Research

Postdoctoral Related Research

(since September 2023)

Research Project: Structural Analysis of Non-Gaussian Time Series with Applications to Climate and Macroeconomy.

Research Objectives: The primary aim of the research project is the development of new econometric methods related to non-Gaussian Structural Vector Autoregressive (SVAR) models. In addition, the new and existing methods are employed to study the economic effects of climate change as well as economic and financial shocks. We aim to develop robust estimation and inference procedures using techniques for statistical identification of non-Gaussian structural shocks, building on earlier work by Lanne, M., Meitz, M., and Saikkonen, P. (2017), as well as earlier research on SVARs with possibly nonstationary regressors.

Supervision: by Prof. Markku Lanne (PI) and Prof. Mika Meitz.
Institution: Faculty of Social Sciences, University of Helsinki
Position: Postdoctoral Researcher in Econometrics.
Funding: Financial support from the Research Council of Finland (Grant No. 347986) is gratefully acknowledged.

Research Papers:

"Robust Identification and Estimation in Non-Gaussian Structural VARs with Near Unit Roots". Funding: RCF (Grant No. 347986).

"Econometric Inference in Non-Gaussian Structural Panel Vector Autoregressions and Local Projection Methods". Funding: RCF (Grant No. 347986).

Research Theme: Robust Inference to Deviations from Unit Roots (Near Unit Roots) in SVARs.

These research papers propose (robust and uniform) estimation and inference methods such as when constructing impulse response functions and local projections in VAR, Cointegrated VAR and SVAR models with Near Unit Roots. In particular, we investigate how the presence of near-unit root processes (i.e., when regressors are parameterized as LUR) affect the main asymptotic properties of estimators and related test statistics, especially when techniques for statistical identification of non-Gaussian time series are employed. Exploiting the non-Gaussianity property ensures a valid identification scheme which can be extended to the case of weakly or strongly dependent data. Our research also contributes to the literature of uniform inference in non-Gaussian time series.

“As an Early Career Researcher in Econometrics, I am excited to be conducting original research and contributing to knowledge and understanding, which concentrates on the development of state-of-the-art identification, estimation and inference methods in macroeconometrics.”

Dr Christis Katsouris

1 March 2025

Related Literature:

Anttonen, J., Lanne, M., and Luoto, J. (2024). "Statistically Identified SVAR Model with Potentially Skewed and Fat-tailed Errors". Journal of Applied Econometrics, 39(3), 422-437.
Katsouris, C. (2023). "Structural Analysis of Vector Autoregressive Models". Preprint arXiv:2312.06402.
Lanne, M., Meitz, M., and Saikkonen, P. (2017). "Identification and Estimation of non-Gaussian Structural Vector Autoregressions". Journal of Econometrics, 196(2), 288-304.
Lanne, M., and Luoto, J. (2016). "Noncausal Bayesian Vector Autoregression". Journal of Applied Econometrics, 31(7), 1392-1406.
Lanne, M., and Saikkonen, P. (2013). "Noncausal Vector Autoregression". Econometric Theory, 29(3), 447-481.
Lanne, M., and Lütkepohl, H. (2010). "Structural Vector Autoregressions with Nonnormal Residuals". Journal of Business & Economic Statistics, 28(1), 159-168.
Kauppi, H. (2004). "On the Robustness of Hypothesis Testing based on Fully Modified Vector Autoregression when Some Roots are Almost One". Econometric Theory, 20(2), 341-359.
Lanne, M. (2002). "Testing the Predictability of Stock Returns". Review of Economics and Statistics, 84(3), 407-415.
Lanne, M. (2000). "Near Unit Roots, Cointegration, and the Term Structure of Interest Rates". Journal of Applied Econometrics, 15(5), 513-529.
Lanne, M. (1999). "Near Unit Roots and the Predictive Power of Yield Spreads for changes in Long-Term Interest Rates". Review of Economics and Statistics, 81(3), 393-398.

Ex-Post Related Literature:

Ludwig, J. F. (2024). "Local Projections Are VAR Predictions of Increasing Order". Working paper, Texas Tech University, Department of Economics. Available at SSRN 4882149.
Lewis, D. J. (2024). "Identification based on Higher Moments". Working paper, University College London, Department of Economics.

Doctoral Related Research

[1]. "Weak Convergence of Self-Normalized Partial Sum Processes in the M1 topology".

Supervision: Associate Prof. Danijel Krizmanic (Jul 2022-Sep 2023)

Summary: The contribution of the research paper in the probability and applied probability literature, is that we establish the joint weak convergence (coordinatewise) of functionals of regularly varying processes to the M1 Skorokhod topology, such as self-normalized partial sum processes, using the analytical expression of the characteristic function of alpha-stable stochastic variables and regularly varying time series. Specifically, the proposed probability theory driven framework contributes to the literature of functional central limit theorems (invariance principles) within the M1 Skorokhod topology for partial sum and self-normalized partial sum processes of weakly dependent data [Preprint arXiv].

Limitations and Further Research: The established probability theory (invariance principles), within a properly defined probability space equipped with the M1 topology, corresponds to univariate regularly varying sequences. An interesting avenue for further research is to establish novel Functional Central Limit Theorems (FCLTs) for partial sum processes and self-normalized partial sum processes for multivariate regularly varying sequences. Other possible applications of our FCLTs include their use for establishing limit results of functionals obtained in Autoregressive Duration models, where the interval duration is a random variable or even in Jump Regressions which require conditions on the magnitude and size of jumps. Lastly, establishing weak convergence of stochastic integrals for regularly varying Levy-type processes in the Skorokhod's J1 and M1 topologies, can be useful in the development of asymptotic theory for functionals commonly used in time series and cointegrating regressions.

*Financial support from the University of Exeter is gratefully acknowledged.

Related Literature:

> Weak Convergence for Partial-Sum Processes of Regularly Varying Stationary Sequences in M1 Topology

Katsouris, C. (2024). "Weak Convergence for Self-Normalized Partial Sum Processes in the Skorokhod M1 Topology with Applications to Regularly Varying Time Series". Preprint arXiv:2405.01318.
Krizmanic, D. (2024). "A Functional Limit Theorem for Self-Normalized Partial Sum Processes in the M1 Topology". Preprint arXiv:2411.18236.
Basrak, B., Planinic, H., and Soulier, P. (2018). "An Invariance Principle for Sums and Record Times of Regularly Varying Stationary Sequences". Probability Theory and Related Fields, 172(3), 869–914.
Basrak, B., and Krizmanić, D. (2015). "A Multivariate Functional Limit Theorem in Weak M1 Topology". Journal of Theoretical Probability, 28(1), 119-136.
Owada, T., and Samorodnitsky, G. (2015). "Functional Central Limit Theorem for Heavy Tailed Stationary Infinitely Divisible Processes Generated by Conservative Flows". Annals of Probability, 43(1), 240-285.
Basrak, B., Krizmanic, D., and Segers, J. (2012). "A Functional Limit Theorem for Partial Sums of Dependent Random Variables with Infinite Variance". Annals of Probability, 40(5), 2008-2033.
Davis, R. A. and Hsing, T. (1995). "Point Process and Partial Sum Convergence for Weakly Dependent Random Variables with Infinite Variance". Annals of Probability, 23(2), 879–917.

> Weak Convergence for Partial-Sum Maxima Processes in M1 Topology

Krizmanić, D. (2020). "On Joint Weak Convergence of Partial Sum and Maxima Processes". Stochastics, 92(6), 876-899.
Krizmanić, D. (2016). "Functional Weak Convergence of Partial Maxima Processes". Extremes, 19, 7-23.

> Partial-Sum Processes for Near-Stationary Processes in J1 Topology

Demetrescu, M., and Hosseinkouchack, M. (2025). "Partial Sums of Almost Overdifferenced, Near‐Stationary Processes With Time‐Varying Properties". Journal of Time Series Analysis.

> Weak Convergence for Stochastic Integrals in J1 Topology

Søjmark, A., and Wunderlich, F. (2024). "Functional Weak Convergence of Stochastic Integrals for Moving Averages and Continuous-Time Random Walks". Preprint arXiv:2401.13543.
Sojmark, A., and Wunderlich, F. (2023). "Weak Convergence of Stochastic Integrals on Skorokhod Space in Skorokhod's J1 and M1 Topologies". Preprint arXiv:2309.12197.
Hult, H., and Lindskog, F. (2007). "Extremal Behavior of Stochastic Integrals driven by Regularly Varying Lévy Processes". Annals of Probability, 35(1), 309-339.

> Weak Convergence for Bootstrap-based Empirical Processes

Bai, S., Taqqu, M. S., and Zhang, T. (2016). "A Unified Approach to Self-Normalized Block Sampling". Stochastic Processes and their Applications, 126(8), 2465-2493.
Ziegler, K. (1997). "Functional Central Limit Theorems for Triangular Arrays of Function-Indexed Processes under Uniformly Integrable Entropy Conditions". Journal of Multivariate Analysis, 62(2), 233-272.
Præstgaard, J., and Wellner, J. A. (1993). "Exchangeably Weighted Bootstraps of the General Empirical Process". Annals of Probability, 2053-2086.

Bibliography:

Mikosch, T., and Wintenberger, O. (2024). Extreme Value Theory for Time Series: Models with Power-Law Tails. Springer, New York.
Kulik, R., and Soulier, P. (2020). Heavy-Tailed Time Series. Springer, New York.
Häusler, E., and Luschgy, H. (2015). Stable Convergence and Stable Limit Theorems (Vol. 74). Springer, Berlin.
Victor H. Peña, V. H., Lai T. L., and Shao, Q-M. (2009). Self-Normalized Processes: Limit Theory and Statistical Applications. Springer, New York.
Resnick, S. I. (2007). Heavy-Tail Phenomena: Probabilistic and Statistical Modeling (Vol. 10). Springer Science.
Ken-Iti, S. (1999). Lévy Processes and Infinitely Divisible Distributions (Vol. 68). Cambridge University Press.
Samoradnitsky, G. and Taqqu, M. S. (1994). Stable Non-Gaussian Random Processes. Chapman and Hall, New York.
Horváth, L., and Csörgö, M. (1993). Weighted Approximations in Probability and Statistics. Wiley.

The Ph.D. in Economics thesis proposes novel estimation and inference techniques for modelling systemic risk measures in unstable and nonstationary environments. These risk measures are robust to procyclicality as are estimated via quantile predictive regressions with the near unit root parametrization that captures the unknown persistence and endogeneity of predictors.

[2]. "Estimation and Inference in Systems of Nonstationary Quantile Predictive Regressions". (Chapter 4 PhD thesis, submitted at the University of Southampton, 2022)

[3]. "Structural Break Detection in Quantile Predictive Regression with Persistent Regressors". (Chapter 3 PhD thesis, submitted at the University of Southampton, 2022)

Funding: Financial support from a University of Southampton PhD Scholarship is gratefully acknowledged. Additional funding was provided in the form of Teaching assistantship from the University of Southampton.

Research and Knowledge Exchange Related Activities

Research Visits:

20 - 24 February 2023. Research visit hosts: Dr. Chao Zheng and Prof. Zudi Lu, Statistics Division, School of Mathematical Sciences, University of Southampton, UK.

Summary. During the research visit I participated in the activities of the Reading Group in Time Series and Machine Learning at The University of Southampton. I also worked on a research manuscript with topic related on: "Specification Testing for Nonparametric Cointegrating Regression with Nearly Integrated Regressors under NED", under the supervision of Dr. Chao Zheng and Prof. Zudi Lu. The main idea of this research paper it was to develop a framework for specification testing based on unusually general limit theory which covers both parametric and nonparametric regressions with nonstationary data. Statistical estimation is based on the local linear estimator proposed by Lu and Linton (2007). We impose extra distributional assumptions in the form of a smoothness condition which requires integrability of the characteristic function to establish convergence to local time processes based on functionals from the related literature. Overall, further applications include to develop asymptotic theory for tests of functional form misspecification in nonlinear cointegrating regression models, which employ the classical FM-OLS approach from cointegrating regressions such that the robustness property holds, which means that it leads to a zero mean Gaussian mixture limiting distribution that coincides with the limiting distribution in the linear cointegrating regression model.

*Funding from the Southampton Statistical Sciences Research Institute is gratefully acknowledged.

Related Literature:

a. Literature on Specification Testing:

Tu, Y., Liang, H. Y., and Wang, Q. (2022). "Nonparametric Inference for Quantile Cointegrations with Stationary Covariates". Journal of Econometrics, 230(2), 453-482.
Wang, Q., Wu, D., and Zhu, K. (2018). "Model Checks for Nonlinear Cointegrating Regression". Journal of Econometrics, 207(2), 261-284.
Cai, Z., Jing, B., Kong, X., and Liu, Z. (2017). "Nonparametric Regression with Nearly Integrated Regressors under Long‐Run Dependence". The Econometrics Journal, 20(1), 118-138.
Dong, C., Gao, J., Tjøstheim, D., and Yin, J. (2017). "Specification Testing for Nonlinear Multivariate Cointegrating Regressions". Journal of Econometrics, 200(1), 104-117.
Phillips, P.C.B., Li, D., and Gao, J. (2017). "Estimating Smooth Structural Change in Cointegration Models". Journal of Econometrics, 196, 180-195.
Wang, Q., and Phillips, P.C.B. (2016). "Nonparametric Cointegrating Regression with Endogeneity and Long Memory". Econometric Theory, 32(2), 359-401.
Liang, Z., Lin, Z., and Hsiao, C. (2015). "Local Linear Estimation of a Nonparametric Cointegration Model". Econometric Reviews, 34(6-10), 882-906.
Chen, J., Gao, J., Li, D., and Lin, Z. (2015). "Specification Testing in Nonstationary Time Series Models". The Econometrics Journal, 18(1), 117-136.
Gu, J., and Liang, Z. (2014). "Testing Cointegration Relationship in a Semiparametric Varying Coefficient Model". Journal of Econometrics, 178, 57-70.
Wang, Q. (2014). "Martingale Limit Theorem Revisited and Nonlinear Cointegrating Regression". Econometric Theory, 30(3), 509-535.
Wang, Q., and Wang, Y.X.R. (2013). "Nonparametric Cointegrating Regression with NNH Errors". Econometric Theory, 29(1), 1-27.
Li, D., Lu, Z., and Linton, O. (2012). "Local Linear Fitting under Near Epoch Dependence: Uniform Consistency with Convergence Rates". Econometric Theory, 28(5), 935-958.
Wang, Q., and Phillips, P.C.B. (2012). "A Specification Test for Nonlinear Nonstationary Models". Annals of Statistics, 40(2), 727-758
Gao, J., King, M., Lu, Z., and Tjøstheim, D. (2009). "Specification Testing in Nonlinear and Nonstationary Time Series Autoregression". Annals of Statistics, 37(6B), 3893-3928.
Wang, Q., and Phillips, P.C.B. (2009). "Structural Nonparametric Cointegrating Regression". Econometrica, 77(6), 1901-1948.
Lu, Z., and Linton, O. (2007). "Local Linear Fitting under Near Epoch Dependence". Econometric Theory, 23(1), 37-70.

Remark 1: Earlier work related to nonparametric cointegrating regression models focus on the development of estimation and inference procedures on the fully nonparametric regression model with endogeneity (see, Wang & Phillips (2012, AoS) and Wang & Phillips (2016, ET)), the stationary and nonstationary nonparametric linear cointegrating regression (see, Chen, Gao, Li & Lin (2015, Econometric Reviews)), and the nonlinear cointegrating regression (see, Gao, King, Lu & Tjøstheim (2009, AoS) and Wang, Wu & Zhu (2018, JoE)), and the structural cointegrating regression model (see, Wang & Phillips (2009, Ecta)) and the semiparametric varying coefficient model (see, Gu & Liang (2014, JoE)). Without loss of generality, hypothesis testing regarding the correct functional form of the model (such as specification testing), is constructed using discrepancy measures between the semiparametric estimate and the restricted estimate obtained under the null of a linear functional form specification. Further relevant environments for establishing limit theory and inference procedures correspond to a nonstationary regression framework with non-cointegrated variables under endogeneity. Several studies consider econometric estimation and inference in cointegrating regressions based on both conditional mean and conditional quantile functional forms (see, Tu, Liang & Wang (2022, JoE)), as well as with functional coefficient parametrizations (see, Phillips & Wang (2023, JoE) and Wang, Phillips & Tu (2024, JoE)), and also settings in the presence of smooth structural breaks (see, Phillips, Li and Gao (2017, JoE)).

Remark 2: Moreover, several studies in the literature consider limit theory for functional coefficient cointegrating regression. Take for example the following ingredients: (i) a local constant estimator as proposed in some papers of Prof. Zudi Lu (see, Lu & Linton (2007, ET)), (ii) nonstationary regressors in cointegrating regression models, and (iii) weighted kernel estimation techniques. Does the conventional limit theory of functional cointegrating regressions extends to the case of nonstationary regressors when we employ the local constant estimator? Most likely the asymptotic theory will require certain changes to be incorporated, due to the presence of nonstationary regressors which could in practice introduce random bias in the expansion term for the consistency of the local constant estimator. In fact, the particular term will dominate the asymptotic expansion due to its stochastic properties inherited from the nonstationary and possibly high persistent regressors. These issues are being studied in the framework proposed by Phillips & Wang (2023, JoE), where a corrected limit theory is proposed. Furthermore, in a follow-up paper the authors propose limit theory for general kernel weighted local p-th order polynomial estimator (see, Wang & Phillips (2025, JoE)).

b. Literature on Limit Theory for Cointegrating Regression:

Wang, Y., and Phillips, P.C.B. (2025). "Limit Theory of Local Polynomial Estimation in Functional Coefficient Regression". Journal of Econometrics (forthcoming).
Wang, Y., Phillips, P.C.B., and Tu, Y. (2025). "Limit Theory and Inference in Non-Cointegrated Functional Coefficient Regression". Journal of Econometrics, 249, 105996.
Hu, Z., Liu, N., Phillips, P.C.B., and Wang, Q. (2024). "Self-Weighted Estimation for Local Unit Root Regression with Applications". Cowles Foundation Discussion Papers. 2805.
Phillips, P.C.B., and Wang, Q. (2024). "A General Limit Theory for Nonlinear Functionals of Nonstationary Time Series". Cowles Foundation Discussion Papers. 2801.
Phillips, P.C.B., and Wang, Y. (2023). "When Bias Contributes to Variance: True Limit Theory in Functional Coefficient Cointegrating Regression". Journal of Econometrics, 232(2), 469-489.

c. Literature on Quantile Cointegrating Regression:

Li, H., Zhang, J., and Zheng, C. (2025). "Functional-Coefficient Quantile Cointegrating Regression with Stationary Covariates". Statistics & Probability Letters, 219, 110344.
Lin, Y., and Tu, Y. (2024). "Functional Coefficient Cointegration Models with Box–Cox Transformation". Economics Letters, 234, 111472.
Liu, B., and Pang, T. (2024). "Weighted Composite Quantile Inference for Nearly Nonstationary Autoregressive Models". Statistical Methods & Applications, 1-43.

Remark 3: A novel framework which adds on the nonstationary time series econometrics literature, extending the scope of the limit theory for moderate deviations from unity in nearly nonstationary autoregressions developed by study of PM (2007, JoE), is presented by Liu and Pang (2024, SMA), who propose a different type of quantile regression (i.e., the weighted composite quantile) and derive the asymptotic theory for the mildly integrated and mildly explosive cases. Furthermore, the framework proposed by Li, Zhang and Zheng (2025, SPL), corresponds to a broader class of specification testing procedures in quantile cointegrating regression models. In particular, these authors propose a sequential two phase procedure, where during the first-stage a test statistic for parameter stability of the cointegrating coefficient is implemented, while during the second-stage a statistic testing whether the fixed-coefficient models gives a good-fit of the model specification to the underline data generating process against the alternative that the functional-coefficient, using a CUSUM-based functional along with a moving block bootstrap procedure for finite-sample refinements.

d. Literature on Self-Weighted M-Estimation:

She, R. (2025). "Inference in Median AR Models with Nonstationary and Heavy-Tailed Heteroscedastic Errors". Econometric Theory. Available at econometric theory.
Guo, F., She, R., and Yang, Y. (2024). "Inference on Nonstationary Heavy‐Tailed AR Processes via Model Selection". Journal of Time Series Analysis.
Li, X., Pan, J., and Song, A. (2023). "Geometric Ergodicity and Conditional Self‐Weighted M‐Estimator of a GRC-AR (p) Model with Heavy‐Tailed Errors". Journal of Time Series Analysis, 44(4), 418-436.
Wang, X., and Hu, S. (2017). "Asymptotics of Self-Weighted M-Estimators for Autoregressive Models". Metrika, 80, 83-92.
Ling, S. (2007). "Self-Weighted and Local Quasi-Maximum Likelihood Estimators for ARMA-GARCH/IGARCH Models". Journal of Econometrics, 140(2), 849-873.

Workshop Participation:

Time Series Workshop (3nd Edition). School of Economics, University of East Anglia, UK (22nd - 23rd May 2025).
Time Series Workshop (2nd Edition). School of Economics, University of East Anglia, UK (22nd - 23rd May 2024).
The 8th RCEA Time Series Econometrics Workshop. Brunel University Business School, UK (21st May 2024).
Annual Econometric Workshops of Research Methods Centre, University of Exeter Business School, UK (9th - 10th May 2023).

IX Workshop in Time Series Econometrics, Zaragoza, Spain (April 2019).

Discussion Participation:

Recent Advances in Extreme Value Theory (Young Statisticians Europe, April 20th 2023).
Recent Challenges in Model Specification Testing based on different data structures (Young Statisticians Europe, November 9th 2022).
"Inference via Bootstrap Resampling Methods in Autoregressive Models with Local to Unity and Mildly Unstable Processes" (Jiangxi University of Finance and Economics, October 4th 2022).

Conference Participation:

International Association for Applied Econometrics Annual Conference 2025, Torino, Italy (25th - 27th June 2025).

International Symposium on Nonparametric Statistics, Paphos, Cyprus (20th - 24th June 2022).

International Association for Applied Econometrics, Annual Conference 2019, Nicosia, Cyprus (25th - 28th June 2019).

Summer School Participation:

Hausdorff Center for Mathematics, Summer School in High-Dimensional Statistics, Bonn, Germany (July 2021).

Participation to Interview & Profile Presentation Sessions:

List of Selected Interview Sessions:

Academic Positions:

Interview for the position: Lecturer in Economics (teaching focused). School of Economics, University of Bristol, UK (10th September 2021).
Interview for the position: Postdoctoral Researcher in Econometrics. Department of Economics and Business Economics, Aarhus University, Denmark (20th December 2021).
Interview for the position: Lecturer in Economics (teaching focused). Department of Economics, University of Exeter, UK (24th May 2022).
Interview for the position: Postdoctoral Researcher in Econometrics. Economics Division, Faculty of Social Sciences, University of Helsinki, Finland (Spring 2023).
Interview for the position: Lecturer in Econometrics (teaching focused). Department of Economics, University of Manchester, UK (28th May 2024). Slides

Non-Academic Positions:

Interview for the position: Modelling Consultant. Risk Consulting Department, KPMG Cyprus (18th March 2022).
Interview for the position: Macroeconomics Research and Policy Officer. Macroprudential Policy Group, Department of Financial Stability, Central Bank of Cyprus (18th July 2024).

Summary of Key Points.

During the interview session with CBC on the 18th of July 2024, I discussed the mechanism of systemic risk transmission in the banking sector which includes the accumulation of financial risks across a network of interconnected bank and non-bank financial institutions within the economy. Specifically, financial contagion can arise due to economy-wide shocks (such as uncertainty shocks) which could affect the financial stability of both bank and non-bank financial institutions. A simple example, is a bank which holds a bad portfolio of non-performing loans, which can contribute to the presence of balance sheet risks, thereby increasing the probability of defaults. In particular, such scenarios imply that Systemically Important Financial Institutions (SIFIs) can impact the overall stability of the financial network, especially under the presence of downward risks (such as when a "key player" is forced to be removed from the network). Therefore, these potential challenges emphasize the crucial role of macroprudential measures as a mechanism for ensuring financial stability as well as allowing to minimize the impact of financial vulnerabilities to the real economy.

Central Banks play a key role in ensuring price stability as well as for monitoring the financial stability of the system. Moreover, the macroprudential policy function focuses on two important responsibilities regarding compliance and prediction with respect to: (i) the use micro-prudential supervision tools (firm/micro level) such as stress tests, threshold determination of capital buffers (e.g., using firm-specific characteristics and macroeconomic conditions) in order to make timely policy interventions and (ii) the use of macro-prudential supervision tools (aggregate/macro level) such as the modelling of aggregate business cycle fluctuations (e.g., heterogeneous agent models), the identification and estimation of the various channels of macroeconomic shocks in relation to policy recommendations (e.g., via a suitable identification strategy with SVAR models). Specifically, in the case of insurance companies and other non-bank financial institutions those have a crucial role in preventing the transmission of spillover risk and financial contagion both at the aggregate level as well as the economic agent level through the investment and consumption channels. Therefore, mitigating actions beyond the use of early warning systems and real-time monitoring macroeconometric toolkits, include the design of an Emergency Lending Facility for non-bank financial institutions which can facilitate the absorption of systemic risks using appropriate financial instruments and additional lending functions. Overall, a resilient banking sector allows to further support economic growth through green investments and other structural reforms necessary to maintain economic competitiveness within a fast changing environment characterized by various global factors which can shape our responses such as climate change and other technological advancements.

Related Literature:

Barreda-Tarrazona, I., Grimalda, and Teglio, A. (2024). "Voluntary Insurance versus Stabilization Funds: An Experimental Analysis on Bank Runs". Journal of Behavioral and Experimental Finance, 42.
Katsouris, C. (2021). "Optimal Portfolio Choice and Stock Centrality for Tail Risk Events". Preprint arXiv:2112.12031.
Ahnert, T., and Elamin, M. (2020). "Bank Runs, Portfolio Choice, and Liquidity Provision". Journal of Financial Stability, 50, 100781.
Chen, C. Y., Härdle, W. K., and Okhrin, Y. (2019). "Tail Event Driven Networks of Systemically Important Financial Institutions". Journal of Econometrics, 208(1), 282-298.
Dicks, D. L., and Fulghieri, P. (2019). "Uncertainty Aversion and Systemic Risk". Journal of Political Economy, 127(3), 1118-1155.
Debarsy, N., Dossougoin, C., Ertur, C., and Gnabo, J. Y. (2018). "Measuring Sovereign Risk Spillovers and Assessing the Role of Transmission Channels: A Spatial Econometrics Approach". Journal of Economic Dynamics and Control, 87, 21-45.
Katsouris, C. (2018). "Aspects of Financial Connectedness: A Global Financial Contagion-Stability Measure". PhD Progression Review, Faculty of Social Sciences, University of Southampton.
Zhu, H. (2005). "Bank Runs, Welfare and Policy Implications". Journal of Financial Stability, 1(3), 279-307.
Diamond, D. W.*, and Dybvig, P. H. (1983). "Bank Runs, Deposit Insurance, and Liquidity". Journal of Political Economy, 91(3), 401-419. * Laureate of the Nobel Memorial Prize in Economics 2022.

Recent Econometrics Literature:

Dettaa, E., and Wang, E. (2024). "Inference in High-Dimensional Linear Projections: Multi-Horizon Granger Causality and Network Connectedness". Preprint arXiv:2410.04330.
Dufour, J.M., and Wang, E. (2024). "Simple Robust Two-Stage Estimation and Inference for Generalized Impulse Responses and Multi-Horizon Causality". Preprint arXiv:2409.10820.

Participation to Econometrics Seminars Co-hosting Activities:

University of Helsinki

Position: Postdoctoral Researcher in Econometrics, Economics Division, Faculty of Social Sciences, University of Helsinki (Academic year 2023/2024).
Econometrics Seminars Organizer: Prof. Mika Meitz and Dr. Timm Prein, University of Helsinki

Econometrics Seminar Series:

Dr. Xuewen Yu (Assistant Professor in Economics, Fudan University). Seminar on 22nd of November 2023 titled: "Large Structural VARs with Multiple Sign and Ranking Restrictions" (cancelled, discussion on 12th January 2024).
Dr. Stephanie Ettmeier (Postdoctoral Researcher, University of Bonn). Seminar on 8th December 2023 titled: "Functional VARs for Macro: The Distributional Effects of Tax Changes, Mixed Frequency Considerations and Relationships to Panel Approaches".

University of Exeter

Position: Lecturer in Economics at the Department of Economics, University of Exeter Business School (Academic year 2022/2023).
Along with academic members of the Econometrics Research Group: Dr. Samuel Engle (Lecturer in Economics), Dr. Sebastian Kripfganz (Senior Lecturer in Econometrics) and Dr. Namhyun Kim (Senior Lecturer in Economics) we have co-hosted at the UEBS the following seminar speakers:

Econometrics Seminar Series:

Dr. Maurizio Daniele (Postdoctoral Researcher, ETH Zurich). Seminar on 16th of December 2022 titled: "Deep Learning with Non-Linear Factor Models: Adaptability and Avoidance of Curse of Dimensionality".

Related publications:

Daniele, M. (2024). "Selecting the Number of Factors in Approximate Factor Models using Group Variable Regularization". Econometric Reviews, 1-28.
Daniele, M., Pohlmeier, W., and Zagidullina, A. (2024). "A Sparse Approximate Factor Model for High-Dimensional Covariance Matrix Estimation and Portfolio Selection". Journal of Financial Econometrics, 23(1), nbae017.

Dr. Byunghoon Kang (Associate Professor in Economics, Department of Economics, University of Lancaster). Seminar on 21st March 2023 titled: "Robust Inference for GMM with Possibly Non-smooth Moments".

Related publications:

Kang, B., Lee, S., and Song, J. (2025). "Convergence Rates of GMM Estimators with Nonsmooth Moments under Misspecification". Preprint arXiv:2501.09540.
Kang, B. and Lee, S. (2024). "Robust Asymptotic and Bootstrap Inference for Nonsmooth GMM". Working Paper.

Prof. Dennis Kristensen (Professor of Economics, Department of Economics, UCL). Seminar on 23rd May 2023 titled: "Iterative Estimation of Structural Models with an Application to Perturbed Utility Models".

Discussion of Seminar Talks:

Dr. Pietro Spini (Lecturer in Economics, School of Economics, University of Bristol). Seminar on 6th of December 2022 titled: "Robustness, Heterogeneous Treatment Effects and Covariate Shifts".
Prof. Anna Simoni (Professor of Statistics & Econometrics, ENSAE). Seminar on 28th of March 2023 titled: "Bayesian Bi-level Sparse Group Regressions for Macroeconomic Forecasting".

University of Southampton

Position: Doctoral Researcher in Econometrics, Department of Economics, University of Southampton (Jan 2018 to Sep 2022).

Econometrics Seminar Series:

[along with Econometrics Seminars Organizer during academic year 2021/2022: Dr. Jayeeta Bhattacharya]

Dr. Chaowen Zheng (Lecturer in Economics, Southampton). Seminar on 17th October 2023 titled: "A Spatio-temporal Autoregressive Factor Model of the Global Business Cycle".
Dr. Loriano Mancini (Associate Professor of Finance, USI Lugano). Seminar on 16th March 2022 titled: "Portfolio choice when stock returns may disappoint: An empirical analysis based on L-moments".
Dr. Markus Pelger (Assistant Professor of Management Science & Engineering, Stanford University). Seminar on 9th March 2022 titled: "Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference".

Dr. Katerina Petrova (Assistant Professor in Economics, UPF). Seminar on 8th December 2021 titled: "Uniform and distribution-free inference with general autoregressive processes".
Prof. Marcelo Medeiros (Department of Economics, PUC-Rio). Seminar on 6th October 2021 titled: "Bridging Factor and Sparse Models".

List of Selected Econometrics Research Papers for which I provided comments and constructive feedback:

In particular, some of following authors are indeed indebted to Dr. Christis Katsouris for raising important questions leading to the study of relevant aspects to the presented research and for valuable (written) comments as well as for prior helpful discussions on closely relevant issues.

Pitarakis, J. Y. (2025). "Serial-Dependence and Persistence Robust Inference in Predictive Regressions". Preprint arxiv:2502.00475. [February 2025 version: via research proposal April 2020 and constructive comments on the 4th Feb 2025 version]
Ettmeier, S. (2024). "No Taxation without Reallocation: the Distributional Effects of Tax Changes". Working paper, University of Bonn. [November 2024 version]
Keyan L. (2024). "Non-Gaussian Structural Vector Autoregression with Unknown Break Points". Working paper, Helsinki Graduate School of Economics. [November 2024 version]
Hoga, Y. (2024). "Persistence-Robust Break Detection in Predictive Quantile and CoVaR Regressions". Preprint arXiv:2410.05861. [October 2024 version]
Virolainen, S. (2024). "Identification by non-Gaussianity in Structural Threshold and Smooth Transition Vector Autoregressive Models". Preprint arXiv:2404.19707. [July 2024 version]
Magdalinos, T., and Petrova, K. (2024). "OLS Limit Theory for Drifting Sequences of Parameters on the Explosive Side of Unity". Working paper, Department of Economics and Business, Universitat Pompeu Fabra. [July 2024 version]

Magdalinos, T., and Petrova, K. (2022). "Uniform and Distribution-free Inference with General Autoregressive Processes". Working paper, Department of Economics and Business, Universitat Pompeu Fabra. [May 2023 version]
Olmo, J. (2022). "A Simple Cointegration Test Robust to Serial Correlation". Available at SSRN 4203205. [December 2022 version]
Spini, P. E. (2021). "Robustness, Heterogeneous Treatment Effects and Covariate Shifts". Preprint arXiv:2112.09259. [July 2022 version]

Olmo, J. (2021). "Optimal Portfolio Allocation and Asset Centrality Revisited". Quantitative Finance, 21(9), 1475-1490 [May 2020 version]
Pitarakis, J. Y. (2020). "A Novel Approach to Predictive Accuracy Testing in Nested Environments". Preprint arXiv:2008.08387. [August 2020 version]

Participation to Time Series and Machine Learning Reading Groups:

Active participation and helped in co-organizing along with Dr. Chao Zheng and Prof. Zudi Lu the Time Series and Machine Learning Reading Group at the Statistics Division, part of the School of Mathematical Sciences at the University of Southampton, UK.

Time Series and Machine Learning (October 22 - January 23)
- - Presentation 1: (17 Oct 2022). Discussion of paper: "Rates of convergence for empirical processes of stationary mixing sequences".
  - Presentation 2: (18 Nov 2022). Discussion of paper: "On Hoeffding’s inequality for dependent random variables".
Time Series and Machine Learning (February - June 2023)

Presentation 1: (12 May 2023). Discussion of paper: "Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations".
Presentation 2: (26 May 2023). Discussion of paper: "On The Rate of Convergence of A Neural Network Regression Estimate Learned by Gradient Descent".

Time Series and Machine Learning (October 2023 - January 2024)
- - Presentation 1: (8 Dec 2023). Discussion of paper: "Debiased Inference on Heterogenous Quantile Treatment Effects with Regression Rank Scores".
Time Series and Machine Learning (Februrary 2024 - June 2024)

References on Deep Neural Networks and Related Limit & Approximations Theory:

Cattaneo, M.D., and Yu, R.R. (2024). "Strong Approximations for Empirical Processes Indexed by Lipschitz Functions". Preprint arXiv:2406.04191.
Li, J., Fearnhead, P., Fryzlewicz, P., and Wang, T. (2024). "Automatic Change-Point Detection in Time Series via Deep Learning". Journal of the Royal Statistical Society Series B: Statistical Methodology, 86(2), 273-285.
Chen, J. (2024). "Robust Nonparametric Regression based on Deep ReLU Neural Networks". Journal of Statistical Planning and Inference, 233, 106182.
Jiao, Y., Shen, G., Lin, Y., and Huang, J. (2023). "Deep Nonparametric Regression on Approximate Manifolds: Nonasymptotic Error Bounds with Polynomial Prefactors". Annals of Statistics, 51(2), 691-716.
Abdeljawad, A., and Grohs, P. (2022). "Approximations with Deep Neural Networks in Sobolev Time-Space". Analysis and Applications, 20(03), 499-541.
Tang, W., Shen, G., Lin, Y., and Huang, J. (2022). "Nonparametric Quantile Regression: Non-Crossing Constraints and Conformal Prediction". Preprint arXiv:2210.10161.
Shen, G., Jiao, Y., Lin, Y., Horowitz, J.L., and Huang, J. (2021). "Deep Quantile Regression: Mitigating the Curse of Dimensionality through Composition". Preprint arXiv:2107.04907.
Farrell, M. H., Liang, T., and Misra, S. (2021). "Deep Neural Networks for Estimation and Inference". Econometrica, 89(1), 181-213.
Schmidt-Hieber, J. (2020). "Nonparametric Regression using Deep Neural Networks with ReLU Activation Function". Annals of Statistics, 48(4), 1875-1897.
Zhang, D., and Wu, W. B. (2017). "Gaussian Approximation for High Dimensional Time Series". Annals of Statistics, 45(5), 1895-1919.
Liu, W., and Wu, W. B. (2010). "Simultaneous Nonparametric Inference of Time Series". Annals of Statistics, 38(4), 2388-2421

Bibliography:

Dudley, R.M. (2014). Uniform Central Limit Theorems (Vol. 142). Cambridge University Press.
Du, K.L., and Swamy, M.N. (2013). Neural Networks and Statistical Learning. Springer Science & Business Media.
van der Vaart, A., and Wellner, J. (2013). Weak Convergence and Empirical Processes: with Applications to Statistics. Springer Science & Business Media.
Chen, X. (2007). Large Sample Sieve Estimation of Semi-Nonparametric Models. Handbook of Econometrics, 6, 5549-5632.
Györfi, L., Kohler, M., Krzyzak, A., and Walk, H. (2006). A Distribution-Free Theory of Nonparametric Regression. Springer Science & Business Media.
Ambrosio, L., Fusco, N., and Pallara, D. (2000). Functions of Bounded Variation and Free Discontinuity Problems. Oxford University Press.
Sutton, R. S., and Barto, A. G. (1998). Reinforcement Learning: An Introduction. MIT Press.
Pollard, D. (1990). Empirical Processes: Theory and Applications. IMS.
Csörgo, M. (1981). Strong Approximations in Probability and Statistics. Academic Press.

Participation to Research Seminars, Talks and Presentations:

Past Seminars:

"Aspects of Estimation and Inference for Predictive Regression Models". Econometrics Seminar, 20th September 2023 (In-person Seminar). Helsinki Graduate School of Economics, University of Helsinki, Finland.
"Aspects of Estimation and Inference for Predictive Regression Models". Research Away Day, 14th June 2023 (Short talk). Department of Economics, University of Exeter Business School, UK.

"Inference in Predictive Regression Models with a Break-Point". Departmental Seminar Series (Online Presentation), 12th May 2022. Department of Economics, University of Southampton, UK.

"Structural Break Detection in Nonstationary Quantile Time Series Models". Seminar Series (Online Seminar), 11th November 2021. Southampton Statistical Sciences Research Institute, UK.

Past Presentations:

"Testing for Structural Breaks in Predictive Regression Models" (PhD Candidacy Oral Exam). October 2020. Department of Economics, University of Southampton, UK [Admission to PhD Candidacy Examination: Succeeded & Confirmed by GSO on 22th of October 2020]. Committee: Prof. Jean-Yves Pitarakis, Prof. Tassos Magdalinos.
"Aspects of Financial Connectedness" (PhD Confirmation Presentation). October 2019. Department of Economics, University of Southampton, UK. Committee: Dr. Hector Calvo-Pardo, Prof. Tassos Magdalinos.
"Optimal Portfolio Choice under tail events" (PhD Workshop Talk). March 2019. Department of Economics, University of Southampton, UK.

"Monitoring Economic Indicators: A Sequential Break-Point Detection Study" (Master thesis Presentation). May 2017. Department of Economics, University of Cyprus, Cyprus.

Past Discussant Presentations:

Paper titled: "Predictive Accuracy Testing with IVX Approach in Predictive Regression Models". Author: Anibal Emiliano Da Silva Neto, PhD Candidate, University of Southampton. PhD Workshop, University of Southampton, Southampton, UK (March 2019). [Slides available]

Participation to Research Grantwriting Training:

"Writing a Successful Funding Application for the MSCA Postdoctoral Fellowships". Research and Innovation Foundation Cyprus, Cyprus (June 2024).
"Small Research Grant Applications for Early Career Researchers". Research Services, University of Exeter, UK (October 2022).
"Preparing Postdoctoral Research Grant Applications". School of Economic, Social and Political Sciences, University of Southampton, UK (June 2019).

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