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Forecasting linear dynamical systems using subspace methods

García Hiernaux, Alfredo (2009) Forecasting linear dynamical systems using subspace methods. [Working Paper or Technical Report] (Unpublished)

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Abstract

A new procedure to predict with subspace methods is presented in this paper. It is based on combining multiple forecasts obtained from setting a range of values for a specic parameter that is typically xed by the user in the subspace methods literature. An algorithm to compute these predictions and to obtain a suitable number of combinations is provided. The procedure is illustrated by forecasting the German gross domestic product.

Item Type:Working Paper or Technical Report
Additional Information:JEL Classification: C53,C22,E27
Uncontrolled Keywords:Forecasting, Subspace methods, Combining forecasts.
Subjects:Social sciences > Economics > Finance
Series Name:Documentos de trabajo del Instituto Complutense de Análisis Económico (ICAE)
Volume:2009
Number:0902
ID Code:8588
References:

Akaike, H. (1976). Canonical Correlation Analysis of Time Series and the Use of an Information Criterion. Academic Press.

Bauer, D. (2005a). Comparing the CCA subspace method to pseudo maximum likelihood methods in the case of no exogenous inputs. Journal of Time Series Analysis, 6(5):631-668.

Bauer, D. (2005b). Estimating linear dynamical systems using subspace methods. Econometric Theory, 21:181-211.

Bauer, D. and Wagner, M. (2002). Estimating cointegrated systems using subspace algorithms. Journal of Econometrics, 111:47-84.

Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. Holden-Day, San Francisco, 2nd ed. edition.

Deistler, M., Peternell, K., and Scherrer, W. (1995). Consistency and relative ecency of subspace methods. Automatica, 31(12):1865-1875.

Diebold, F. and Mariano, R. (1995). Comparing predictive accuracy. Journal of Business and Economic Statistics, 13(3):253-263.

García-Hiernaux, A., Jerez, M., and Casals, J. (2007). Estimating the system order by subspace methods. Statistics and Econometrics Working Papers, 070301.

García-Hiernaux, A., Jerez, M., and Casals, J. (2009). Fast estimation methods for time series models in state-space form. Journal of Statistical Computation and Simulation, 79(2):121-134.

García-Hiernaux, A., Jerez, M., and Casals, J. (2010). Unit roots and cointegration modeling through a family of exible information criteria. Journal of Statistical Computation and Simulation, 80(2):173-189.

Granger, C. W. J. and Ramanathan, R. (1984). Improved methods of combining forecasts. Journal of Forecasting, 3:197-204.

Hannan, E. J. and Deistler, M. (1988). The Statistical Theory of Linear Systems. John Wiley, New York.

Kapetanios, G. (2004). A note on modelling core in ation for the uk using a new dynamic factor estimation method and a large disaggregated price index dataset. Economic Letters, 85:63-69.

Kascha, C. and Mertens, K. (2009). Business cycle analysis and VARMA models. Journal of Economics Dynamics and Control, 33(2):267-282.

Katayama, T. (2005). Subspace Methods for System Identication. Springer Verlag, London.

Ljung, L. (1999). System Identication, Theory for the User. PTR Prentice Hall, New Jersey, second edition.

Mossberg, M. (2007). Forecasting electric power consumption using subspace al-gorithms. International Journal of Power and Energy Systems, 27:369-386.

Schneider, T. and Neumaier, A. (2001). Algorithm 808: Art - a matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive models. ACM Transactions on Mathematical Software, 27:58-65.

Schumacher, C. (2007). Forecasting german gdp using alternative factor models based on large datasets. Journal of Forecasting, 26:271-302.

Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6:461-464.

Tiao, G. C. and Tsay, R. S. (1989). Model specication in multivariate time series. Journal of the Royal Statistical Society, B Series, 51(2):157-213.

Deposited On:03 Mar 2009 09:40
Last Modified:06 Feb 2014 08:10

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