A model state space for the monthly Spanish energy production
DOI:
https://doi.org/10.54571/ajee.507Keywords:
State space models, Kalman Filter, filtered state, smoothed state, ARIMA, tslm, ets, accuracy of forecasts , KFAS statistical package, forecast statistical packageAbstract
In this paper a model state space is estimated for the Monthly Spanish total production and distribution of energy, for the period January 2013 to January 2021. After a brief presentation of these models state space, the estimated model is used for prediction. To enhance the utility of these models, its predictions are compared with those generated with other models used in Time Series Analysis.
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References
Alonso-Rodriguez, A., La prediccion de series temporales mediante el modelo de regresión, Anuario Jurídico y Económico Escurialense, LIV(2021)501-520. DOI: https://doi.org/10.54571/ajee.480
Anderson B. D. O. y Moore, J., Optimal Filtering, Prentice-Hall, 1979.
Durbin, J. y Koopman, Time Series Analysis by State Space Methods, segunda edición, Oxford University Press, 2012. DOI: https://doi.org/10.1093/acprof:oso/9780199641178.001.0001
Commandeur, J. J. F.,Koopman, S. J. y Ooms, M., Statistical Software for State Space Methods, Journal of Statistical Software, 41(2011)1-18. DOI: https://doi.org/10.18637/jss.v041.i01
Commandeur, J. J. F. y Koopman, S. J., An introduction to State Space Time Series Analysis, Oxford University Press, 2007.
Cowpertwait, P. S. P., y Metcalfe, A. V., Introductory Time series with R, Springer, 2009. DOI: https://doi.org/10.1007/978-0-387-88698-5_1
Cryer, J. D. y Chan, K-S., Time Series Analysis with Applications in R, segunda edición, Springer, 2008. DOI: https://doi.org/10.1007/978-0-387-75959-3
FRED, Federal Reserve Economic Data, https://fred.stlouisfed.org
Kitagawa, G., Introduction to Time Series Modeling, Chapman & Hall/CRC, 2010
Hamilton, J. D., Time Series Analysis, Princeton University Press, 1994. DOI: https://doi.org/10.1515/9780691218632
Helske, J., KFAS: Exponential Family State Space Models in R, Journal of Statistical Software, 78(2017)1-39. DOI: https://doi.org/10.18637/jss.v078.i10
Hyndman, R. J., Koehler, A. B., Ord, J. K. y Snyder, R. D., Forecasting with Exponential Smoothing, The State Space Approach, Springer-Verlag Berlin Heidelberg, 2008. DOI: https://doi.org/10.1007/978-3-540-71918-2
Hyndman, R.J. y Athanasopoulos, G., Forecasting, Principles and Practice, segunda edición, OTexts, 2018.
Hyndman, R. J. y Athanasopoulos, G., Forecasting, Principles and Practice, tercera edción, OTexts, 2021.
Kitagawa, G., Introduction to Time Series Modeling, CRC Press, 2010. DOI: https://doi.org/10.1201/9781584889229
Kleiber, Ch., y Zeileis, A., Applied Econometrics with R, Springer, 2008. DOI: https://doi.org/10.1007/978-0-387-77318-6
OCDE, https://www.oecd.org
Martin, V., Hurn, S. y Harris, D., Econometric Modelling with Time Series, Specification, Estimation and Testing, Cambridge University Press, 2013.
Petris, G., Petrone, S. y Campagnoli, P., Dynamic Linear Models with R, Springer, 2009. DOI: https://doi.org/10.1007/b135794_2
Pelagatti, M. M., Time Series Modelling with Unobserved Components, CRC Press, 2016. DOI: https://doi.org/10.1201/b18766
R. R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
Shumway, R. H. y Stoffer, D. S., Time Series Analysis and Its Applications, with R Examples, cuarta edición, Springer, 2017 DOI: https://doi.org/10.1007/978-3-319-52452-8
Tusell, F., Kalman Filtering in R, Journal of Statistical Software, 39(2011)1-27 DOI: https://doi.org/10.18637/jss.v039.i02
Wei, W. S., Time Series Analysis, Univariate and Multivariate Methods, Addison-Wesley Publishing Company, Inc., 1990.
Woodward, W. A., Gray, H.L. y Elliott, A- C., Applied Time Series Analysis with R, segunda edición, CRC Press, 2017. DOI: https://doi.org/10.1201/9781315161143
Zivot, E. y Wang, J., Modeling Financial Time Series with S-PLUS, segunda edición, Springer, 2006.
