Fine-grained economic analysis in dynamic settings often requires a rich state space. This creates the curse of dimensionality, which has long posed a major computational and estimation challenge in the dynamic modeling literature. To address this issue, I develop a topology-aware computational method that exploits the structure of the state-transition graph to decompose a high-dimensional equilibrium fixed-point system into smaller local subproblems. The method also exposes within-layer parallelism, yielding considerable speedups in large-scale applications. By reducing computation time, the method substantially expands the class of dynamic models that can be feasibly estimated in empirical work.

I apply this method to study innovation dynamics in the Chinese electric vehicle industry. Capturing the key competitive mechanisms, including knowledge spillovers, charging infrastructure compatibility, and strategic heterogeneity across firms, requires a high-dimensional dynamic game. I estimate the model using firm-level data on patents, charging infrastructure, vehicle sales, and policy exposure, and then use the estimated model for counterfactual policy analysis. The results highlight the importance of incorporating firm heterogeneity into innovation policy design. The welfare-maximizing subsidy scheme is a stage-specific mix of R&D subsidies and public-charging subsidies that steers firms toward investments aligned with their comparative advantages, thereby amplifying knowledge spillovers and infrastructure compatibility.

The growing availability of big data enables firms to predict consumer search outcomes and outside options more accurately than consumers themselves. This paper examines how a firm can utilize such superior information to offer personalized buy-now discounts intended to deter consumer search. However, discounts can also serve as signals of attractive outside options, potentially encouraging rather than discouraging consumer search. We show that, despite the firm’s ability to tailor discounts across a continuum of consumer valuations, the firm-optimal equilibrium features a simple two-tier discount scheme, comprising a uniform positive discount when the consumer outside option is intermediate and no discount when the outside option is low or high. Furthermore, compared to a scenario where the firm lacks superior information, we find that the firm earns lower profits, consumers search more while their welfare remains unchanged, and total welfare declines.

To address range anxiety, the main obstacle to electric vehicle (EV) adoption, policymakers and firms have pursued two primary strategies: R&D aimed at driving range extension, and public charging network expansion. Using data from the Chinese automobile market and patent filings, this paper examines how infrastructure expansion stimulates or discourages subsequent innovation. I first identify range-improving patents using a fine-tuned large language model and quantify patents' novelty and impact through text similarity metrics. Next, drawing on the persistent regional sales reliance of Chinese automakers, I construct a sales-weighted index of charging infrastructure exposure. The study provides empirical evidence showing that the interplay of two opposing effects leads to innovation redirection: On one hand, readily available chargers make long driving ranges less critical, potentially crowding out breakthrough innovations aimed at driving-range improvements due to the substitution effect. On the other hand, a more extensive public charging network enlarges the EV market, thereby raising the marginal returns to innovations directed toward other vehicle improvements, creating a synergy effect. Specifically, easier access to public chargers significantly reduces breakthrough driving-range innovations but significantly stimulates non‑driving-range innovations. Overall, it significantly increases the total number of patents in the automotive sector. Furthermore, I estimate a structural choice model of automobiles that captures the substitution between EV driving range and access to public charging network, and simulate automakers’ expected marginal return to quality improvements under a series of counterfactual infrastructure availability. The results show that expanding charging infrastructure may shrink marginal return to driving range extension but always results in higher marginal return to engine power improvement, reinforcing the empirical evidences earlier.