December 29, 2025

This document provides a comprehensive analysis of bank borrowing behavior across Federal Reserve emergency facilities (BTFP and Discount Window) during the March 2023 banking crisis. With Added new models.

This analysis examines bank borrowing choices during the 2023 banking crisis using three complementary approaches: Model Framework: Linear Probability Model (LPM): BTFP vs DW among borrowers Logit Model: BTFP vs DW among borrowers Multinomial Logit: Choice among {No participation, BTFP only, DW only, Both BTFP+DW} Trivariate Probit: Joint modeling of BTFP, DW, and FHLB decisions with error correlations.

This analysis examines bank borrowing behavior across Federal Reserve emergency facilities (BTFP and Discount Window) during the March 2023 banking crisis. We exploit the institutional differences between facilities—particularly BTFP’s par valuation of eligible collateral—to test whether banks strategically selected facilities based on balance sheet vulnerabilities.

Version 1

1. California State Maps Individual county maps for each year (2018-2024) Combined multi-panel view Summary statistics table 2. US Maps by Year Individual full US county maps for each year Combined multi-panel overview 3. Year-to-Year Change Maps Percentage change maps for each transition period Uses diverging color scale (red for decrease, blue for increase) Summary statistics for each period 4. Distribution Analysis Histogram with kernel density overlay for each year Normal distribution curve overlay (dashed green line) Q-Q plots to assess normality Statistical summaries (mean, median, SD) 5. Top/Bottom 1% and 5% Maps Separate maps for both 1% and 5% thresholds Generated for each year (2018-2024) Automatically exports CSV files with county lists Summary tables for the most recent year 6. Additional Features: National trend analysis with interquartile range Growth rate calculations All outputs automatically saved to your specified directory Professional formatting with table of contents Comprehensive documentation

This analysis examines **who used the Bank Term Funding Program (BTFP)** during the 2023 banking crisis and what determined that choice. Building on Jiang et al. (2024) Market-to-market (MTM) Loss, we test whether BTFP's unique **par valuation** feature attracted banks facing **insolvency risk** (not just liquidity risk). **Key Hypotheses:** 1. Banks with higher insolvency risk AND more OMO-eligible assets preferentially chose BTFP 2. BTFP usage is driven more by insolvency concerns than pure liquidity needs 3. The interaction of run risk and OMO-eligible capacity predicts BTFP usage (Borrowing)

This code maps counties top 5/1 percentile and bottom 5/1 percentile by bbd usage

Brad Band penetration map by county and year.

EPA and House data with an extended model incorporating GDP and Employment rate. These Data sets are the final version.

This analysis investigates the relationship between air quality (AQI) and housing prices (log-transformed) using county-level panel data. Initially, the study covers data from 2000 to 2022, utilizing both fixed and random effects models to control for unobserved heterogeneity across counties and time. The Hausman test was conducted to check for endogeneity, with results indicating the random effects model is consistent. In a follow-up analysis, the model was re-run for the shorter period of 2017-2022, incorporating additional control variables such as GDP and the employment rate. The results provide a more focused look at recent years and the impact of these economic factors alongside AQI.

Combined data wildfire aqi and house price.

This document offers an analysis of how the air quality index (AQI) impacts housing prices across U.S. counties from 2000 to 2022. Using R, we refine our dataset to remove outliers, then apply linear and fixed effects regression models to understand AQI's influence on log-transformed housing prices, controlling for county and annual variations.