Intertemporal Similarity of Economic Time Series: An Application of Dynamic Time Warping
Philip Hans Franses and
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Thomas Wiemann: Erasmus School of Economics
Computational Economics, 2020, vol. 56, issue 1, No 4, 59-75
Abstract 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-defined fixed 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 find considerable evidence that temporal alignments across states dynamic. Trough cluster analysis, we further document state-varying recoveries from the recession.
Keywords: Business cycles; Non-parametric method; Dynamic time warping (search for similar items in EconPapers)
JEL-codes: C14 C50 C55 C87 E32 (search for similar items in EconPapers)
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