Abstract
In this paper, we introduce an effective machine learning method which can capture the temporal causal structures between irregular time series to forecast China GDP growth rate with Mixed Frequency data set. The introduced method first generalized the inner product operator via kernels so that regression-based temporal casual models can be applicable to irregular time series, then the temporal casual relationships among the irregular time series are studied by Generalized Lasso Granger (GLG) graphical models. The main advantage of this approach is that it does not directly estimate the values of missing data of low frequency time series or has restricted assumptions about the generation process of the time series. By applying this method to a 17 macroeconomic indicators GLG model, the forecasting accuracy is better than the autoregressive (AR) benchmark model and a widely used mixed-data sampling (MIDAS) model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Silvestrini, A., Veredas, D.: Temporal aggregation of univariate and multivariate time series models: A survey. Journal of Economic Surveys 22(3), 458–497 (2008)
Silvestrini, A., Moulin, L., Salto, M., Veredas, D.: Monitoring and forecasting annual public deficit every month: The case of France. Empirical Economics 34(3), 493–524 (2008)
Chow, G., Lin, A.: Best linear unbiased interpolation, distribution, and extrapolation of time series by related series. The Review of Economics and Statistics 53(4), 372–375 (1971)
Mittnik, S., Zadrozny, P.A.: Forecasting German GDP at monthly frequency using monthly IFO business conditions data. In: Sturm, J.-E., Wollmershauser, T. (eds.) Ifo Survey Data in Business Cycle and Monetary Policy Analysis, pp. 19–48 (2005)
Ghysels, E., Santa-Clara, P., Valkanov, R.: The MIDAS touch: Mixed data sampling regressions. In: Mimeo, vol. 131. Chapel Hill N.C (2004)
Ghysels, E., Santa-Clara, P., Valkanov, R.: Predicting volatility: Getting the most out of return data sampled at different frequencies. Journal of Econometrics 131, 59–95 (2006)
Marcellino, M., Schumacher, C.: Factor nowcasting of German GDP with ragged-edge data. A model comparison using MIDAS projections. In: Bundes bank Discussion Paper, Series 1 No. 34 (2007)
Kuzin, J., Marcellino, M., Schumacher, C.: MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area. International Journal of Forecasting 27(2), 529–542 (2011)
Bahadori, Taha, M., Yan, L.: Granger Causality Analysis in Irregular Time Series. In: 9th VLDB Workshop on Secure Data Management, SDM 2012 (2012)
Zhe, G., Wang, Z., Wang, L., Tan, S.: Linear non-Gaussian causal discovery from a composite set of major US macroeconomic factors. Expert Systems with Applications 39(12), 10867–10872 (2012)
Pesaran, M.H., Shin, Y.: An autoregressive distributed-lag modeling approach to cointegration analysis. Econometric Society Monographs 31, 371–413 (1998)
Ghysels, E., Sinko, A., Valkanov, R.: MIDAS regressions: Further results and new directions. Econometric Reviews 26, 53–90 (2006)
Granger, C.W.: Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37(3), 424–438 (1969)
Arnold, A., Liu, Y., Abe, N.: Temporal causal modeling with graphical granger methods. In: KDD 2007, p. 66. ACM Press, New York (2007)
Zou, H.: The adaptive lasso and its oracle properties. Journal of the American Statistical Association 101, 1418–1429 (2006)
Liu, H., Liu, J.: Nowcasting and Short-term Forecasting of Chinese Macroeconomic Aggregates: Based on the Empirical Study of MIDAS Model. Economic Research Journal (3), 4–17 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gao, Z., Yang, J., Tan, S. (2013). Forecasting Chinese GDP with Mixed Frequency Data Set: A Generalized Lasso Granger Method. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38715-9_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-38715-9_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38714-2
Online ISBN: 978-3-642-38715-9
eBook Packages: Computer ScienceComputer Science (R0)