Déjà vol’: Predictive Regressions for Aggregate Stock Market Volatility using Macroeconomic Variables". Journal of Financial Economics, 106(3), 527-546.
References:
Ahir, H., Bloom, N., and Furceri, D. (2022). "The World Uncertainty Index". National Bureau of Economic Research Working paper (No. w29763).
Baker, S. R., Bloom, N., and Davis, S. J. (2016). "Measuring Economic Policy Uncertainty". Quarterly Journal of Economics, 131(4), 1593-1636.
Davis, S. J. (2016). "An index of Global Economic Policy Uncertainty". National Bureau of Economic Research Working paper (No. w22740).
References:
Phillips, P.C.B., and Shi, S. (2020). "Real time Monitoring of Asset Markets: Bubbles and crises". In Handbook of Statistics (Vol. 42, pp. 61-80). Elsevier.
Horváth, L., Liu, Z., Rice, G., and Wang, S. (2020). "Sequential Monitoring for Changes from Stationarity to Mild non-stationarity". Journal of Econometrics, 215(1), 209-238.
Katsouris, C. (2017). "Sequential break-point detection in stationary time series: An application to monitoring economic indicators". Preprint arXiv:2112.06889.
The GAT time series correspond to temperature anomalies since the main objective is to track changes in temperature. These temperature anomalies are calculated as the difference between the observed temperature and the long-term average temperature for each location and date.
The index is an objective measure of observed changes in extreme weather and sea levels which allows to monitor climate trends. It is updated quarterly using meteorological season data. Such observations can be employed as a proxy of weather shocks into macroeconometric models.
References:
Bilal, A., and Känzig, D. R. (2026). "The Macroeconomic Impact of Climate Change: Global Versus Local Temperature". Quarterly Journal of Economics, qjag011.
Kim, H. S., Matthes, C., and Phan, T. (2022). "Severe Weather and the Macroeconomy". Federal Reserve Bank of Richmond Working Papers, 21-14R.
Acevedo et al. (2020). "The effects of Weather Shocks on Economic Activity: What are the channels of impact?". Journal of Macroeconomics, 65, 103207.
Gallic, E., and Vermandel, G. (2020). "Weather Shocks". European Economic Review, 124, 103409.
References:
Araujo, R. (2026). "When Clouds Go Dry: An Integrated Model of Deforestation, Rainfall, and Agriculture". Journal of Political Economy (forthcoming).
Ciccarelli, M., and Marotta, F. (2024). "Demand or Supply? An Empirical Exploration of the Effects of Climate Change on the Macroeconomy". Energy Economics, 129, 107163.
Lanne, M., and Virolainen, S. (2024). "A Gaussian Smooth Transition Vector Autoregressive Model: An application to the Macroeconomic Effects of Severe Weather Shocks". Preprint arXiv:2403.14216.
References:
Barrage, L. (2020). "Optimal Dynamic Carbon Taxes in a Climate–Economy Model with Distortionary Fiscal Policy". Review of Economic Studies, 87(1), 1-39.
Osotimehin, S. (2019). "Aggregate Productivity and the Allocation of Resources over the Business Cycle". Review of Economic Dynamics, 32, 180-205.
Mayer, E., Rüth, S., and Scharler, J. (2016). "Total factor productivity and the Propagation of Shocks: Empirical evidence and implications for the Business Cycle". Journal of Macroeconomics, 50, 335-346.
Econometric Applications for Macro, Financial and Climate Time Series Data
Measuring the impact of climate change to the economy, designing policies that promote economic development and growth, as well as examining the links between inequality and the joint distribution of income, consumption and wealth requires novel econometric theory and methods, due to the presence of large shocks and data-specific features that can invalidate conventional inference procedures.
Dynamics of Economic and Financial Processes: such as the modelling of financial networks using econometric models in which the functional form is specified based on the network topology. Applications in economics and finance include the econometric analysis of systemic risk, financial contagion and spillover effects from the oil market to the stock market for example. Moreover, structural identification and estimation in the presence of large shocks (such as climate-induced shocks), is of particular interest to academics, policymakers and practitioners. Further econometric applications include methods for change-point detection using nonstationary time series data (break-date recovery), as well as model calibration and parameter estimation for both nearly unstable and mildly explosive processes. For instance, financial time series exhibit mildly explosivity during phases of the business cycle with faster-than-expected growth. Therefore, the presence of such features in economic time series require the development of robust and asymptotically uniform inference procedures.
Dynamics of Environmental and Ecological Processes: includes the time series analysis and modelling of oscillation sequences which have applications when considering the large sample properties of nearly, mildly and purely unstable processes. Moreover, econometric estimation and inference methods for temporally dependent data rely on the assumption that functional form specifications approximate the underline stochastic processes that characterise epidemiological and environmental phenomena of interest, such as the temporal dynamics of infection spreading, biodiversity loss, as well as the impact of climate change to the economy, among others.
© Christis G. Katsouris Institute of Econometrics and Data Science
Photo Credit: © Christis Katsouris (2009)
“A transition from a fragmentation and compartmentalisation mode of knowledge to an interdisciplinary approach where methodological reasoning and human values co-exist harmonously, is a driving-force for new thinking.”
Dr Christis Katsouris, Ph.D. University of Southampton
References:
A. Econometric Analysis of Macrofinance Risk
Chen, S., and Schienle, M. (2024). "Large Spillover Networks of Nonstationary Systems". Journal of Business & Economic Statistics, 42(2), 422-436.
Bykhovskaya, A. (2022). "Time Series Approach to the Evolution of Networks: Prediction and Estimation". Journal of Business & Economic Statistics, 41(1), 170-183.
Katsouris, C. (2021). "Optimal Portfolio Choice and Stock Centrality for Tail Risk Events". Preprint arXiv:2112.12031.
Cited by: Hatami, Z. (2022). "A New Approach for Analyzing Financial Markets using Correlation Networks and Population Analysis". PhD thesis. University of Nebraska at Omaha.
Bräuning, F., and Koopman, S. J. (2020). "The Dynamic Factor Network Model with an Application to International Trade". Journal of Econometrics, 216(2), 494-515.
Hale, G., and Lopez, J.A. (2019). "Monitoring Banking System Connectedness with Big Data". Journal of Econometrics, 212(1), 203-220.
Baruník, J., and Křehlík, T. (2018). "Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk". Journal of Financial Econometrics, 16(2), 271-296.
Demirer, M., Diebold, F.X., Liu, L., and Yilmaz, K. (2018). "Estimating Global Bank Network Connectedness". Journal of Applied Econometrics, 33(1), 1-15.
Blasques, F., Koopman, S.J., Lucas, A., and Schaumburg, J. (2016). "Spillover Dynamics for Systemic Risk Measurement using Spatial Financial Time Series Models". Journal of Econometrics, 195(2), 211-223.
Härdle, W. K., Wang, W., and Yu, L. (2016). "Tenet: Tail-Event Driven Network Risk". Journal of Econometrics, 192(2), 499-513.
Diebold, F. X., and Yılmaz, K. (2014). "On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms". Journal of Econometrics, 182(1), 119-134.
Aktekin, T., Soyer, R., and Xu, F. (2013). "Assessment of Mortgage Default Risk via Bayesian State Space Models". Annals of Applied Statistics, 1450-1473.
Koopman, S.J., Lucas, A., and Schwaab, B. (2011). "Modeling Frailty-Correlated Defaults using Many Macroeconomic Covariates". Journal of Econometrics, 162(2), 312-325.
Hong, Y., Liu, Y., and Wang, S. (2009). "Granger Causality in Risk and Detection of Extreme Risk Spillover between Financial Markets". Journal of Econometrics, 150(2), 271-287.
B. Structural Breaks and Dynamic Misspecification
Ling, B., and Tu, Y. (2026). "High-Dimensional Banded Vector Autoregressions Subject to Structural Breaks". Econometric Reviews, 1-27.
Fujimori, K., and Tsukuda, K. (2025). "Two-Step Estimations via the Dantzig Selector for Models of Stochastic Processes with High-Dimensional Parameters". Stochastic Processes and their Applications, 104809.
Andrews, D. W., and Kwon, S. (2024). "Misspecified Moment Inequality Models: Inference and Diagnostics". Review of Economic Studies, 91(1), 45-76.
Lin, Y., Poignard, B., Pong, T. K., and Takeda, A. (2024). "Break Recovery in Graphical Networks with D-trace Loss". Preprint arXiv:2410.04057.
Xu, H., Wang, D., Zhao, Z., and Yu, Y. (2024). "Change-Point Inference in High-Dimensional Regression Models under Temporal Dependence". Annals of Statistics, 52(3), 999-1026.
Xu, W. (2022). "Testing for Time-Varying Factor Loadings in High-Dimensional Factor Models". Econometric Reviews, 41(8), 918-965.
Koo, B., and Seo, M. H. (2015). "Structural-Break Models under Mis-specification: Implications for Forecasting". Journal of Econometrics, 188(1), 166-181.
Chan, N. H., Yau, C. Y., and Zhang, R. M. (2014). "Group LASSO for Structural Break Time Series". Journal of the American Statistical Association, 109(506), 590-599.
C. Macroeconomic Forecasting and Forecast Evaluation
Fu, Z., Su, L., and Wang, X. (2025). "Distinguishing Time-Varying Factor Models". Journal of Business & Economic Statistics, 43(3), 508-519.
Rolla, L. M., and Giovannelli, A. (2025). "Macroeconomic Forecasting using Factor Models with Martingale Difference Errors". International Journal of Forecasting.
Cui, X., Gafarov, B., Ghanem, D., and Kuffner, T. (2024). "On Model Selection Criteria for Climate Change Impact Studies". Journal of Econometrics, 239(1), 105511.
Arias, J. E., Rubio-Ramirez, J. F., and Shin, M. (2023). "Macroeconomic Forecasting and Variable Ordering in Multivariate Stochastic Volatility Models". Journal of Econometrics, 235(2), 1054-1086.
Odendahl, F., Rossi, B., and Sekhposyan, T. (2023). "Evaluating Forecast Performance with State Dependence". Journal of Econometrics, 237(2), 105220.
Li, J., Liao, Z., and Quaedvlieg, R. (2022). "Conditional Superior Predictive Ability". Review of Economic Studies, 89(2), 843-875.
Phillips, P.C.B., Leirvik, T., and Storelvmo, T. (2020). "Econometric Estimates of Earth’s Transient Climate Sensitivity". Journal of Econometrics, 214(1), 6-32.
Pretis, F. (2020). "Econometric Modelling of Climate Systems: The Equivalence of Energy Balance Models and Cointegrated Vector Autoregressions". Journal of Econometrics, 214(1), 256-273.
Wagner, M., Grabarczyk, P., and Hong, S. H. (2020). "Fully Modified OLS Estimation and Inference for Seemingly Unrelated Cointegrating Polynomial Regressions and the Environmental Kuznets Curve for Carbon Dioxide Emissions". Journal of Econometrics, 214(1), 216-255.
Research Theme:
Machine Learning Methods and Asymptotic Statistics Theory
The use of machine learning and high-dimensional methods in econometrics and statistics allow to conduct causal inference with suitably specified functional forms. Moreover, machine learning methods with continuously updating steps allow for asymptotically efficient estimation with desirable finite and large sample properties that worth further study.
Reading Group:
References:
Asymptotic Statistics: Theory and Methods
Brown, C. (2025). "Statistical Properties of Deep Neural Networks with Dependent Data". Preprint arXiv:2410.11113.
Do, Q.H., Nguyen, B.T., and Ho, L.S.T. (2024). "A Generalization Bound of Deep Neural Networks for Dependent Data". Statistics & Probability Letters, 208, 110060.
Kaji, T., Manresa, E., and Pouliot, G. (2023). "An Adversarial Approach to Structural Estimation". Econometrica, 91(6), 2041-2063.
Metzger, J. (2022). "Adversarial Estimators". Preprint arXiv:2204.10495.
Farrell, M. H., Liang, T., and Misra, S. (2021). "Deep Neural Networks for Estimation and Inference". Econometrica, 89(1), 181-213.
During a Hybrid Seminar at Statistical Sciences Research Institute, University of Southampton (9th December 2021).
[Seminar Speaker: Dr. Sebastian Engelke, University of Geneve].
During a Hybrid Seminar at Statistical Sciences Research Institute, University of Southampton (24th March 2022).
[Seminar Speaker: Dr. Haeren Cho, University of Bristol].
During a Hybrid Seminar at Statistical Sciences Research Institute, University of Southampton (November 2022).
[Seminar Speaker: Marvin Pförtner, PhD student, University of Tübingen].
During a Hybrid Seminar at Statistical Sciences Research Institute, University of Southampton (11th November 2022).
[Seminar Speaker: Dr. Henry Reeve, University of Bristol].
During the Young Statisticians Europe Online Workshop: "Recent Challenges in Model Specification Testing based on Different Data Structures" (9th November 2022).
During the Young Statisticians Europe Online Workshop: "Recent Challenges in Model Specification Testing based on Different Data Structures" (9th November 2022).
During an Econometrics Seminar at the University of Exeter Business School (27th March 2023).
[Seminar Speaker: Prof. Robert Taylor, University of Essex].
During an Econometrics Seminar at the University of Exeter Business School (28th March 2023)
[Seminar Speaker: Prof. Anna Simoni, Institut Polytechnique de Paris].
During a Time Series and Machine Learning Reading Group Session at the University of Southampton (April 2023).
During a Hybrid Seminar at the Department of Economics, University of Southampton (17th October 2023).
[Seminar Speaker: Dr. Chaowen Zheng, University of Southampton].
During a TS and ML Reading Group Session (3rd November 2023).
[Guest Speaker: Dr. Shen Guohao, Department of Applied Mathematics, Hong Kong Polytechnic University].
During a Hybrid Seminar at Statistical Sciences Research Institute, University of Southampton (16th May 2024).
Participation to Workshops, Seminars and Reading Groups in Econometrics and Statistics
Research Theme:
Online Learning and Numerical Methods for Econometric Analysis
Reinforcement and online learning describes how an agent can learn an optimal action policy in a sequential decision process, using repeated experiments. Therefore, to facilitate econometric estimation and inference within reinforcement learning settings, techniques such as the matrix completion bandit approach, and minimax regret optimisation, which have applications in expected welfare maximization and treatment choice problems.
Numerical and high-dimensional optimisation is used to calibrate dynamic macroeconomic models, which permits to construct macroeconometric measures (e.g., such as sufficient statistics), on how heterogeneous agents can benefit from a social planner's perspective who is concerned about maximising total welfare. In addition, solving dynamic structural macro models typically involves an infinite-dimensional parameter space. Therefore, optimising the computational resources during the estimation and optimisation steps, is crucial for environments with limited resources; especially when storing large datasets is infeasible, impractical or computationally costly.
References:
Online Learning Methods and Econometric Inference
Chen, X., Tamer, E., and Yao, Q. (2026). "Online Learning in Semiparametric Econometric Models". Preprint arXiv:2603.08614.
Akker, R. V. D., Werker, B. J., and Zhou, B. (2025). "Valid Post-Contextual Bandit Inference". Preprint arXiv:2505.13897.
Chernozhukov, V., Lee, S., Rosen, A. M., and Sun, L. (2025). "Policy Learning with Confidence". Preprint arXiv:2502.10653.
Duan, C., Li, J., and Xia, D. (2024). "Online Policy Learning and Inference by Matrix Completion". Preprint arXiv:2404.17398.
Solving High-Dimensional State Space Models
Bilal, A. (2023). "Solving Heterogeneous Agent Models with the Master Equation". NBER Working Paper (No. w31103). Available at nber/w31103.
Auclert, A., Bardóczy, B., Rognlie, M., and Straub, L. (2021). "Using the Sequence‐Space Jacobian to Solve and Estimate Heterogeneous‐Agent Models". Econometrica, 89(5), 2375-2408.
Winberry, T. (2018). "A Method for Solving and Estimating Heterogeneous Agent Macro Models". Quantitative Economics, 9(3), 1123-1151.
Research Theme:
Empirical Process Theory for Nonparametric Analysis
Empirical processes are widely used for both nonparametric and semiparametric methods, due to their robust properties when estimating nuisance parameters in high-dimensional settings. Asymptotic theory is developed using empirical process theory which allows to conduct inference regardless of the size of the parameter space and error dependence structures.
Reading Group:
References:
Statistical Learning: Theory and Inference
Kock, A. B., and Preinerstorfer, D. (2026). "Regularizing Fairness in Optimal Policy Learning with Distributional Targets". Journal of Econometrics, 254, 106186.
Athey, S., Imbens, G. W., Metzger, J., and Munro, E. (2024). "Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations". Journal of Econometrics, 240(2), 105076.
Meitz, M. (2024). "Statistical Inference for Generative Adversarial Networks and Other Minimax Problems". Scandinavian Journal of Statistics.
Qiu, Y., Gao, Q., and Wang, X. (2024). "Adaptive Learning of the Latent Space of Wasserstein Generative Adversarial Networks". Preprint arXiv:2409.18374.
Wei, W., Zhou, Y., Zheng, Z., and Wang, J. (2024). "Inference on the Best Policies with Many Covariates". Journal of Econometrics, 239(2), 105460.
Zhou, X., et. al. (2023). "A Deep Generative Approach to Conditional Sampling". Journal of the American Statistical Association, 118(543), 1837-1848.
Fiksel, J., Datta, A., Amouzou, A., and Zeger, S. (2022). "Generalized Bayes Quantification Learning under Dataset Shift". Journal of the American Statistical Association, 117(540), 2163-2181.
Empirical Processes, Weak Convergence and Invariance Principles
Munko, M., and Dobler, D. (2026). "Conditional Delta-Method for Resampling Empirical Processes in Multiple Sample Problems". Stochastic Processes and their Applications, 104885.
Hult, H., Lindhe, A., Nyquist, P., and Wu, G. J. (2025). "A Weak Convergence Approach to Large Deviations for Stochastic Approximations". Preprint arXiv:2502.02529.
Austern, M., and Orbanz, P. (2022). "Limit Theorems for Distributions Invariant under Groups of Transformations". Annals of Statistics, 50(4), 1960-1991.
Fasen‐Hartmann, V., and Kimmig, S. (2020). "Robust Estimation of Stationary Continuous‐Time ARMA Models via Indirect Inference". Journal of Time Series Analysis, 41(5), 620-651.
Kuersteiner, G. M. (2019). "Invariance Principles for Dependent Processes Indexed by Besov Classes with an Application to a Hausman Test for Linearity". Journal of Econometrics, 211(1), 243-261.
Leucht, A., Kreiss, J. P., and Neumann, M. H. (2015). "A Model Specification Test for GARCH (1, 1) Processes". Scandinavian Journal of Statistics, 42(4), 1167-1193.
Chau, T. W. (2014). "On the Equivalence of Indirect Inference and Bootstrap Bias Correction for Linear IV Estimators". Economics Letters, 123(3), 333-335.
Arel-Bundock, V. (2013). "A Solution to the Weak Instrument Bias in 2SLS Estimation: Indirect Inference with Stochastic Approximation". Economics letters, 120(3), 495-498.
Phillips, P.C.B. (2012). "Folklore Theorems, Implicit Maps, and Indirect Inference". Econometrica, 80(1), 425-454.
Pardon, J. (2011). "Central Limit Theorems for Random Polygons in an Arbitrary Convex Set". Annals of Probability, 39(3), 881-903.
Drees, H., and Rootzén, H. (2010). "Limit Theorems for Empirical Processes of Cluster Functionals". Annals of Statistics, 38(4): 2145-2186.
High-Dimensional Time Series Analysis
Zhu, J., Kong, D., Zhang, Z., and Lin, Z. (2026). "Stationarity of Manifold Time Series". Journal of the American Statistical Association, 1–23 (just-accepted).
Huang, C., and Zhang, A. R. (2025). "High-Order Accurate Inference on Manifolds". Preprint arXiv:2501.06652.
Kumar, S., Yang, Y., and Lin, L. (2024). "A Likelihood based Approach to Distribution Regression using Conditional Deep Generative Models". Preprint arXiv:2410.02025.
Zhu, C., and Müller, H. G. (2024). "Spherical Autoregressive Models with Application to Distributional and Compositional Time Series". Journal of Econometrics, 239(2), 105389.
Huo, Y., Fan, Y., and Han, F. (2023). "On the Adaptation of Causal Forests to Manifold Data". Preprint arXiv:2311.16486.
Lubold, S., Chandrasekhar, A. G., and McCormick, T. H. (2023). "Identifying the Latent Space Geometry of Network Models through Analysis of Curvature". Journal of the Royal Statistical Society Series B, 85(2), 240-292.
Yao, Z., Su, J., Li, B., and Yau, S. T. (2023). "Manifold Fitting". Preprint arXiv:2304.07680.
Zhang, Y., Shen, W., and Kong, D. (2023). "Covariance Estimation for Matrix-Valued Data". Journal of the American Statistical Association, 118(544), 2620-2631.
Chen, R., Xiao, H., and Yang, D. (2021). "Autoregressive Models for Matrix-Valued Time Series". Journal of Econometrics, 222(1), 539-560.
Evans, R. J. (2020). "Model Selection and Local Geometry". Annals of Statistics, 48(6), 3513-3544.
Wang, D., Liu, X., and Chen, R. (2019). "Factor Models for Matrix-Valued High-Dimensional Time Series". Journal of Econometrics, 208(1), 231-248.
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