Forecasting with Unobserved Components Time Series Models
AbstractStructural time series models are formulated in terms of components, such as trends, seasonals and cycles, that have a direct interpretation. As well as providing a framework for time series decomposition by signal extraction, they can be used for forecasting and for `nowcasting'. The structural interpretation allows extensions to classes of models that are able to deal with various issues in multivariate series and to cope with non-Gaussian observations and nonlinear models. The statistical treatment is by the state space form and hence data irregularities such as missing observations are easily handled. Continuous time models offer further flexibility in that they can handle irregular spacing. The paper compares the forecasting performance of structural time series models with ARIMA and autoregressive models. Results are presented showing how observations in linear state space models are implicitly weighted in making forecasts and hence how autoregressive and vector error correction representations can be obtained. The use of an auxiliary series in forecasting and nowcasting is discussed. A final section compares stochastic volatility models with GARCH.
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- Steven Clark & T. Coggin, 2009. "Trends, Cycles and Convergence in U.S. Regional House Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 39(3), pages 264-283, October.
- Terasvirta, Timo, 2006.
"Forecasting economic variables with nonlinear models,"
Handbook of Economic Forecasting,
- Teräsvirta, Timo, 2005. "Forecasting economic variables with nonlinear models," Working Paper Series in Economics and Finance 598, Stockholm School of Economics, revised 29 Dec 2005.
- Helmut Lütkepohl, 2010.
"Forecasting Aggregated Time Series Variables: A Survey,"
OECD Journal: Journal of Business Cycle Measurement and Analysis,
OECD Publishing,CIRET, vol. 2010(2), pages 1-26.
- Helmut Luetkepohl, 2009. "Forecasting Aggregated Time Series Variables: A Survey," Economics Working Papers ECO2009/17, European University Institute.
- Lamey, Lien & Deleersnyder, Barbara & Dekimpe, Marnik & Jan-Benedict E.M., Steenkamp, 2008. "How to mitigate private-label succes in recessions? A cross category investigation," Open Access publications from Katholieke Universiteit Leuven urn:hdl:123456789/198269, Katholieke Universiteit Leuven.
- Katharina Hampel & Marcus Kunz & Norbert Schanne & Ruediger Wapler & Antje Weyh, 2006. "Regional Unemployment Forecasting Using Structural Component Models With Spatial Autocorrelation," ERSA conference papers ersa06p196, European Regional Science Association.
- Schanne, N. & Wapler, R. & Weyh, A., 2010.
"Regional unemployment forecasts with spatial interdependencies,"
International Journal of Forecasting,
Elsevier, vol. 26(4), pages 908-926, October.
- Schanne, Norbert & Wapler, Rüdiger & Weyh, Antje, 2008. "Regional unemployment forecasts with spatial interdependencies," IAB Discussion Paper 200828, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
- Hampel, Katharina & Kunz, Marcus & Schanne, Norbert & Wapler, Rüdiger & Weyh, Antje, 2007. "Regional employment forecasts with spatial interdependencies," IAB Discussion Paper 200702, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
- De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
- DeRossi, G. & Harvey, A., 2006. "Time-Varying Quantiles," Cambridge Working Papers in Economics 0649, Faculty of Economics, University of Cambridge.
- Christophe Planas & Alessandro Rossi & Gabriele Fiorentini, 2008. "The marginal likelihood of Structural Time Series Models, with application to the euroareaa nd US NAIRU," Working Paper Series 21-08, The Rimini Centre for Economic Analysis, revised Jan 2008.
- Vipin Arora, 2013.
"Comparisons of Chinese and Indian Energy Consumption Forecasting Models,"
AccessEcon, vol. 33(3), pages 2110-2121.
- Arora, Vipin, 2013. "Comparisons of Chinese and Indian Energy Consumption Forecasting Models," MPRA Paper 48621, University Library of Munich, Germany.
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