Forecasting, Structural Time Series and the Kalman Filter

doi:10.1080/00401706.1992.10484972

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Forecasting, Structural Time Series and the Kalman Filter

Journal Article•DOI•

Forecasting, Structural Time Series and the Kalman Filter

01 Nov 1992-Technometrics (Taylor & Francis Group)-Vol. 34, Iss: 4, pp 496-497

TL;DR: In this article, the Kalman Filter was used for forecasting, structural time series and Kalman filter was applied to the Structural Time Series (STS) in the context of time series forecasting.

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Abstract: (1992). Forecasting, Structural Time Series and the Kalman Filter. Technometrics: Vol. 34, No. 4, pp. 496-497.

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Journal Article•DOI•

Regime jumps in electricity prices

[...]

Ronald Huisman¹, Ronald Mahieu¹•Institutions (1)

Erasmus University Rotterdam¹

01 Sep 2003-Energy Economics

TL;DR: In this article, a regime-switching model is proposed to model price spikes separated from normal mean-reverting prices, which can capture the volatility of electricity prices in the regime switching process.

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303 citations

Posted Content•

Symptoms of complexity in a tourism system

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Rodolfo Baggio¹•Institutions (1)

Bocconi University¹

05 Jan 2007-arXiv: Physics and Society

TL;DR: In this paper, the authors present a brief overview of the complexity framework as a means to understand structures, characteristics, relationships, and explore the implications and contributions of complexity literature on tourism systems.

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Abstract: Tourism destinations behave as dynamic evolving complex systems, encompassing numerous factors and activities which are interdependent and whose relationships might be highly nonlinear. Traditional research in this field has looked after a linear approach: variables and relationships are monitored in order to forecast future outcomes with simplified models and to derive implications for management organisations. The limitations of this approach have become apparent in many cases, and several authors claim for a new and different attitude. While complex systems ideas are amongst the most promising interdisciplinary research themes emerged in the last few decades, very little has been done so far in the field of tourism. This paper presents a brief overview of the complexity framework as a means to understand structures, characteristics, relationships, and explores the implications and contributions of the complexity literature on tourism systems. The objective is to allow the reader to gain a deeper appreciation of this point of view.

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278 citations

Cites background from "Forecasting, Structural Time Series..."

...A tem’s elements, combined with the influence of a large set of external factors. The value of the tourism destination comprises a number of elements: the tourism operators, the support struc- chaos and complexity framework in understanding the development of a destination and the role of tures, public and private organizations and associations. All of these elements have some kind of small tourism business networks has also been discussed by Tinsley and Lynch (2001). relationship among themselves and the possible nonlinearities in these relationships are well known The main objective of this article is to give a and have been described several times (Farrell & brief overview of the complexity framework and Twining-Ward, 2004; Faulkner & Russell, 1997). to explore the implications and contributions that Moreover, we can include in the system also ele- the study of complex systems can give to the unments not traditionally thought as belonging derstanding of the tourism destination model. Constrictly to the tourism sector, but whose impor- tinuing the line of research presented above, this tance and role in this framework is undoubtedly work aims at complementing and reinforcing it by very high. providing some quantitative evidence in support An important, although rather scarce, strand of of this approach. This, it is hoped, will allow the literature has pointed out the necessity to change reader to gain a deeper appreciation of this point attitude when studying tourism and tourism sys- of view. tems. In a pioneering work, Faulkner and Valerio The remainder of the article is organized as fol(1995) start from the realization of the deficiencies lows....
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...For ships is considered to be detrimental for the devel- example, Agostinho and Teixeira de Castro (2003) opment of the system, because evolution and analyze a Brazilian experience and provide tangigrowth can only be possible in regions of the ble data showing that an adaptive, self-organizing, phase space at the boundary between order and management system produce better performance...
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...A time se- tions of the initial separation vector. Thus, there exists a whole spectrum of Lyapunov exponents. ries can be used to derive such a plot. Before doing that, we must recreate the phase space by using Their number is equal to the number of dimensions of the phase space. The largest LCE deterone of the techniques devised for this purpose. The most commonly used is the time-lagged (de- mines the general behavior of the system. If it is negative, the system follows a stable trajectory; if lay-coordinate) technique (Kantz & Schreiber, 1997; Schreiber, 1999). A delay coordinate recon- it is null, the system is in a steady state; if it is positive the system exhibits unstable and chaotic struction can be obtained by plotting the time series versus a time-delayed version of it. For a two- behavior (Sprott, 2003). In most cases, the calculation of Lyapunov exponents cannot be carried out dimensional reconstruction, it is possible to plot the delay vector yn = (tn, tn−V), where V is the lag or analytically and numerical techniques must be used. In cases like ours, when only a one-dimensampling delay: the difference between the adjacent components of the delay vector measured in sional time series is given, the highest LCE can be number of samples. The theoretical basis for this estimated with the method proposed by Wolf, procedure is due to Takens (1980). His fundamen- Swift, Swinney, and Vastano (1985) and Rotal theorem states that a dynamical system can be senstein, Collins, and De Luca (1993)....
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...Harvey, A. C. (1989)....
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...) transmission are greatly improved with re(1998) and Barabási and Albert (1999) have prospect to a random ER network, in some cases it vided evidence that, in many cases, real-world netis shown that there are no critical thresholds at works are quite different from ER graphs....
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Journal Article•DOI•

Small Area Estimation‐New Developments and Directions

[...]

Danny Pfeffermann¹•Institutions (1)

Hebrew University of Jerusalem¹

01 Apr 2002-International Statistical Review

TL;DR: In this paper, the authors provide a critical review of the main advances in small area estimation (SAE) methods in recent years and discuss some of the earlier developments, which serve as a necessary background for the new studies.

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Abstract: Summary The purpose of this paper is to provide a critical review of the main advances in small area estimation (SAE) methods in recent years. We also discuss some of the earlier developments, which serve as a necessary background for the new studies. The review focuses on model dependent methods with special emphasis on point prediction of the target area quantities, and mean square error assessments. The new models considered are models used for discrete measurements, time series models and models that arise under informative sampling. The possible gains from modeling the correlations among small area random effects used to represent the unexplained variation of the small area target quantities are examined. For review and appraisal of the earlier methods used for SAE, see Ghosh and Rao (1994).

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256 citations

Cites methods from "Forecasting, Structural Time Series..."

...(7.5) If mi signifies the last time point with observations, the optimal predictor of mT under this model is easily obtained by application of the recursive Kalman filter equations (Harvey 1989)....
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...See Harvey (1989) for details....
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...If m i signifies the last time point with observations, the optimal predictor of m T under this model is easily obtained by application of the recursive Kalman filter equations (Harvey 1989)....
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Posted Content•

Cyclically Adjusted Budget Balances: An Alternative Approach

[...]

Carine Bouthevillain¹, Philippine Cour-Thimann², Gerrit van den Dool³, Pablo Hernández de Cos⁴, Geert Langenus⁵, Matthias Mohr², Sandro Momigliano, Mika Tujula² - Show less +4 more•Institutions (5)

Banque de France¹, European Central Bank², Organisation for Economic Co-operation and Development³, Bank of Spain⁴, National Bank of Belgium⁵

01 Sep 2001-Social Science Research Network

TL;DR: In this article, the effects of changes in the structure of demand and national income on government revenue and expenditure are captured by a disaggregated method for the calculation of the cyclical component of the budget balance.

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Abstract: Estimates of cyclically-adjusted budget balances, correcting actual government budget balances for business cycle fluctuations, are produced by many institutions. This paper presents an alternative approach for the cyclical adjustment of budget balances. The approach is based on a disaggregated method for the calculation of the cyclical component of the budget balance. In this approach, the effects of changes in theA structure of demand and national income on government revenue and expenditure are captured. Cases where the various macroeconomic bases are in different phases of the cycle or exhibit fluctuations of different magnitude are taken into account in this way. The computation of the cyclical components of these macroeconomic bases is based on the Hodrick-Prescott filter and takes into account the latest evidence presented in the literature about the properties of this filter. The paper also presents new estimates of the elasticities of individual budget items with respect to the relevant macroeconomic variables.

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254 citations

Journal Article•DOI•

Structural time series models and the Kalman filter: A concise review

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João Tovar Jalles¹, João Tovar Jalles²•Institutions (2)

International Monetary Fund¹, Technical University of Lisbon²

19 Jun 2009-Social Science Research Network

TL;DR: In this paper, structural time series models by which a time series can be decomposed as the sum of a trend, seasonal and irregular components are presented, and the recursive estimation and smoothing by means of the Kalman lter algorithm is described taking into account its different stages, from initialisation to parameter's estimation.

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Abstract: The continued increase in availability of economic data in recent years and, more importantly, the possibility to construct larger frequency time series, have fostered the use (and development) of statistical and econometric techniques to treat them more accurately. This paper presents an exposition of structural time series models by which a time series can be decomposed as the sum of a trend, seasonal and irregular components. In addition to a detailled analysis of univariate speci cations we also address the SUTSE multivariate case and the issue of cointegration. Finally, the recursive estimation and smoothing by means of the Kalman lter algorithm is described taking into account its different stages, from initialisation to parameter's estimation.

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199 citations

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References

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Book•

Time Series: Theory and Methods

[...]

Peter J. Brockwell, Richard A. Davis

19 Aug 2009

TL;DR: In this article, the mean and autocovariance functions of ARIMA models are estimated for multivariate time series and state-space models, and the spectral representation of the spectrum of a Stationary Process is inferred.

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Abstract: 1 Stationary Time Series.- 2 Hilbert Spaces.- 3 Stationary ARMA Processes.- 4 The Spectral Representation of a Stationary Process.- 5 Prediction of Stationary Processes.- 6* Asymptotic Theory.- 7 Estimation of the Mean and the Autocovariance Function.- 8 Estimation for ARMA Models.- 9 Model Building and Forecasting with ARIMA Processes.- 10 Inference for the Spectrum of a Stationary Process.- 11 Multivariate Time Series.- 12 State-Space Models and the Kalman Recursions.- 13 Further Topics.- Appendix: Data Sets.

...read moreread less

5,260 citations

Book•

Introduction to the Theory of Statistics

[...]

Alexander M. Mood, Franklin A. Graybill

01 Jan 1963

TL;DR: In this article, a tabular summary of parametric families of distributions is presented, along with a parametric point estimation method and a nonparametric interval estimation method for point estimation.

...read moreread less

Abstract: 1 probability 2 Random variables, distribution functions, and expectation 3 Special parametric families of univariate distributions 4 Joint and conditional distributions, stochastic independence, more expectation 5 Distributions of functions of random variables 6 Sampling and sampling distributions 7 Parametric point estimation 8 Parametric interval estimation 9 Tests of hypotheses 10 Linear models 11 Nonparametric method Appendix A Mathematical Addendum Appendix B tabular summary of parametric families of distributions Appendix C References and related reading Appendix D Tables

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4,571 citations

Book•

Time Series Analysis : Univariate and Multivariate Methods

[...]

William W. S. Wei¹•Institutions (1)

Bowling Green State University¹

01 Jan 1990

TL;DR: This work presents a meta-modelling framework for estimating the modeled properties of the Shannon filter, which automates the very labor-intensive and therefore time-heavy process of Fourier analysis.

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Abstract: 1. Overview. 2. Fundamental Concepts. 3. Stationary Time Series Models. 4. Non-Stationary Time Series Models. 5. Forecasting. 6. Model Identification. 7. Parameter Estimation, Diagnostic Checking, and Model Selection. 8. Seasonal Time Series Models. 9. Intervention Analysis and Outlier Detection. 10. Fourier Analysis. 11. Spectral Theory of Stationary Processes. 12. Estimation of the Spectrum. 13. Transfer Function Models. 14. Vector Time Series Models. 15. State Space Models and the Kalman Filter. 16. Aggregation and Systematic Sampling in Time Series. 17. References. 18. Appendix.

...read moreread less

1,497 citations

Book•

ITSM: An Interactive Time Series Modelling Package for the PC

[...]

Peter J. Brockwell, Richard A. Davis

01 Jan 1991

TL;DR: This package provides the reader with a practical understanding of the six programs contained in the ITSM software (PEST, SPEC, SMOOTH, TRANS, ARVEC, and ARAR).

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Abstract: Designed for the analysis of linear time series and the practical modelling and prediction of data collected sequentially in time. It provides the reader with a practical understanding of the six programs contained in the ITSM software (PEST, SPEC, SMOOTH, TRANS, ARVEC, and ARAR). This IBM compatible software is included in the back of the book on two 5 1/4'' diskettes and on one 3 1/2 '' diskette. - Easy to use menu system - Accessible to those with little or no previous compu- tational experience - Valuable to students in statistics, mathematics, busi- ness, engineering, and the natural and social sciences. This package is intended as a supplement to the text by the same authors, "Time Series: Theory and Methods." It can also be used in conjunction with most undergraduate and graduate texts on time series analysis.

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26 citations