Replication Package for: Investor Learning about Monetary-Policy Transmission and the Stock Market
Description
This replication package contains the data, code, and instructions to reproduce all results in the paper "Investor Learning about Monetary-Policy Transmission and the Stock Market" by Daniel Andrei and Michael Hasler, forthcoming in the Journal of Financial Economics. Paper Abstract: We model how investor learning about monetary-policy transmission impacts asset prices. In an asset-pricing model, investors learn from realized inflation surprises how effectively monetary policy steers future inflation. Downward revisions in perceived effectiveness raise expected inflation persistence, increasing return volatility and risk premia. These effects intensify when policy deviates significantly from neutral or monetary-transmission uncertainty is high. We estimate the model using U.S. macro and policy data from 1954 to 2023. The resulting dynamics align with observed patterns in equity returns and volatility. Empirical tests support the model's core prediction: investor learning turns central-bank credibility into a priced risk factor. This package contains all the data and code necessary to reproduce the figures and tables in the paper and its internet appendix. The data include U.S. macroeconomic time series (real GDP, CPI, Federal funds rate, output gap) from 1954 to 2023, sourced from FRED and NIPA, as well as financial market data. The code includes a Mathematica notebook for solving the theoretical model and other Matlab scripts for the maximum likelihood estimation and all empirical tests. A README file is included with detailed, step-by-step instructions for replication.
Files
Steps to reproduce
The workflow consists of three main stages: 1. Data Collection: The data consists of monthly U.S. macroeconomic and financial time series from 1954 to 2023. Key data sources include the Federal Reserve Bank of St. Louis (FRED), the NIPA tables for real GDP, the Survey of Professional Forecasters for expected output growth, and the VIX index for market volatility. The market risk premium is from Chabi-Yo and Loudis (2020) and the monetary policy uncertainty index is from Baker et al. (2016). 2. Model Estimation and Solution: The model's structural parameters are estimated from the macroeconomic data using Maximum Likelihood Estimation, implemented in MATLAB. The resulting Hamilton-Jacobi-Bellman (HJB) equation of the model is then solved numerically using the Chebyshev collocation method. This numerical solution is implemented in a provided Mathematica notebook. 3. Generation of Results: The empirical tables and figures are generated using MATLAB scripts. These scripts use the collected data and the model-implied series from the estimation and solution stages to run all regressions and produce the final outputs presented in the paper. The included README file provides step-by-step instructions on the exact scripts to run to reproduce each specific result.