Limited information Bayesian model averaging for dynamic panels with short time periods [[electronic resource] /] / prepared by Huigang Chen, Alin Mirestean, and Charalambos G. Tsangarides

• Record Nr.: UNINA9910464070203321
• Autore: Chen Huigang
• Pubbl/distr/stampa: [Washington D.C.], : International Monetary Fund, 2009
• ISBN: 1-4623-7192-2; 1-4527-1274-3; 9786612842955; 1-4518-7221-6; 1-282-84295-1
• Descrizione fisica: 1 online resource (45 p.)
• Collana: IMF working paper ; ; WP/09/74
• Altri autori (Persone): MiresteanAlin; TsangaridesCharalambos G
• Soggetti: Panel analysis; Bayesian statistical decision theory; Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Contents; I. Introduction; II. Model Uncertainty in the Bayesian Context; A. Model Selection and Hypothesis Testing; B. Bayesian Model Averaging; C. Choice of Priors; III. Limited Information Bayesian Model Averaging; A. A Dynamic Panel Data Model with Endogenous Regressors; B. Estimation and Moment Conditions; C. The Limited Information Criterion; IV. Monte Carlo Simualtions and Results; A. The Data Generating Process; B. Simulation Results; V. Conclusion; References; Tables; 1. Posterior Probability of the True Model; 2. Posterior Probability Ratio of True Model/Best among the Other Models; 3. Probability of Retrieving the True Model4. Model Recovery: Medians and Variances of Posterior Inclusi; 5. Model Recovery: Medians and Variances of Estimated Paramet; 6. Posterior Probability of the True Model (Non-Gaussian Case); 7. Posterior Probability Ratio: True Model/best among the Other Models (Non-Gaussian Case); 8. Probability of Retrieving the True Model (Non-Gaussian Case); 9. Model ; Recovery: Medians and Variances of Posterior Inclusion Probability for Each Variable (Non-Gaussian Case); 10. Model Recovery: Medians and Variances of Estimated Parameter Values (Non- Gaussian Case); Appendix A Figures1. Posterior Densities for the Probabilities in Table 1; 2. Posterior Densities for the Probabilities in Table 2; 3. Box Plots for Parameters in Table 5; 4. Posterior Densities for the Probabilities in Table 6; 5. Posterior Densities for the Probabilities in Table 7; 6. Box Plots for Parameters in Table 10
• Sommario/riassunto: Bayesian Model Averaging (BMA) provides a coherent mechanism to address the problem of model uncertainty. In this paper we extend the BMA framework to panel data models where the lagged dependent variable as well as endogenous variables appear as regressors. We propose a Limited Information Bayesian Model Averaging (LIBMA) methodology and then test it using simulated data. Simulation results suggest that asymptotically our methodology performs well both in Bayesian model selection and averaging. In particular, LIBMA recovers the data generating process very well, with high posterior inclusion

Waveguide-Based Photonic Sensors: From Devices to Robust Systems

• Record Nr.: UNINA9910553075403321
• Autore: Milvich Johannes
• Pubbl/distr/stampa: Karlsruhe, : KIT Scientific Publishing, 2022
• ISBN: 1000140049
• Descrizione fisica: 1 online resource (276 p.)
• Collana: Karlsruhe Series in Photonics & Communications
• Soggetti: Electrical engineering
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Sommario/riassunto: Integrated photonic sensor systems are miniaturized, mass-producible devices that leverage the mature semiconductor fabrication technology and a well-established ecosystem for photonic circuits. This book aims at a holistic treatment of waveguide-based photonic sensor systems by ; analyzing photonic waveguide design, photonic circuit design and readout design. Across all levels, a special emphasis is given to system-level performance optimization under realistic environmental conditions.