Identifying the Presence and Cause of Fashion Cycles in Data

Fashions and conspicuous consumption play an important role in marketing. In this article, the author presents a three-pronged framework to analyze fashion cycles in data composed of (1) algorithmic methods for identifying cycles, (2) a statistical framework for identifying cycles, and (3) methods for examining the drivers of such cycles. In the first module, the author identifies cycles by pattern-matching the amplitude and length of cycles observed to a user-specified definition. In the second module, the author defines the conditional monotonicity property, derives conditions under which a data-generating process satisfies it, and demonstrates its role in generating cycles. A key challenge in estimating this model is the presence of endogenous lagged dependent variables, which the author addresses using system generalized method of moments estimators. Third, the author presents methods that exploit the longitudinal and geographic variations in agents' economic and cultural capital to examine the different theories of fashion. The author applies her framework to data on given names for infants, shows the presence of large-amplitude cycles both algorithmically and statistically, and confirms that the adoption patterns are consistent with Bourdieu's theory of fashion as a signal of cultural capital.