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PosterEEP_Shchekinova.pdf

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/77866 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/77866 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/77866 archive 353 disk0/00/01/44/25 2017-03-06 09:18:45 2021-08-27 17:28:38 2017-03-06 09:18:45 conference_item show 1 Shchekinova E. 2127 Puchkova A. 8625 Rovenskaya E. 8206 0000-0002-2761-3443 Dieckmann U. 1668 0000-0001-7089-0393 pub prog_asa prog_eep Recessions are economic downturns that can be recognized from macro-indicators such as the Dow Jones Industrial Average (DJIA) and the Federal Reserve Interest Rate (FRIR). To provide early-warning signals of recessions and similar systemic transitions, here we propose a new approach based on pattern recognition, called inclination analysis [1, 2]. For this purpose, we develop a stochastic model based on time-series analysis to assess the probability of a recession to occur at a given moment in the past, present, or future. Calibrating our model to data proceeds in three steps, involving the coarse-graining of the available input time series, the identification of short series motifs that foreshadow recessions, and the optimization of key model parameters according to the model’s desired forecasting horizon. 2017-02-27 published 2830 1522 260 66 public poster IIASA Institutional Evaluation 2017 IIASA, Laxenburg, Austria 27 February-1 March 2017 other FALSE FALSE no info:eu-repo/semantics/conferenceObject