Zacharias Bragoudakis and Ioannis Krompas

Correspondence: Zacharias Bragoudakis, ZBRAGOUDAKIS@bankofgreece.gr

National and Kapodistrian University of Athens, Greece.

pdf (547.71 Kb) | doi: https://doi.org/10.47260/bae/1224

Abstract

Building on a proper selection of macroeconomic variables for constructing a Gross Domestic Product (GDP) forecasting multivariate model (Kazanas, 2017), this paper evaluates whether alternative Bayesian model specifications can provide greater forecasting accuracy compared to a standard Vector Error Correction model (VECM). To that end, two Bayesian Vector Autoregression models (BVARs) are estimated, a BVAR using Litterman’s prior (1979) and a BVAR with time-varying parameters (TVP-VAR). The BVAR is found to have statistically significant forecasting gains against the benchmark and the TVP-VAR. Furthermore, the BVAR requires only minimal modifications to account for the effect of pandemic observations on its coefficients, only for longer-term forecasts.

Keywords:

  Bayesian VARs, Forecasting, GDP, VECM.

References

Bobeica, E. and Hartwig, B. (2023). ‘The COVID-19 shock and challenges for inflation modelling’. International Journal of Forecasting, Vol 39, pp 519-539.

Carriero, A., Mumtaz, H. Theophilopoulou, A. (2015). ‘Macroeconomic information, structural change, and the prediction of fiscal aggregates’. International Journal of Forecasting, Vol. 31 pp.325-348.

Carriero, A., Clark, T., Marcellino, M., & Mertens, E. (2022). ‘Addressing COVID-19 outliers in BVARs with stochastic volatility. Review of Economics and Statistics, pp 1-38.

Cascaldi-Garcia, D. (2022). ‘Pandemic Priors’. International Finance Discussion Papers 1352. Board Of Governors of the Federal Reserve System.

Chan, J., Jeliazkov, I. (2009). ‘Efficient simulation and integrated likelihood estimation in state space models’. International Journal of Mathematical Modelling and Numerical Optimisation, Vol.1 (1-2), pp. 101-120.

Christiano, J. L., Eichenbaum, S.M. and Trabandt, M. (2018). ‘On DSGE models’, NBER Working Paper 24811.

Cogley, T., and Sargent, T. J. (2002). ‘Evolving post-World War II US inflation dynamics’. NBER macroeconomics Annual, Vol. 16, pp. 331–388.

Cogley, T., and Sargent, T. J. (2005). ‘Drifts and volatilities: monetary policies and outcomes in the post WWII US’, Review of Economic Dynamics, Vol.8(2), pp. 262–302.

Del Negro, M. and Schorfheide, F. (2010). Bayesian Macroeconometrics, In: Del Negro, M. and Schorfheide, F.  Handbook of Bayesian Econometrics.

Dickey, D. and Fuller, W. (1981). ‘Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root’, Econometrica, Vol.49, pp. 1057-1072.

Dieppe, A., van Roye, B. & Legrand, R. (2016). ‘The BEAR toolbox’, European Central Bank Working Paper 1934.

Dieppe, A., van Roye, B. & Legrand, R. (2018). The Bayesian Estimation, Analysis and Regression (BEAR) Toolbox Technical guide.

Doan, T., Litterman, R. and Sims, C. (1983). Forecasting and Conditional Projection Using Realistic Prior Distributions, NBER Working Paper 1202.

Engle, R. and Granger, C. (1987). ‘Co-Integration and Error Correction: Representation, Estimation, and Testing’, Econometrica, Vol. 55, pp. 251-276.

Giannone, D., Lenza, M. and Primiceri, G. (2012). ‘Prior Selection for Vector Autoregressions’ Working Papers ECARES 2012-002, Universite Libre de Bruxelles.

Giannone, D., Lenza, M. and Primiceri, G. (2015). ‘Prior Selection for Vector Autoregressions’, Review of Economics and Statistics, Vol. 97, pp. 436-451.

Granger, C. (1981). ‘Some properties of time series data and their use in econometric model specification’, Journal of Econometrics, Vol. 16, pp. 121-130.

Granger, C. (2008). ‘Non-linear Models: Where Do We Go Next? – Time-Varying Parameter Models’, Studies in Nonlinear Dynamics and Econometrics, Vol. 12, pp.1-10.

Hansen, P. R., Lunde, A., & Nason, J. M. (2011). ‘The Model Confidence Set’. Econometrica, 79(2), 453–497.

Johansen, S. (1995). Likelihood-based Inference in Cointegrated Vector Autoregressive Models. Oxford: Oxford University Press.

Kazanas, T. (2017). A Vector Error Correction Forecasting Model of the Greek Economy, Hellenic Fiscal Council Working Paper 2.

Klein, L. R. (1976). ‘Project LINK: Linking National Economic Models’, Challenge, Vol. 19, pp. 25–29.

Gupta, R., Kabundi, A. (2010). ‘Forecasting macroeconomic variables in a small open economy: a comparison between small- and large-scale models’, Journal of Forecasting, Vol. 29, pp. 168-185.

Lenza, M., & Primiceri, G. E. (2022). How to estimate a VAR after March 2020. Journal of Applied Econometrics, 37, pp. 688–699.

Litterman, R., (1979). Techniques of forecasting with Bayesian vector autoregressions, Federal Reserve Bank of Minneapolis Working Papers 115.

Louzis, D. P. (2017). ‘Macroeconomic and credit forecasts during the Greek crisis using Bayesian VARs’. Empirical Economics, Vol. 53, pp. 569-598.

Louzis, D. P. (2018). ‘Greek GDP revisions and short-term forecasting’, Bank of Greece Economic Bulletin, Vol. 48.

Lubik, T. and Matthes, C. (2015). ‘Time-Varying Parameter Vector Autoregressions: Specification, Estimation and an Application’, Federal Reserve Bank of Richmond Economic Quarterly, Vol. 101, pp. 323-352.

Lucas, R. (1976). ‘Econometric policy evaluation: A critique’, Carnegie-Rochester Conference Series on Public Policy, Vol. 1, pp. 19-46.

Mackinnon, J., Haug, A., and Michelis, L. (1999). ‘Numerical Distribution Functions of Likelihood Ratio Tests for Cointegration’, Journal of Applied Econometrics, Vol. 14, pp.563-577.

Ouliaris, S., Pagan, A. R., Restrepo J. (2016). Bayesian Vars, In: Ouliaris, S., Pagan, A. R., Restrepo J.  Quantitative Macroeconomic Modelling with Structural Vector Autoregressions - An Eviews Implementation, 49-62.

Primiceri, G. (2005). ‘Time Varying Structural Vector Autoregressions and Monetary Policy’, The Review of Economic Studies, Vol. 72, pp. 821-852.

Schorfheide, F., & Song, D. (2021). Real-time forecasting with a (standard) mixed frequency VAR during a pandemic. Working Paper No. 29535. National Bureau of Economic Research.

Sims, C. (1980). ‘Macroeconomics and Reality’, Econometrica, Vol. 48, 1-48.

Sims, C. (2002). ‘Comments on Cogley and Sargent’s ‘Evolving Post-World War II U.S. Inflation Dynamics’, NBER Macroeconomics Annual 2001, Vol. 16, pp. 373-379.

Sims, C. and Zha, T. (1998). ‘Bayesian Methods for Dynamic Multivariate Models’, International Economic Review, Vol. 39, pp. 949-968.

Stock, J. and Watson, W. (2001). ‘Vector Autoregressions’, The Journal of Economic Perspectives, Vol. 15, pp. 101-115.

Swamy, P. (1975). ‘Bayesian and Non-Bayesian Analysis of Switching Regressions and a Random Coefficient Regression Model’, Journal of the American Statistical Association, Vol. 70, pp. 593-602.

Sznajderska, A. and Haug, A. (2023). ‘Bayesian VARs of the U.S. economy before and during the pandemic’. Eurasian Economic Review Vol.13, pp.211-236.