Embedded Files
Working papers
Working papers
Gambetti, L., Korobilis, D., Tsoukalas, J. and Zanetti, F. (2023). “Agreed and disagreed uncertainty” Revision requested by Review of Economic Studies
Summary: When agents' information is imperfect and dispersed, existing measures of macroeconomic uncertainty based on the forecast error variance have two distinct drivers: the variance of the economic shock and the variance of the information dispersion. The former driver increases uncertainty and reduces agents' disagreement (agreed uncertainty). The latter increases both uncertainty and disagreement (disagreed uncertainty). We use these implications to identify empirically the effects of agreed and disagreed uncertainty shocks, based on a novel measure of consumer disagreement derived from survey expectations. Disagreed uncertainty has no discernible economic effects and is benign for economic activity, but agreed uncertainty exerts significant depressing effects on a broad spectrum of macroeconomic indicators.
Korobilis, D., Mamatzakis, E. and Pappas, V. (2024). “Bayesian Nonparametric Inference in Bank Business Models with Transient and Persistent Cost Inefficiency” Revision requested by Journal of Econometrics
Summary: This paper introduces a novel econometric framework for identifying and modeling bank business models (BBMs), which dynamically evolve in response to changing financial and economic conditions. Building on the stochastic frontier literature, we extend the traditional cost-efficiency models by decomposing inefficiency into persistent and transient components. We propose a Bayesian nonparametric approach that adapts to the data through an infinite mixture model with predictor-dependent clustering, enabling a flexible classification of banks into distinct business models. Our method, based on the Logit Stick-Breaking Process (LSBP), provides a data-driven way to capture the heterogeneity in bank strategies, allowing for dynamic transitions between business models over time. This model offers a significant advancement over existing parametric and kernel-based approaches by combining the scalability of nonparametric methods with efficient computational routines. We apply the model to a dataset of European banks and identify four distinct business model clusters, providing novel insights into the evolution of bank performance and efficiency. Our findings contribute to the growing literature on the identification and measurement of bank business models, offering valuable implications for policy and regulatory frameworks.
Summary: I introduce a high-dimensional Bayesian vector autoregressive (BVAR) framework designed to estimate the effects of conventional monetary policy shocks. The model captures structural shocks as latent factors, enabling computationally efficient estimation in high-dimensional settings through a straightforward Gibbs sampler. By incorporating time variation in the effects of monetary policy while maintaining tractability, the methodology offers a flexible and scalable approach to empirical macroeconomic analysis using BVARs, well-suited to handle data irregularities observed in recent times. Applied to the U.S. economy, I identify monetary shocks using a combination of high-frequency surprises and sign restrictions, yielding results that are robust across a wide range of specification choices. The findings indicate that the Federal Reserve's influence on disaggregated consumer prices fluctuated significantly during the 2022-24 high-inflation period, shedding new light on the evolving dynamics of monetary policy transmission.
Summary: From the perspective of flexible inflation targeting using a simple targeting rule, this paper introduces the Monetary Policy Deviation Error (MPDE) as a novel metric for assessing central bank performance and deliberations. The MPDE captures potentially time-varying shifts in the trade-off between stabilizing inflation and supporting real economic activity. Specifically, it quantifies the gap between the intended trade-off envisioned by policymakers and the trade-off realized through actual monetary policy outcomes. Under an optimal and unbiased monetary policy strategy, the MPDE should average to zero. Nonzero deviations indicate misalignment between the central bank's stated objectives and the trade-offs actually achieved, suggesting that an alternative interest rate path would have better aligned outcomes with intentions. Applying the MPDE to evaluate the monetary policy strategies of Norges Bank and the Reserve Bank of New Zealand, we find posterior evidence supporting optimal policy alignment in the case of New Zealand.
Publications in academic journals
Publications in academic journals
1. Korobilis, D. and Schröder, M. (forthcoming). “Probabilistic Quantile Factor Analysis,” Journal of Business and Economic Statistics.
[Working paper] [Published version] [MATLAB code] [Online Supplement] [Presentation slides]
2. Korobilis, D. and Schröder, M. (forthcoming). “Monitoring Multi-Country Macroeconomic Risk: A Quantile Factor-Augmented Vector Autoregressive (QFAVAR) Approach,” Journal of Econometrics.
[Working paper] [Published version] [MATLAB code] [Online Supplement] [Presentation slides]
3. Koop, G. and Korobilis, D. (2023) “Bayesian Dynamic Variable Selection in High Dimensions,” International Economic Review, 64, 1047-1074.
[Working paper] [Published version] [MATLAB code] [Online Appendix] [Presentation slides]
4. Korobilis, D. (2022). “A New Algorithm for Structural Restrictions in Bayesian Vector Autoregressions,” European Economic Review, 148, 104241.
[Working paper] [Published version] [MATLAB code] [Online Appendix] [Presentation slides]
5. Baumeister, C., Korobilis, D. and Lee, T. K. (2022). “Energy Markets and Global Economic Conditions,” Review of Economics and Statistics, 104, 828-844.
[Working paper] [Published version] [Data for GECON index] [Replication code]
6. Korobilis, D. (2021). “High-Dimensional Macroeconomic Forecasting using Message Passing Algorithms,” Journal of Business and Economic Statistics, 39, 493-504.
[Working paper] [Published version] [MATLAB code] [Online Appendix] [Presentation Slides]
7. Beckmann, J, Koop, G., Korobilis, D. and Schüssler, R. (2020). “Exchange Rate Predictability and Dynamic Bayesian Learning,” Journal of Applied Econometrics, 35, 410-421.
[Working paper] [Published version] [MATLAB code] [Online Appendix]
8. Koop, G. and Korobilis, D. (2019). “Forecasting with High-Dimensional Panel VARs,” Oxford Bulletin of Economics and Statistics, 81, 937-959.
[Working paper] [Published version] [MATLAB Code] [Presentation Slides]
9. Korobilis, D. and Pettenuzzo, D. (2019). “Adaptive Hierarchical Priors for High-Dimensional Vector Autoregressions,” Journal of Econometrics, 212, 241-271.
[Working paper] [Published version] [MATLAB Code] [Presentation Slides]
10. Koop, G., Korobilis, D. and Pettenuzzo, D. (2019). “Bayesian Compressed Vector Autoregressions,” Journal of Econometrics, 210, 135-154.
[Working paper] [Published version] [Online Appendix] [MATLAB Code] [Presentation Slides]
11. Byrne, J., Cao, S. and Korobilis, D. (2019). “Decomposing Global Yield Curve Co-Movement,” Journal of Banking and Finance, 106, 500-513.
[Working paper] [Published version]
12. Byrne, J., Korobilis, D. and Ribeiro, P. (2018). “On the Sources of Uncertainty in Exchange Rate Predictability,” International Economic Review, 59, 329-357.
[Working paper] [Published version] [Online Appendix]
13. Byrne, J., Cao, S. and Korobilis, D. (2017). “Forecasting the Term Structure of Government Bond Yields in Unstable Environments,” Journal of Empirical Finance, 44, 209-225.
[Working paper] [Published version]
14. Korobilis, D. (2017). “Quantile Regression Forecasts of Inflation under Model Uncertainty,” International Journal of Forecasting, 33, 11-20.
[Working paper] [Published version] [MATLAB Code] [Data]
15. Korobilis, D. (2016). “Prior Selection for Panel Vector Autoregressions,” Computational Statistics and Data Analysis, 101, 110-120.
[Working paper] [Published version] [MATLAB Code]
16. Byrne, J., Korobilis, D. and Ribeiro, P. (2016). “Exchange Rate Predictability in a Changing World,” Journal of International Money and Finance, 62, 1-24.
[Working paper] [Published version] [MATLAB Code]
17. Koop, G. and Korobilis, D. (2015). “Model Uncertainty in Panel Vector Autoregressions,” European Economic Review, 81, 115-131.
[Working paper] [Published version] [MATLAB Code] [Presentation Slides]
18. Bauwens, L, Koop, G., Korobilis, D. and Rombouts, J. (2015). “The Contribution of Structural Break Models to Forecasting Macroeconomic Series,” Journal of Applied Econometrics, 30, 596-620.
[Working paper] [Published version] [Online Appendix]
19. Koop, G. and Korobilis, D. (2014). “A New Index of Financial Conditions,” European Economic Review, 71, 101-116.
[Working paper] [Published version] [MATLAB Code] [Presentation Slides]
20. Belmonte, M., Koop, G. and Korobilis, D. (2014). “Hierarchical Shrinkage in Time-Varying Coefficients Models,” Journal of Forecasting, 33, 80-94.
[Working paper] [Published version] [MATLAB Code]
21. Koop, G. and Korobilis, D. (2013). “Large Time-Varying Parameter VARs,” Journal of Econometrics, 177, 185-198.
[Working paper] [Published version] [MATLAB Code] [Presentation Slides]
22. Korobilis, D. (2013). “VAR Forecasting Using Bayesian Variable Selection,” Journal of Applied Econometrics, 28, 204-230.
[Working paper] [Published version] [MATLAB Code] [Presentation Slides] [Old poster presentation]
23. Korobilis, D. (2013). “Assessing the Transmission of Monetary Policy Shocks Using Time-Varying Parameter Dynamic Factor Models,” Oxford Bulletin of Economics and Statistics, 75, 157–179.
[Working paper] [Published version] [MATLAB Code] [Presentation Slides]
24. Korobilis, D. (2013). “Bayesian Forecasting with Highly Correlated Predictors,” Economics Letters, 118, 148-150.
[Working paper] [Published version] [Data Appendix] [Technical Appendix] [MATLAB Code]
25. Korobilis, D. (2013). “Hierarchical Shrinkage Priors for Dynamic Regressions with Many Predictors,” International Journal of Forecasting, 29, 43-59.
[Working paper] [Published version] [MATLAB Code] [Presentation Slides]
26. Koop, G. and Korobilis, D. (2012). “Forecasting Inflation Using Dynamic Model Averaging,” International Economic Review, 53, 867-886.
[Working paper] [Published version] [MATLAB Code] [Presentation Slides]
Other journal publications
Other journal publications
Koop, G., Korobilis, D and Ravazzolo, F. (2024). “Editorial Introduction of the Special Issue of the Studies in Nonlinear Dynamics and Econometrics in honor of Herman van Dijk”, Studies in Nonlinear Dynamics and Econometrics, 28, 151-153.
2. Korobilis, D. and Montoya-Blandón, S. (2023). “Discussion of "Multivariate Dynamic Modeling for Bayesian Forecasting of Business Revenue"”, Applied Stochastic Models in Business and Industry.
[Working paper] [Published version]
3. Gambetti, L, Görtz, C., Korobilis, D., Tsoukalas, J. and Zanetti, F. (2022). “The Effect of News Shocks and Monetary Policy ”, Advances in Econometrics, 44A, 139-164.
[Working paper] [Published version]
4. Gilmartin, M. and Korobilis, D. (2012). “On Regional Unemployment: An Empirical Examination of the Determinants of Geographical Differentials in the UK”, Scottish Journal of Political Economy, 59, 179-195.
[Working paper] [Published version] [Presentation Slides]
5. Koop, G. and Korobilis, D. (2011). “UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When do they do So?”, Economic Modelling, 28, 2307-2318.
[Working paper] [Published version] [MATLAB Code]
6. Korobilis, D. (2008). “Forecasting in Vector Autoregressions with Many Predictors”, Advances in Econometrics, 23, 403-431.
Published Monographs, Books and Chapters
Published Monographs, Books and Chapters
1. Korobilis, D. and Shimizu, K. (2022). “Bayesian Approaches to Shrinkage and Sparse Estimation”, Foundations and Trends in Econometrics, 11, 230-354.
[Working paper] [Published version] [Techincal Document] [MATLAB code]
2. Korobilis, D. and Pettenuzzo, D. (2020). “Machine Learning Econometrics: Bayesian Algorithms and Methods”, Oxford Research Encyclopedia of Economics and Finance
[Working paper] [Published version]
3. Bauwens, L. and Korobilis, D. (2013). “Bayesian Methods”, Handbook of Research Methods and Applications in Empirical Macroeconomics, Chapter 16.
[Working paper] [Published version]
4. Koop, G. and Korobilis, D. (2010). “Bayesian Multivariate Time Series Methods for Empirical Macroeconomics”, Foundations and Trends in Econometrics, 3, 267-358.
[Working paper] [Published version] [Chinese Translation] [MATLAB Code]
Permanent Working Papers
Permanent Working Papers
Korobilis, D., Lundau, B., Musso, A. and Phella, A. (2021). “The time-varying evolution of inflation risks”, ECB working paper no 2600
Korobilis, D. (2018). “Machine Learning Macroeconometrics: A Primer”
Korobilis, D. and Yilmaz, K. (2018). “Measuring Dynamic Connectedness with Large Bayesian VAR Models”
Korobilis, D. and Schumacher, C. (2014). “Efficient Estimation and Forecasting in Dynamic Factor Models with Structural Instability”
Korobilis, D. (2014). “Data-Based Priors for Vector Autoregressions with Drifting Coefficients”
Korobilis, D. (2013). “Forecasting with Factor Models: A Bayesian Model Averaging Perspective”
Published Theses
Published Theses
PhD Thesis submitted to the Department of Economics, University of Strathclyde: PhD Thesis
MSc Thesis submitted to the Department of Econometrics, Erasmus University Rotterdam: MSc Thesis
Dimitrios Korompilis Magkas
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