A review of forecasting techniques for large data sets

National Institute Economic Review

Volumn 203, Issue 1, 2008, Pages 109-115

  Eklund, Jana ^a   Kapetanios, George ^b  

^a BANK OF ENGLAND   (United Kingdom)

^b QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

Factors; Forecast combinations; Large datasets

Indexed keywords

EID: 67649775478     PISSN: 00279501     EISSN: 17413036     Source Type: Journal    
DOI: 10.1177/00279501082030011201     Document Type: Article

References (31)

1. Akaike, H. (1978), A Bayesian analysis of the minimum AIC procedure, Annals of the Institute of Statistical Mathematics, 30.
2. Akaike, H. (1979), A Bayesian extension of the minimum AIC procedure of autoregressive model fitting, Biometrika, 66.
3. Banburra, M., Giannone, D. and Reichlin, L. (2007), Bayesian VARS with large panels, CEPR Discussion Paper no. 6326.
4. Boivin, J. and Ng, S. (2006), Are more data always better for factor analysis?, Journal of Econometrics, 312, pp. 169-94.
5. Brüggemann, R., Krolzig, H.M. and Lütkepohl, H. (2003), Comparison of model reduction methods for VAR processes, Technical Report 2003-W13, Nuffield College, University of Oxford.
6. Buhlmann, P. (2006), Boosting for high-dimensional linear models, Annals of Statistics, 34, pp. 559-83.
7. Burnham, K.P. and Anderson, D.R. (1998), Model Selection and Inference, Berlin, Springer Verlag.
8. Carriero, A., Kapetanios, G. and Marcellino, M. (2007), Forecasting large datasets with reduced rank multivariate models, Queen Mary, University of London Working Paper no. 617.
9. De Mol, C., Giannone, D. and Reichlin, L. (2006), Forecasting using a large number of predictorsis Bayesian regression a valid alternative to principal components?, European Central Bank Working Paper Series no. 700.
10. Dorsey, R.E. and Mayer, W.J. (1995), Genetic algorithms for estimation problems with multiple optima, nondifferentiability and other irregular features, Journal of Business and Economic Statistics, 13(1), pp. 53-66.
11. Eklund, J. and Karlsson, S. (2007), Forecast combination and model averaging using predictive measures, Econometric Reviews, 26(2-4), pp. 329-63.
12. Fernandez, C., Ley, E. and Steel, M.F.J. (2001), Benchmark priors for Bayesian model averaging, Journal of Econometrics, 100, pp. 381-427.
13. Forni, M., Hallin, M., Lippi, M. and Reichlin, L. (2000), The generalised factor model: identification and estimation, Review of Economics and Statistics, 82, pp. 540-54.
14. Forni, M., Hallin, M., Lippi, M. and Reichlin, L. (2004), The generalised factor model: consistency and rates, Journal of Econometrics, 119, pp. 231-55.
15. Forni, M., Hallin, M., Lippi, M. and Reichlin, L. (2005), The generalized dynamic factor model: one-sided estimation and forecasting, Journal of the American Statistical Association, 100 (471), pp. 830-40.
16. Hendry, D.F. (1995), Dynamic Econometrics, Oxford, Oxford University Press.
17. Hendry, D.F. (1997), On congruent econometric relations: a comment, Carnegie-Rochester Conference Series on Public Policy, 47, pp. 163-90.
18. Hoover, K.D. and Perez, S.J. (1999), Data mining reconsidered: encompassing and the general-to-specific approach to specification search, Econometrics Journal, 2, pp. 167-91.
19. Kapetanios, G. (2007), Variable selection in regression models using non-standard optimisation of information criteria, Computational Statistics and Data Analysis (forthcoming).
20. Kapetanios, G., Labhard, V. and Price, S. (2006), Forecasting using predictive likelihood model averaging, Economics Letters, 91(3), pp. 373-9.
21. Kapetanios, G., Labhard, V. and Price, S. (2007), Forecasting using Bayesian and information theoretic model averaging: an application to UK inflation, Journal of Business and Economic Statistics (forthcoming).
22. Kapetanios, G. and Marcellino, M. (2003), A comparison of estimation methods for dynamic factor models of large dimensions, Queen Mary, University of London Working Paper no. 489.
23. Krolzig, H.M. and Hendry, D.F. (2001), Computer automation of general-to-specific model selection procedures, Journal of Economic Dynamics and Control, 25(6-7), pp. 831-66.
24. Lutz, R.W. and Buhlmann, P. (2006), Boosting for high-multivariate responses in high-dimensional linear regression, Statistica Sinica, 16, pp. 471-94.
25. Marimon, R., McGratten, E. and Sargent, T.J. (1990), Money as a medium of exchange in an economy with artificially intelligent agents, Journal of Economic Dynamics and Control, 14, pp. 329-73.
26. Ostermark, R. (1999), Solving irregular econometric and mathematical optimization problems with a genetic hybrid algorithm, Computational Economics, 13(2), pp. 103-15.
27. Reinsel, G.C. and Velu, R.P. (1998), Multivariate Reduced Rank Regression, Lecture Notes in Statistics No. 136, New York, Springer.
28. Stock, J.H. and Watson, M.W. (1989), New indices of coincident and leading indicators, in Blanchard, O.J. and Fischer, S. (eds), NBER Macroeconomics Annual 1989, Cambridge (Mass.), MIT Press.
29. Stock, J.H. and Watson, M.W. (2002), Macroeconomic forecasting using diffusion indices, Journal of Business and Economic Statistics, 20, pp. 147-62.
30. Svensson, L.E.O. (2005), Monetary policy with judgment: forecast targeting, International Journal of Central Banking, 1(1), pp. 1-54.
31. Velu, R.P., Reinsel, G.C. and Wichern, D.W. (1986), Reduced rank models for multiple time series, Biometrika, 73, pp. 105-18.