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Cover -- Contents -- I. Introduction -- II. The Role Of Debt in Macro-Financial Models -- III. Empirical Analysis and Results -- A. Household Debt and Output Growth in 80 Countries -- B. Institutional Factors and Distributional Characteristics -- C. Systemic Banking Crises -- D. Neglected Downside Risk -- E. Debt Overhang: Micro-Level Evidence -- IV. Robustness: Panel Var Analysis -- A. Exchange Rate Regime -- B. Distributional Characteristics of Household Debt -- V. Conclusions -- Appendix I. Data Description and Methodology -- Appendix II. Tables and Figures -- A. Tables -- B. Figures -- References -- Tables -- 1. Summary Statistics -- 2a. Household Debt and Future GDP Growth -- 2b. Household Debt and Future GDP Growth: AEs and EMs -- 3. The Role of Institutional Factors, Policies, and Household-Level Debt Characteristics -- 4. Probability of Systemic Banking Crisis -- 5a. Probability of Systemic Banking Crisis: Robustness-Lags -- 5b. Probability of Systemic Banking Crisis: Robustness-Methods -- 6. Bank Equity Returns and Crashes -- 7. Abnormal Returns for Bank Stocks -- 8. Euro Area: Household Debt Overhang -- Figures -- 1. Systemic Banking Crises: Area Under Curve (AUC) -- 2. Europe: Debt Overhang and Consumption (Macro-Level) -- 3. Euro Area Households: Debt Overhang and Consumption (Micro-Level) -- 4. Panel Var Analysis: Baseline -- 5. Panel Var Analysis: High vs Low Exchange Rate Flexibility -- 6. Panel Var Analysis: Participation Rate of Low-Income Households and Financial Development -- 7. Panel Var Analysis: High vs Low Participation for Low Income Borrowers -- 8. Panel Var Analysis: High vs Low Debt Share for Low-Income Borrowers.

From the Amazon Prime Air drone delivery service to the usage of unmanned aerial vehicles (UAV) in military operations, recent years have seen the development of autonomous flight technologies becoming one of the major research topics in the drone industry. Tracking the geographic position of drones is a crucial part of any autonomous flight, but the common methods of drone location tracking either have too large of an error margin or require extensive environmental setup. The aforementioned issues are major roadblocks in the advancement of autonomous flight operations. The proposed solution is a new and improved method to track the location of a drone relative to a single reference point. This method will not require any environmental setup and offers a greater degree of precision than the commonly used Global Positioning System (GPS). The designed proof of concept model, which is a completely modular and self-reliant radio-frequency (RF) based location tracking system, was built to show the viability of this new drone tracking method. The tracking system can determine the relative location of a radio-frequency source with only one receiver module. By requiring only one receiver, this tracking system eliminates the need to set up a triangulation zone. Additionally, optimizing the tracking system to generate a location from the RF telemetry signals needed in user-drone communication, the solution effectively presents an efficient manner to track a drone without the need for additional attachments. The proposed solution introduces a novel method that has the potential to vastly improve autonomous flight development and push it to full realization and fruition.