Efficient Estimation of Stochastic Parameters: A GLS Approach

Da Da Huo

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Time Series Analysis; Dynamic Linear Model; Stochastic Parameters; Least Squares (search for similar items in EconPapers)
JEL-codes: C13 C22 C32 C58 G11 G12 (search for similar items in EconPapers)
Date: 2024-01-15
New Economics Papers: this item is included in nep-ecm and nep-ets
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:119731

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