Dynamic structural models capture forward looking behavior on the part of economic agents. While these models can bring new insights, the computational cost of estimating these models can be prohibitive. In the three chapters of my dissertation, I use dynamic models to capture qualitative insights into the consumer packaged goods industry, while introducing methodological innovations that expand the range of problems where dynamic models can be estimated. In the first chapter, I seek to explain counter-cyclic pricing, the phenomenon where the price of seasonal goods increases in periods of high demand. I do this by modeling consumers who may purchase without search. Consumers are more likely to purchase without search in low demand periods, which decreases estimated price sensitivity. I test this explanation using a dynamic, structural inventory model where consumers make decisions on whether to search, which reveals price promotions. I estimate this model by developing a cyclic-successive approximation algorithm, which removes the computational burden of adding cyclic variables to the state space of any dynamic model.Several recent papers develop methods to identify discount factors in dynamic models. However, in some cases, the estimation of discount factors is a computational challenge. The nested pseudo likelihood method of Agguiregabiria and Mira (2007) allows the value function to be calculated without costly contraction mapping evaluations. However, when the discount factor is being estimated, this computational efficiency is lost. In the second chapter of my dissertation, I restore this computational efficiency through an application of the eigendecomposition.The third chapter is a joint work with Ron Borkovsky, Avi Goldfarb, and Sridhar Moorthy, in which we capture the inherently dynamic nature of brand value. Firms invest in brand equity through advertising, and the value of this equity will depend on the expected decisions of rival firms. Measuring brand value then requires one to account for the current state of the market, and the evolution of the market in the future. We apply our approach by estimating brand values in the stacked potato chips industry by adapting the Pakes-McGuire quality ladder to allow firms to have a stochastic advertising decision.Ph.D
