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A review of forecasting techniques for large datasets

Published online by Cambridge University Press:  26 March 2020

Jana Eklund  and
George Kapetanios
Jana Eklund*
Affiliation:
Bank of England
George Kapetanios*
Affiliation:
Queen Mary, University of London
*
e-mail: [email protected]
e-mail: [email protected]
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Abstract

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.

Keywords

Large datasetsfactorsforecast combinationsC01C53
Type
Articles
Information
National Institute Economic Review , Volume 203 , January 2008 , pp. 109 - 115
Copyright
Copyright © 2008 National Institute of Economic and Social Research

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Footnotes

The views expressed in this paper are those of the authors, and not necessarily those of the Bank of England.

References

Akaike, H. (1978), ‘A Bayesian analysis of the minimum AIC procedure’, Annals of the Institute of Statistical Mathematics, 30.CrossRefGoogle Scholar
Akaike, H. (1979), ‘A Bayesian extension of the minimum AIC procedure of autoregressive model fitting’, Biometrika, 66.CrossRefGoogle Scholar
Banburra, M., Giannone, D. and Reichlin, L. (2007), ‘Bayesian VARS with large panels’, CEPR Discussion Paper no. 6326.Google Scholar
Boivin, J. and Ng, S. (2006), ‘Are more data always better for factor analysis?’, Journal of Econometrics, 312, pp. 169–94.Google Scholar
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.Google Scholar
Buhlmann, P. (2006), ‘Boosting for high-dimensional linear models’, Annals of Statistics, 34, pp. 559–83.CrossRefGoogle Scholar
Burnham, K.P. and Anderson, D.R. (1998), Model Selection and Inference, Berlin, Springer Verlag.CrossRefGoogle Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
Eklund, J. and Karlsson, S. (2007), ‘Forecast combination and model averaging using predictive measures’, Econometric Reviews, 26(2-4), pp. 329–63.CrossRefGoogle Scholar
Fernandez, C., Ley, E. and Steel, M.F.J. (2001), ‘Benchmark priors for Bayesian model averaging’, Journal of Econometrics, 100, pp. 381–427.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Forni, M., Hallin, M., Lippi, M. and Reichlin, L. (2004), ‘The generalised factor model: consistency and rates’, Journal of Econometrics, 119, pp. 231–55.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Hendry, D.F. (1995), Dynamic Econometrics, Oxford, Oxford University Press.CrossRefGoogle Scholar
Hendry, D.F. (1997), ‘On congruent econometric relations: a comment’, Carnegie-Rochester Conference Series on Public Policy, 47, pp. 163–90.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Kapetanios, G. (2007), ‘Variable selection in regression models using non-standard optimisation of information criteria’, Computational Statistics and Data Analysis (forthcoming).CrossRefGoogle Scholar
Kapetanios, G., Labhard, V. and Price, S. (2006), ‘Forecasting using predictive likelihood model averaging’, Economics Letters, 91(3), pp. 373–9.CrossRefGoogle Scholar
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).CrossRefGoogle Scholar
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.Google Scholar
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.CrossRefGoogle Scholar
Lutz, R.W. and Buhlmann, P. (2006), ‘Boosting for high-multivariate responses in high-dimensional linear regression’, Statistica Sinica, 16, pp. 471–94.Google Scholar
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.CrossRefGoogle Scholar
Ostermark, R. (1999), ‘Solving irregular econometric and mathematical optimization problems with a genetic hybrid algorithm’, Computational Economics, 13(2), pp. 103–15.CrossRefGoogle Scholar
Reinsel, G.C. and Velu, R.P. (1998), Multivariate Reduced Rank Regression, Lecture Notes in Statistics No. 136, New York, Springer.CrossRefGoogle Scholar
Stock, J.H. and Watson, M.W. (1989), ‘New indices of coincident and leadingindicators’, in Blanchard, O.J. and Fischer, S. (eds), NBER Macroeconomics Annual 1989, Cambridge (Mass.), MIT Press.Google Scholar
Stock, J.H. and Watson, M.W. (2002), ‘Macroeconomic forecasting using diffusion indices’, Journal of Business and Economic Statistics, 20, pp. 147–62.CrossRefGoogle Scholar
Svensson, L.E.O. (2005), ‘Monetary policy with judgment: forecast targeting’, International Journal of Central Banking, 1(1), pp. 1–54.Google Scholar
Velu, R.P., Reinsel, G.C. and Wichern, D.W. (1986), ‘Reduced rank models for multiple time series’, Biometrika, 73, pp. 105–18.CrossRefGoogle Scholar