This paper adapts the non-parametric dynamic time warping (DTW) technique in an application to examine the temporal alignment and similarity across economic time series. DTW has important advantages over existing measures in economics as it alleviates concerns regarding a pre-defned fxed temporal alignment of series. For example, in contrast to current methods, DTW can capture alternations between leading and lagging relationships of series. We illustrate DTW in a study of US states’ business cycles around the Great Recession, and fnd considerable evidence that temporal alignments across states dynamic. Trough cluster analysis, we further document state-varying recoveries from the recession.

Additional Metadata
Keywords Business cycles · Non-parametric method · Dynamic time warping
JEL Semiparametric and Nonparametric Methods (jel C14), Econometric Modeling: General (jel C50), Large datasets: Modelling and Analysis (jel C55), Econometric Software (jel C87), Business Fluctuations; Cycles (jel E32)
Persistent URL hdl.handle.net/1765/129420
Journal Computational Economics
Citation
Franses, Ph.H.B.F, & Wiemann, T. (2020). Intertemporal Similarity of Economic Time Series: An Application of Dynamic Time Wrapping. Computational Economics, 56, 59–75. Retrieved from http://hdl.handle.net/1765/129420