Forecasting with factor-augmented error correction models

International Journal of Forecasting

Volumn 30, Issue 3, 2014, Pages 589-612

  Banerjee, Anindya ^a,b   Marcellino, Massimiliano ^c,d,e   Masten, Igor ^f  

^a Banque de France ^*  (France)

^b UNIVERSITY OF BIRMINGHAM   (United Kingdom)

^c EUROPEAN UNIVERSITY INSTITUTE   (Italy)

^d BOCCONI UNIVERSITY   (Italy)

^e CEPR   (United Kingdom)

^f UNIVERSITY OF LJUBLJANA   (Slovenia)

Author keywords

Cointegration; Dynamic factor models; Error correction models; Factor augmented error correction models; FAVAR; Forecasting

Indexed keywords

EID: 84902252456     PISSN: 01692070     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijforecast.2013.01.009     Document Type: Article

References (34)

1. Bai J. Estimating cross-section common stochastic trends in nonstationary panel data. Journal of Econometrics 2004, 122:137-183.
2. Bai J., Kao C., Ng S. Panel cointegration with global stochastic trends. Journal of Econometrics 2009, 149:82-99.
3. Bai J., Ng S. Determining the number of factors in approximate factor models. Econometrica 2002, 70:191-221.
4. Bai J., Ng S. A PANIC attack on unit roots and cointegration. Econometrica 2004, 72:1127-1177.
5. Bai J., Ng S. Confidence intervals for diffusion index forecasts with a large number of predictors and inference for factor-augmented regressions. Econometrica 2006, 74:1133-1150.
6. Banerjee A., Marcellino M. Factor-augmented error correction models. The methodology and practice of econometrics - a festschrift for David Hendry 2009, 227-254. Oxford University Press, Oxford. J.L. Castle, N. Shephard (Eds.).
7. Banerjee A., Marcellino M., Masten I. Forecasting macroeconomic variables using diffusion indexes in short samples with structural change. Forecasting in the presence of structural breaks and model uncertainty 2008, 149-194. Elsevier, Amsterdam. M.E. Wohar, D.E. Rapach (Eds.).
8. Bernanke B.S., Boivin J., Eliasz P. Measuring the effects of monetary policy: a factor-augmented vector autoregressive (FAVAR) approach. Quarterly Journal of Economics 2005, 120:387-422.
9. Carriero A., Kapetanios G., Marcellino M. Forecasting exchange rates with a large Bayesian VAR. International Journal of Forecasting 2009, 25:400-417.
10. Carriero A., Kapetanios G., Marcellino M. Forecasting government bond yields with large Bayesian VARs. Journal of Banking and Finance 2012, 36(7):2026-2047.
11. Cheng X., Phillips P.C.B. Semiparametric cointegrating rank selection. Econometrics Journal 2009, 12(s1):S83-S104.
12. Clark T.E., West K.D. Approximately normal tests for equal predictive accuracy in nested models. Journal of Econometrics 2007, 138(1):291-311.
13. Clements M.P., Hendry D.F. Forecasting in cointegrated systems. Journal of Applied Econometrics 1995, 10:127-146.
14. D'Agostino, A., Giannone, D., & Surico, P. (2007). (Un)predictability and macroeconomic stability. CEPR Discussion Papers 6594, C.E.P.R. Discussion Papers.
15. Diebold F.X., Li C. Forecasting the term structure of government bond yields. Journal of Econometrics 2006, 130:337-364.
16. Eickmeier S., Ziegler C. How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach. Journal of Forecasting 2008, 27:237-265.
17. Engle R.F., Granger C.W. Co-integration and error correction: representation, estimation and testing. Econometrica 1987, 55:257-276.
18. Engle R.F., Kozicki S. Testing for common features. Journal of Business and Economic Statistics 1993, 11:369-390.
19. Engel C.M., West K.D. Exchange rate and fundamentals. Journal of Political Economy 2005, 113:485-517.
20. Favero C., Marcellino M., Neglia F. Principal components at work: the empirical analysis of monetary policy with large data sets. Journal of Applied Econometrics 2005, 20:603-620.
21. Forni M., Hallin M., Lippi M., Reichlin L. The generalized dynamic-factor model: identification and estimation. Review of Economics and Statistics 2000, 82:540-554.
22. Gengenbach, C., Urbain, J.-P., & Westerlund, J. (2008). Panel error correction testing with global stochastic trends. METEOR Research Memorandum 51.
23. Gürkaynak R.S., Levin A., Swanson E. Does inflation targeting anchor long-run inflation expectations? Evidence from the U.S., U.K., and Sweden. Journal of the European Economic Association 2010, 8:1208-1242.
24. Hall D.A., Anderson H.M., Granger C.W.J. A cointegration analysis of Treasury Bill yields. Review of Economics and Statistics 1992, 75:116-126.
25. Johansen S. Likelihood-based inference in cointegrated vector autoregressive models 1995, Oxford University Press, Oxford and New York.
26. Lam C., Yao Q., Bathia N. Estimation of latent factors for high-dimensional time series. Biometrika 2011, 98(4):901-918.
27. Marcellino M., Schumacher C. Factor MIDAS for nowcasting and forecasting with ragged-edge data: a model comparison for German GDP. Oxford Bulletin of Economics and Statistics 2010, 72:518-550.
28. Marcellino M., Stock J.H., Watson M.W. A comparison of direct and iterated AR methods for forecasting macroeconomic series h-steps ahead. Journal of Econometrics 2006, 135:499-526.
29. Meese R., Rogoff K. Empirical exchange rate models of the seventies: do they fit out of sample?. Journal of International Economics 1983, 14:3-24.
30. Rudebusch, G. D., & Svensson, L. E. O. (1998). Policy rules for inflation targeting. NBER Working Paper No.6512.
31. Stock J.H., Watson M.W. Testing for common trends. Journal of the American Statistical Association 1998, 83:1097-1107.
32. Stock J.H., Watson M.W. Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association 2002, 97:1167-1179.
33. Stock J.H., Watson M.W. Macroeconomic forecasting using diffusion indexes. Journal of Business and Economic Statistics 2002, 20:147-162.
34. Stock, J. H., & Watson, M. W. (2005). Implication of dynamic factor models for VAR analysis. NBER Working Paper 11467.