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پیش بینی سری زمانی ترکیبی: یک روش فضای حالت
عنوان انگلیسی مقاله:
Forecasting compositional time series: A state space approach
سال انتشار : 2017
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5. Estimation
Looking at the local trend model as the most general case under consideration, the r series could involve as many as rk + p + r(r + 1)/2 parameters, where the three terms correspond to the counts of the starting values, the smoothing parameters and the variance matrix elements respectively. We consider two different maximum likelihood-based estimation procedures in the next two subsections, illustrated in terms of the LTM. 5.1. A vector ARIMA approach The problem in this framework is the estimation of the parameters (α, β) and the r (r + 1) /2 potentially distinct elements of the variance matrix V. One advantage of the ARIMA approach is that the unobservable random state vectors appear to be absent and need not be estimated. However, a direct specification of the unconditional likelihood is not possible because of the existence of the two unit roots (differences). Instead, it is necessary to base the likelihood function on the density p (y3, . . . , yn|y1, y2; α, β, V), which is conditioned on the first two observations. Equivalently, this function may be represented as the density p ∇ 2 y3, . . . , ∇ 2 yn; α, β, V of second differences. Thus, the effective sample size for each transformed series is n − 2.
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کلمات کلیدی:
[PDF]Forecasting Compositional Time Series - Monash Business School https://business.monash.edu/econometrics-and-business-statistics/.../wp11-15.pdf by RD Snyder - 2015 - Related articles Apr 21, 2015 - Forecasting Compositional Time Series: A State Space Approach1 ... data on proportions are known as compositional time series methods and ... Forecasting Compositional Time Series: A State Space Approach https://forecasters.org/wp-content/uploads/gravity_forms/7.../Ord_Keith_ISF2015.pdf by R Snyder - Related articles Forecasting Compositional Time Series: A State Space Approach. Ralph Snyder (Monash University, Australia). Keith Ord (Georgetown University, USA). Forecasting compositional time series - ResearchGate https://www.researchgate.net/.../227090242_Forecasting_compositional_time_series Apr 21, 2017 - Forecasting compositional time series on ResearchGate, the professional network for scientists. Forecasting compositional time series - IDEAS/RePEc https://ideas.repec.org/a/spr/qualqt/v44y2010i4p673-690.html by T Mills - 2010 - Cited by 10 - Related articles Terence Mills. (t.c.mills@lboro.ac.uk). Registered author(s):. Abstract. No abstract is available for this item. Download Info. If you experience problems ... Modeling Compositional Time Series with ... - Wiley Online Library onlinelibrary.wiley.com/doi/10.1002/for.2336/pdf by P Kynčlová - 2015 - Cited by 4 - Related articles Mar 12, 2015 - Journal of Forecasting, J. Forecast. ... Compositional time series (CTS) represent multivariate time series of compositions, often characterized ... [PDF]Compositional Time Series Analysis: A Review - UdG ima.udg.edu/~barcelo/index_archivos/ISI2007_Aguilar.pdf Approaches to the Analysis of Compositional Time Series ... obtain estimates and forecasts for the transformed series, followed by the application of the additive. Compositional Time Series: An Application - Semantic Scholar https://pdfs.semanticscholar.org/b546/9eddfc148e38d1c093108fe39b27b85fb614.pdf by J Bergman - Cited by 5 - Related articles Compositional Time Series: An Application. J. Bergman1. 1Lund University, Lund, Sweden; jakob.bergman@stat.lu.se. Abstract. The composition of the labour ... Forecasting compositional time series - EBSCOhost search.ebscohost.com/login.aspx?direct=true&profile=ehost...site... by TC Mills - 2010 - Cited by 12 - Related articles May 12, 2009 - forecasting compositional time series, it illustrates the techniques using ... to compositional time series, however, notably by Quintana and West ...
