This paper aims to provide a brief and relatively non-technical overview of state-of-the-art forecasting with large data sets. We classify existing methods into four groups depending on whether data sets are used wholly or partly, whether a single model or multiple models are used and whether a small subset or the whole data set is being forecast. In particular, we provide brief descriptions of the methods and short recommendations where appropriate, without going into detailed discussions of their merits or demerits.
Akaike, H. (1978), `A Bayesian analysis of the minimum AIC procedure', Annals ofthe Institute of Statistical Mathematics, 30.
2.
—( 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 predictors - is 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.
—( 2004), `The generalised factor model: consistency and rates' , Journal of Econometrics, 119, pp. 231-55.
15.
—( 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.
—( 1997), `On congruent econometric relations: a comment', Carnegie-Rochester Conference Series on Public Policy, 47, pp. 16390.
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.
—( 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.
—( 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. 154.
31.
Velu, R.P., Reinsel, G.C. and Wichern, D.W. (1986), `Reduced rank models for multiple time series ', Biometrika, 73, pp. 105-18.
