Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-10T13:39:39.417Z Has data issue: false hasContentIssue false

A SUGGESTION FOR A DYNAMIC MULTIFACTOR MODEL (DMFM)

Published online by Cambridge University Press:  11 January 2021

Heather D. Gibson
Affiliation:
Bank of Greece
Stephen G. Hall
Affiliation:
University of Leicester, Bank of Greece and University of Pretoria
George S. Tavlas*
Affiliation:
Bank of Greece and Hoover Institution, Stanford University
*
Address correspondence to: George S. Tavlas, Bank of Greece, 21 E Venizelos Ave, Athens, 10250, Greece. e-mail: gtavlas@bankofgreece.gr. Phone: +30 210 320 2370; Fax: +30 210 320 2432.

Abstract

We provide a new way of deriving a number of dynamic unobserved factors from a set of variables. We show how standard principal components may be expressed in state space form and estimated using the Kalman filter. To illustrate our procedure, we perform two exercises. First, we use it to estimate a measure of the current account imbalances among northern and southern euro area countries that developed during the period leading up to the outbreak of the euro area crisis, before looking at adjustment in the post-crisis period. Second, we show how these dynamic factors can improve forecasting of the euro exchange rate.

Type
Articles
Copyright
© Cambridge University Press 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Artis, M., Banerjee, A. and Marcellino, M. (2001) Factor forecasts for the UK. IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University, Working Papers 203.Google Scholar
Barnett, W., Chauvet, M. and Tierney, H. (2009) Measurement error in monetary aggregates: A Markov switching factor approach. Macroeconomic Dynamics 13(S2), 381412.CrossRefGoogle Scholar
Caraiani, P. (2017) Evaluation exchange rate forecasts along time and frequency. International Review of Economics and Finance 51(C), 315330.CrossRefGoogle Scholar
Cuthbertson, K., Hall, S. G. and Taylor, M. P. (1992) Applied Econometric Techniques. Ann Arbor: University of Michigan Press.Google Scholar
Doz, C., Giannone, D. and Reichlin, L. (2011) A two-step estimator for large approximate dynamic factor models based on Kalman filtering. Journal of Econometrics 164(1), 188205.CrossRefGoogle Scholar
Doz, C., Giannone, D. and Reichlin, L. (2012) A quasi-maximum likelihood approach for large approximate dynamic factor models. The Review of Economics and Statistics 94(4), 10141024.CrossRefGoogle Scholar
Forni, M., Hallin, M., Lippi, M. and Reichlin, L. (2000) The generalized dynamic-factor model: Identification and estimation. The Review of Economics and Statistics 82(4), 540554.CrossRefGoogle Scholar
Fuleky, P. and Bonham, C. (2015) Forecasting with mixed-frequency factor models in the presence of common trends. Macroeconomic Dynamics 19(4), 753775.CrossRefGoogle Scholar
Garratt, A. and Hall, S. G. (1996) Measuring underlying economic activity. Journal of Applied Econometrics II, 135151.3.0.CO;2-Z>CrossRefGoogle Scholar
Giannone, D., Reichlin, L. and Small, D. (2008) Nowcasting: The real time informational content of macroeconomic data releases. Journal of Monetary Economics 55, 665676.CrossRefGoogle Scholar
Gibson, H. D., Hall, S. G., Petroulas, P. and Tavlas, G. S. (2020) On the effects of the ECB’s funding policies on bank lending. Journal of International Money and Finance 102(C), 102112.CrossRefGoogle Scholar
Gibson, H. D., Hall, S. G. and Tavlas, G. S. (2016) How the euro-area sovereign-debt crisis led to a collapse in bank equity prices. Journal of Financial Stability 26(C), 266275.CrossRefGoogle Scholar
Gibson, H. D., Hall, S. G. and Tavlas, G. S. (2017) Self-fulfilling dynamics: The interactions of sovereign spreads, sovereign ratings and bank ratings during the euro financial crisis. Journal of International Money and Finance 73, 371385.CrossRefGoogle Scholar
Gibson, H. D., Hall, S. G. and Tavlas, G. S. (2018) Measuring systemic vulnerability in European banking systems. Journal of Financial Stability 36(C), 279–92.CrossRefGoogle Scholar
González, E., Melo, L. F., Monroy, V. and Rojas, B. (2009) A dynamic factor model for the Colombian inflation. Borradores de Economia, 549, Banco de la Republica de Colombia.Google Scholar
Harvey, A. (1989) Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge, UK: Cambridge University Press.Google Scholar
Henzel, S. and Wieland, E. (2017) International synchronization and changes in long-term inflation uncertainty. Macroeconomic Dynamics 21(4), 918946.CrossRefGoogle Scholar
Jazwinski, A. H. (1970) Stochastic Processes and Filtering Theory. New York: Academic Press, isbn 0-12-381550-9.Google Scholar
Joliffe, I. T. and Cadima, J. (2016) Principal component analysis: A review and recent developments. Philosophical Transactions A374 Royal Society, doi: 10.1098/rsta.2015.0202.CrossRefGoogle Scholar
Meese, R. A. and Rogoff, K. (1983) Empirical exchange rate models of the seventies: Do they fit out of sample. Journal of International Economics 14(1–2), 324.CrossRefGoogle Scholar
Rossi, B. (2013) Exchange rate predictability. Journal of Economic Literature 51(4), 10631119.CrossRefGoogle Scholar
Stock, J. H. and Watson, M. W. (1989) New indexes of coincident and leading economic indicators. In: Olivier Jean, Blanchard and Stanley, Fischer (eds.), NBER Chapters NBER Macroeconomics Annual 1989, Volume 4, pp. 351409. Cambridge, MA: MIT Press.Google Scholar
Stock, J. H. and Watson, M. W. (1999) Forecasting inflation. Journal of Monetary Economics 44(2), 293335.CrossRefGoogle Scholar
Stock, J. H. and Watson, M. W. (2002a) Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association 97(460), 11671179.CrossRefGoogle Scholar
Stock, J. H. and Watson, M. W. (2002b) Macroeconomic forecasting using diffusion indexes. Journal of Business and Economics Statistics 20(2), 147162.CrossRefGoogle Scholar
Ubilava, D. (2019) On the relationship between financial instability and economic performance: Stressing the business of nonlinear modeling. Macroeconomic Dynamics 23(1), 80100.CrossRefGoogle Scholar
Zaher, F. (2007) Evaluating factor forecasts for the UK: The role of asset prices. International Journal of Forecasting 23(4), 679693.CrossRefGoogle Scholar
Ziegler, C. and Eickmeier, S. (2008) How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach. Journal of Forecasting, 27, 237265.Google Scholar