Efficient Estimation of Stochastic Parameters: A GLS Approach
Name
huo-dhuo-mfin-sloan-2024-thesis.pdf
Description
Thesis PDF
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1.06 MB
Format
Adobe PDF
Checksum (MD5)
f8a124ab4266a3ffe98d8c47dfab62c7
Author(s)
Huo, Da
Advisor(s)
Chen, Hui
Date Issued
February 2024
Publisher
Massachusetts Institute of Technology
Abstract
This thesis presents a novel rolling GLS-based model to improve the precision of time-varying parameter estimates in dynamic linear models. Through rigorous simulations, the rolling GLS model exhibits enhanced accuracy in scenarios with smaller sample sizes and maintains its efficacy when the normality assumption is relaxed, distinguishing it from traditional models like Kalman Filters. Furthermore, the thesis expands on the model to tackle more complex stochastic structures and validates its effectiveness through practical applications to real-world financial data, like inflation risk premium estimations. The research culminates in offering a robust tool for financial econometrics, enhancing the reliability of financial analyses and predictions.
MIT Department
Sloan School of Management
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