Three Essays on Causal Analysis of Banking Regulation and Monetary Policy

As the second largest financial crisis after the "Great Depression", the 2007/8 financial crisis posed great challenges to policy makers. To respond to such challenges, new policies are adopted. In my dissertation, I conduct causal analysis to evaluate the effectiveness of some of the newly proposed regulations following the financial crisis. In the first chapter, Professor Lynda Khalaf and I examine the impact of the Liquidity Coverage Ratio (LCR) on bank lending in the U.S, using a Difference-in-Difference framework. with a variety of identification methods. Available evidence on the LCR is scarce and is restricted to standard event studies. In this paper, we compare standard dynamic TWFE estimates to recently proposed alternative specifications. We find no effects of the LCR on bank lending, and the assumptions embedded in the TWFE models make meaningful empirical difference. In the second chapter, Professor Lynda Khalaf and I study the dynamic causal effects of a monetary policy shock on the US economy within the Local Projection - Instrumental Variable [LP-IV] framework. Our reassessment is motivated by the emerging concerns in the literature about popular IVs that are based on high-frequency identification. We provide weak-instruments robust inference on the traditional LP-IV coefficient which we denote as the direct causal effect [DCE]. We define, estimate and test an alternative response parameter, denoted as the total causal effect [TCE], that accounts for the inherent unobservable endogeneity factor resulting from the first stage regression error. The TCE is identified whether the considered IVs are weak or strong. Our view is that both effects play an important role in capturing the net impact of a policy shock. Using identification-robust approaches produces economically more plausible results. Estimates of the TCEs suggest that DCEs may miss important responses. In the third chapter, Professor Hashmat Khan and I examine the flow view of quantitative easing (QE) using monthly data on Federal Reserve's pre-announced asset purchases from the second and third rounds of QE. We determine both average and cumulative purchasing effects using structural VAR and local projection methods, respectively. We find statistically significant effects on various financial variables and macro aggregates.