Kyriakopoulou, Dimitra (2025): A Shrinkage Factor-Augmented VAR for High-Dimensional Macro–Fiscal Dynamics.
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Abstract
We propose a ridge-regularized Factor-Augmented Vector Autoregression (FAVAR) for forecasting macro–fiscal systems in data-rich environments where the cross-sectional dimension is large relative to the available sample. The framework combines principal-component factor extraction with a shrinkage-based VAR for the joint dynamics of observed macro–fiscal variables and latent components. Applying the model to Greece, we show that the extracted factors capture meaningful real and nominal structures, while the ridge-regularized VAR delivers stable impulse responses and coherent short- and medium-term dynamics for variables central to the sovereign debt identity. A recursive out-of-sample evaluation indicates that the ridge-FAVAR systematically improves medium-term forecasting accuracy relative to standard AR benchmarks, particularly for real GDP growth and the interest–growth differential. The results highlight the usefulness of shrinkage-augmented factor models for macro–fiscal forecasting and motivate further econometric work on regularized state-space and structural factor VARs.
| Item Type: | MPRA Paper |
|---|---|
| Original Title: | A Shrinkage Factor-Augmented VAR for High-Dimensional Macro–Fiscal Dynamics |
| Language: | English |
| Keywords: | FAVAR, Ridge Regression, Forecasting, High-Dimensional Data, Fiscal Policy, Debt Dynamics, Macro–Fiscal Modelling |
| Subjects: | C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C38 - Classification Methods ; Cluster Analysis ; Principal Components ; Factor Models C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C55 - Large Data Sets: Modeling and Analysis E - Macroeconomics and Monetary Economics > E6 - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook > E62 - Fiscal Policy H - Public Economics > H6 - National Budget, Deficit, and Debt > H63 - Debt ; Debt Management ; Sovereign Debt |
| Item ID: | 127158 |
| Depositing User: | Prof Dimitra Kyriakopoulou |
| Date Deposited: | 21 Jan 2026 10:14 |
| Last Modified: | 21 Jan 2026 10:14 |
| References: | Abbate, A., Eickmeier, S., Lemke, W., & Marcellino, M. (2016). The changing international transmission of financial shocks: Evidence from a classical time-varying FAVAR. Journal of Money, Credit and Banking, 48(4), 573–601. https://doi.org/10.1111/jmcb.12311 Angelini, E., Camba-Mendez, G., Giannone, D., Reichlin, L., & Rünstler, G. (2011). Short-term forecasts of euro area GDP growth. The Econometrics Journal, 14(1), C25–C44. https://doi.org/10.1111/j.1368-423X.2010.00328.x Bai, J. (2003). Inferential theory for factor models of large dimensions. Econometrica, 71(1), 135–171. https://doi.org/10.1111/1468-0262.00392 Bai, J., & Ng, S. (2002). Determining the number of factors in approximate factor models. Econometrica, 70(1), 191–221. https://doi.org/10.1111/1468-0262.00273 Bai, J., & Ng, S. (2008). Large dimensional factor analysis. Foundations and Trends in Econometrics, 3(2), 89–163. https://doi.org/10.1561/0800000002 Banbura, M., Giannone, D., Modugno, M., & Reichlin, L. (2013). Now-casting and the real-time data flow. In G. Elliott & A. Timmermann (Eds.), Handbook of Economic Forecasting (Vol. 2A, pp. 195–237). Elsevier. https://doi.org/10.1016/B978-0-444-53683-9.00004-9 Banbura, M., & Modugno, M. (2014). Maximum likelihood estimation of factor models on datasets with arbitrary pattern of missing data. Journal of Applied Econometrics, 29(1), 133–160. https://doi.org/10.1002/jae.2306 Belke, A., & Osowski, T. (2019). International effects of euro area versus U.S. policy uncertainty: A FAVAR approach. Economic Inquiry, 57(1), 453–481. https://doi.org/10.1111/ecin.12701 Bernanke, B. S., Boivin, J., & Eliasz, P. (2005). Measuring the effects of monetary policy: A factor-augmented vector autoregressive (FAVAR) approach. Quarterly Journal of Economics, 120(1), 387–422. https://doi.org/10.1162/0033553053327452 Boeckx, J., Dossche, M., & Peersman, G. (2017). Effectiveness and transmission of the ECB’s balance sheet policies. International Journal of Central Banking, 13(1), 297–333. Bok, B., Caratelli, D., Giannone, D., Sbordone, A. M., & Tambalotti, A. (2018). Macroeconomic nowcasting and forecasting with big data. Annual Review of Economics, 10, 615–643. https://doi.org/10.1146/annurev-economics-080217-053214 Carriero, A., Clark, T. E., & Marcellino, M. (2020). Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors. Journal of Econometrics, 218(2), 507–528. De Mol, C., Giannone, D., & Reichlin, L. (2008). Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components? Journal of Econometrics, 146(2), 318–328. https://doi.org/10.1016/j.jeconom.2008.08.011 Dendramis, Y., Dimitrakopoulos, G., & Tzavalis, E. (2025). Assessing downside public debt risks in an environment of negative interest rates–growth differentials. Working Paper 02-2025, Athens University of Economics and Business. Doz, C., Giannone, D., & Reichlin, L. (2011). A two-step estimator for large approximate dynamic factor models based on Kalman filtering. Journal of Econometrics, 164(1), 188–205. https://doi.org/10.1016/j.jeconom.2011.02.012 Giannone, D., Reichlin, L., & Small, D. (2008). Nowcasting: The real-time informational content of macroeconomic data. Journal of Monetary Economics, 55(4), 665–676. https://doi.org/10.1016/j.jmoneco.2008.05.010 Gilchrist, S., Yankov, V., & Zakrajsek, E. (2009). Credit market shocks and economic activity. NBER Working Paper No. 14863, National Bureau of Economic Research. Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55–67. https://doi.org/10.1080/00401706.1970.10488634 Hosszu, Z. (2018). The impact of credit supply shocks and a new financial conditions index based on a FAVAR approach. Economic Systems, 42(1), 32–44. Hsu, C.-W., & Phillips, P. C. B. (2016). Regularization methods for high-dimensional vector autoregressions. Cowles Foundation Discussion Paper No. 2041. Jimborean, R., & Mesonnier, J.-S. (2010). Banks’ financial conditions and the transmission of monetary policy: A FAVAR approach. International Journal of Central Banking, 6(4), 71–117. Kock, A. B., & Callot, L. A. (2015). Oracle inequalities for high-dimensional vector autoregressions. Journal of Econometrics, 186(2), 325–344. https://doi.org/10.1016/j.jeconom.2015.02.026 Koop, G., & Korobilis, D. (2014). A new index of financial conditions. European Economic Review, 71, 101–116. Madhou, A. (2020). Forecasting the GDP of a small open developing economy. Applied Economics, 52(17), 1845–1856. https://doi.org/10.1080/00036846.2019.1679346 Potjagailo, G. (2016). Spillover effects from euro area monetary policy across the EU: A factor-augmented VAR approach. Kiel Working Paper No. 2033, Kiel Institute for the World Economy. Rahimov, V. (2020). Augmented vector autoregressive (FAVAR) model: Evidence from Azerbaijan. Working Paper, Heidelberg University. Stock, J. H., & Watson, M. W. (2002). Macroeconomic forecasting using diffusion indexes. Journal of Business and Economic Statistics, 20(2), 147–162. https://doi.org/10.1198/073500102317351921 Stock, J. H., & Watson, M. W. (2016). Dynamic factor models, factor-augmented vector autoregressions, and structural vector autoregressions in macroeconomics. In Handbook of Macroeconomics (Vol. 2, pp. 415–525). Elsevier. |
| URI: | https://mpra.ub.uni-muenchen.de/id/eprint/127158 |

