Anticipating Recessions using Inclination Analysis

Shchekinova, E., Puchkova, A., Rovenskaya, E. ORCID: https://orcid.org/0000-0002-2761-3443, & Dieckmann, U. ORCID: https://orcid.org/0000-0001-7089-0393 (2017). Anticipating Recessions using Inclination Analysis. In: IIASA Institutional Evaluation 2017, 27 February-1 March 2017, IIASA, Laxenburg, Austria.

Preview

Text (Poster) PosterEEP_Shchekinova.pdf - Published Version Available under License Creative Commons Attribution. Download (322kB) | Preview

Abstract

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.

Actions (login required)

View Item