ISBN: 9786208434595 - model averaging estimators for handling persistent predictors: developing persistence-adjusted model averaging estimators (11 risultati)

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Taschenbuch. Condizione: 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.…

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 216 pp. Englisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.…

Paperback. Condizione: 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.…

Taschenbuch. Condizione: 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.…

Condizione: New. Print on Demand.

Condizione: New. Print on Demand.

Condizione: New. PRINT ON DEMAND.