Model Averaging Estimators For Handling Persistent Predictors | Developing Persistence-Adjusted Model Averaging Estimators

Ahamuefula Ephraim Ogbonna (u. a.)

ISBN 10: 6208434599 / ISBN 13: 9786208434595

Published by LAP LAMBERT Academic Publishing, 2025

About this Item

Model Averaging Estimators For Handling Persistent Predictors | Developing Persistence-Adjusted Model Averaging Estimators | Ahamuefula Ephraim Ogbonna (u. a.) | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786208434595 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.

Seller Inventory # 134443051

Bibliographic Details

• Title: Model Averaging Estimators For Handling Persistent Predictors | Developing Persistence-Adjusted Model Averaging Estimators
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2025
• Language: English
• ISBN 10: 6208434599
• ISBN 13: 9786208434595
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 13D millimeters 220 millimeters width by 150 millimeters height by 13 millimeters depth
• Weight: 340 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

This book addresses the challenge of endogeneity induced by persistent predictors in linear regression models (LRM), an overlooked factor that increases model uncertainty. It aimed to develop model averaging estimators capable of accounting for the persistence property in predictors. The LRM was modified with persistence-adjustment terms, designed to correct for endogeneity by incorporating first-differences of the predictors. Two estimators, Modified Bayesian Model Averaging (MBMA) and Modified Weighted-Average Least Squares (MWALS), are proposed; and compared with existing variants — Bayesian Model Averaging (BMA), Weighted-Average Least Squares (WALS), and Ordinary Least Squares (OLS) — through Monte Carlo simulations under various persistence scenarios (none, low-transient, high-transient, and permanent). Performance was evaluated using metrics like Absolute Bias, Relative Efficiency, Posterior Inclusion Probability, and Relative Root Mean Square Error. Results highlight the importance of persistence-adjusted model averaging for improving estimation accuracy and forecast reliability in models with persistent predictors; and their characteristic sample sample properties.