Model Averaging Estimators For Handling Persistent Predictors: Developing Persistence-Adjusted Model Averaging Estimators - Softcover
Inhaltsangabe
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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Über die Autorin bzw. den Autor
Dr. Ogbonna, a statistician with over 9 years' experience, specializes in econometric modeling and empirical research. A CEAR fellow, he aids Nigerian economic policy through macroeconomic forecasts and has published in top journals. Prof. Olubusoye, a macroeconometric expert, consults globally and holds top academic and professional distinctions.
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Paperback. Zustand: 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. Bestandsnummer des Verkäufers 9786208434595
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Paperback. Zustand: 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. Bestandsnummer des Verkäufers 9786208434595
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Paperback. Zustand: 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. Bestandsnummer des Verkäufers 9786208434595
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Model Averaging Estimators For Handling Persistent Predictors | Developing Persistence-Adjusted Model Averaging Estimators
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Taschenbuch. Zustand: Neu. 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. Bestandsnummer des Verkäufers 134443051
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Model Averaging Estimators For Handling Persistent Predictors
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9786208434595
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