We study two classes of problems within Algorithmic Economics: revenue guarantees of simple mechanisms, and social welfare guarantees of auctions. We develop new structural and algorithmic tools for addressing these problems, and obtain the following results: In the 𝑘-unit model, four canonical mechanisms can be classified as: (i) the discriminating group, including Myerson Auction and Sequential Posted-Pricing, and (ii) the anonymous group, including Anonymous Reserve and Anonymous Pricing. We prove that any two mechanisms from the same group have an asymptotically tight revenue gap of 1 + θ(1 /√𝑘), while any two mechanisms from the different groups have an asymptotically tight revenue gap of θ(log 𝑘). In the single-item model, we prove a nearly-tight sample complexity of Anonymous Reserve for every value distribution family investigated in the literature: [0, 1]-bounded, [1, 𝐻]-bounded, regular, and monotone hazard rate (MHR). Remarkably, the setting-specific sample complexity poly(𝜖⁻¹) depends on the precision 𝜖 ∈ (0, 1), but not on the number of bidders 𝑛 ≥ 1. Further, in the two bounded-support settings, our algorithm allows correlated value distributions. These are in sharp contrast to the previous (nearly-tight) sample complexity results on Myerson Auction. In the single-item model, we prove that the tight Price of Anarchy/Stability for First Price Auctions are both PoA = PoS = 1 - 1/𝜖² ≈ 0.8647.

This dissertation contains three chapters. The first chapter, Strategic Alliance and Endogenous Production Network, examines how U.S. firms’ involvement in strategic alliances interacts with their endogenous choice of production networks. The results reveal that the alliance firm is more likely to actively create and break supply chains, especially with customers or suppliers from the industries within the alliance-related industrial scope. Moreover, such interactions are stronger when the updated customers and suppliers have closer proximity to the alliance-related industries. To rationalize these stylized findings, we develop a model featured with the firm’s endogenous searching of supplier candidates and endogenous input sourcing strategy. Furthermore, strategic alliance is introduced as a mitigation of friction in candidate searching. The model implies that the strategic alliance could encourage the firm’s search for supplier candidates and boost the adding and dropping of production networks simultaneously. The second chapter, R&D, Risk Premia, and Credit Spreads, is motivated by the empirical evidence that among the U.S. firms with both publicly traded equity and bonds, the R&D-intensive ones tend to show higher expected equity returns, but lower leverage, default rates and credit spreads than R&D-non-intensive ones. To provide a unified explanation for these cross-sectional differences, we propose a production-based dynamic stochastic general equilibrium model featured with long-run risk and disaster risk. Specifically, we assume that the economy consists of an R&D and non-R&D sector. When involved in innovation, the R&D sector is assumed to face a rare disaster shock in the accumulation of the intangible R&D capital. The model implies that the high monopolistic rent increases the market value of R&D sector and generates a lower default rate and credit spread compared to the non-R&D sector. Besides, despite the low leverage capacity restricted by the non-collateralizable intangible capital, the business risk underlying the innovative activities plays a dominant role and results in an overall higher equity return of the R&D sector. Moreover, the model generates sizable heterogeneity in the quantities of interest between the R&D and non-R&D sector as observed from the data, and fits the aggregate macroeconomic and asset pricing moments reasonably well. The third chapter, Measuring Common and Industry-Specific Uncertainty: A Bayesian Approach, estimates the measures of the common and industry-specific uncertainty from U.S. quarterly industry-level financial characteristics by a Bayesian dynamic factor model. In this model, we assume that the industrial financial characteristics are driven by common and industry-specific factors that evolve by VAR processes, where the time-varying standard deviation of the corresponding innovation terms are considered as proxies of common and industrial uncertainty. Then, we compare the estimated common uncertainty measure with three existing aggregate uncertainty measures. The results suggest that our measure interacts with real economy and tracks the business cycles like the other three measures. Moreover, we test if these uncertainty measures could forecast the stock returns of industrial portfolios together with other moments estimated from the model. The results suggest that a time-varying linear forecasting model of the uncertainty measures performs well in return forecasting both in short and long run for most of the industries.