Model Averaging Estimators For Handling Persistent Predictors: Developing Persistence-Adjusted Model Averaging Estimators - Softcover
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.
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Model Averaging Estimators For Handling Persistent Predictors
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Model Averaging Estimators For Handling Persistent Predictors
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Model Averaging Estimators For Handling Persistent Predictors: Developing Persistence-Adjusted Model Averaging Estimators
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ISBN 10: 6208434599
ISBN 13: 9786208434595
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Model Averaging Estimators For Handling Persistent Predictors (Paperback)
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ISBN 10: 6208434599
ISBN 13: 9786208434595
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Paperback. Condition: new. Paperback. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9786208434595
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Model Averaging Estimators For Handling Persistent Predictors
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ISBN 10: 6208434599
ISBN 13: 9786208434595
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Model Averaging Estimators For Handling Persistent Predictors (Paperback)
Published by
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ISBN 10: 6208434599
ISBN 13: 9786208434595
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Paperback. Condition: new. Paperback. 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9786208434595
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Model Averaging Estimators For Handling Persistent Predictors (Paperback)
Published by
LAP Lambert Academic Publishing, 2025
ISBN 10: 6208434599
ISBN 13: 9786208434595
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Paperback. Condition: new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9786208434595
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Model Averaging Estimators For Handling Persistent Predictors
Published by
LAP LAMBERT Academic Publishing Dez 2025, 2025
ISBN 10: 6208434599
ISBN 13: 9786208434595
New
Taschenbuch
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 216 pp. Englisch. Seller Inventory # 9786208434595
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Model Averaging Estimators For Handling Persistent Predictors : Developing Persistence-Adjusted Model Averaging Estimators
Published by
LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6208434599
ISBN 13: 9786208434595
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Model Averaging Estimators For Handling Persistent Predictors
Published by
LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6208434599
ISBN 13: 9786208434595
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