Please use this identifier to cite or link to this item:
|Publication type:||Article in scientific journal|
|Type of review:||Peer review (publication)|
|Title:||Optimal real-time filters for linear prediction problems|
|Published in:||Journal of Time Series Econometrics|
|Publisher / Ed. Institution:||De Gruyter|
|Subject (DDC):||003: Systems|
|Abstract:||The classic model-based paradigm in time series analysis is rooted in the Wold decomposition of the data-generating process into an uncorrelated white noise process. By design, this universal decomposition is indifferent to particular features of a specific prediction problem (e. g., forecasting or signal extraction) – or features driven by the priorities of the data-users. A single optimization principle (one-step ahead forecast error minimization) is proposed by this classical paradigm to address a plethora of prediction problems. In contrast, this paper proposes to reconcile prediction problem structures, user priorities, and optimization principles into a general framework whose scope encompasses the classic approach. We introduce the linear prediction problem (LPP), which in turn yields an LPP objective function. Then one can fit models via LPP minimization, or one can directly optimize the linear filter corresponding to the LPP, yielding the Direct Filter Approach. We provide theoretical results and practical algorithms for both applications of the LPP, and discuss the merits and limitations of each. Our empirical illustrations focus on trend estimation (low-pass filtering) and seasonal adjustment in real-time, i. e., constructing filters that depend only on present and past data.|
|Further description:||Erworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch)|
|Fulltext version:||Published version|
|License (according to publishing contract):||Licence according to publishing contract|
|Departement:||School of Engineering|
|Organisational Unit:||Institute of Data Analysis and Process Design (IDP)|
|Appears in collections:||Publikationen School of Engineering|
Files in This Item:
|2016_Wildi-McElroy_Real-time-filters-linear-prediction-problems.pdf||Version of Record||2.61 MB||Adobe PDF|
|2016_Wildi_Optimal_real_time_filters_for_linear_prediction_problems.pdf||Accepted Version||545.29 kB||Adobe PDF|
Show full item record
Wildi, M., & McElroy, T. (2016). Optimal real-time filters for linear prediction problems. Journal of Time Series Econometrics, 8(2), 155–192. https://doi.org/10.21256/zhaw-4906
Wildi, M. and McElroy, T. (2016) ‘Optimal real-time filters for linear prediction problems’, Journal of Time Series Econometrics, 8(2), pp. 155–192. Available at: https://doi.org/10.21256/zhaw-4906.
M. Wildi and T. McElroy, “Optimal real-time filters for linear prediction problems,” Journal of Time Series Econometrics, vol. 8, no. 2, pp. 155–192, 2016, doi: 10.21256/zhaw-4906.
Wildi, Marc, and Tucker McElroy. “Optimal Real-Time Filters for Linear Prediction Problems.” Journal of Time Series Econometrics, vol. 8, no. 2, 2016, pp. 155–92, https://doi.org/10.21256/zhaw-4906.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.