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Identifying the Presence and Cause of Fashion Cycles in Data

56 Pages Posted: 20 Nov 2012 Last revised: 22 Jan 2016

Hema Yoganarasimhan

Foster School of Business, University of Washington

Date Written: January 21, 2016


Fashions and conspicuous consumption play an important role in marketing. In this paper, we present a three-pronged framework to analyze fashion cycles in data -- a) algorithmic methods for identifying cycles, b) statistical framework for identifying cycles, and c) methods for examining the drivers of such cycles. In the first module, we identify cycles based on pattern-matching the amplitude and length of cycles observed to a user-specified definition. In the second module, we define the Conditional Monotonicity Property, derive conditions under which a data generating process satisfies it, and demonstrate its role in generating cycles. A key challenge that we face in estimating this model is the presence of endogenous lagged dependent variables, which we address using system GMM estimators. Third, we present methods that exploit the longitudinal and geographic variations in agents' economic and cultural capital to examine the different theories of fashion. We apply our framework to data on given names for infants, show the presence of large amplitude cycles both algorithmically and statistically, and confirm that the adoption patterns are consistent with Bourdieu's theory of fashion as a signal of cultural capital.

Keywords: Fashion, Social Influence, Panel Data, Marketing, GMM Estimators

JEL Classification: C33, C52, C32, M31, D71

Suggested Citation

Yoganarasimhan, Hema, Identifying the Presence and Cause of Fashion Cycles in Data (January 21, 2016). Available at SSRN: or

Hema Yoganarasimhan (Contact Author)

Foster School of Business, University of Washington ( email )

474 Paccar Hall
Seattle, WA 98195
United States


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