In Chapter 1, I quantitatively assess the economic implications of two regulatory paradigms in the digital economy: data privacy laws and command-and-control regulations. To this end, I develop a production-based equilibrium model that (i) microfounds firms' technology adoption decisions, (ii) incorporates ``data emissions'' as negative externalities of excessive data collection and data sharing arising from the non-rival nature of digital capital, and (iii) accounts for potential model misspecifications introduced by regulatory changes. The model implies a decomposition of the risk price associated with increasing market concentration, driven by digital capital accumulation, into two components: short-term firm-level productivity gains from adopting data-driven technologies and long-term social costs stemming from data emissions. This theoretical implication aligns with empirical evidence showing that the corresponding equity risk premia in the US have turned negative since the early 2000s, coinciding with the rapid growth of the data-trading market and data-driven technologies over the past 20 years. The model further predicts that firms adopting data-driven technologies exhibit stock returns that co-move more with market concentration growth, resembling the return profiles of growth firms. Using a calibrated model informed by financial market data, I demonstrate that the marginal social cost of data emissions decreases as technology adoption scales and can be further mitigated by increases in total factor productivity or intensity of innovation. Finally, counterfactual analysis suggests that the most effective regulatory paradigm combines data privacy laws with command-and-control regulations. This hybrid paradigm, when enforced through protocols that reduce uncertainty in data emissions while embracing uncertainty in innovation dynamics, can enhance social welfare. Chapter 2 presents a novel Bayesian approach that incorporates financial frictions into a panel structural break model, utilizing economically informed priors from intermediary asset pricing theories. The data-driven prior selection method, adept at handling unbalanced panels, enhances the identification of regime shifts and the selection of return predictors, thus improving equity return forecasts. Validated through simulations and empirical analysis, this approach boosts out-of-sample cumulative returns and Sharpe ratios. Leveraging asset holdings data and intermediary-induced priors, the approach facilitates real-time regime change detection and provides Bayesian insights into the inconsistencies of risk prices associated with intermediary risks.