Tentations [[electronic resource] ] : their nature, danger, cure / / by Richard Capel ... ; to which is added a briefe dispute, as touching restitution in the case of usury

• Record Nr.: UNISA996395015303316
• Autore: Capel Richard <1586-1656.>
• Pubbl/distr/stampa: London, : Printed by T.B. for Iohn Bartlet, and are to be sold at his shop ..., 1650
• Edizione: [The fourth edition /]
• Descrizione fisica: [27], 387 p
• Soggetti: Temptation; Sin; Usury - Religious aspects
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: "Tentations, their nature, danger, cure, the third part" has special t.p.; "Epistle to the reader" signed: Richard Sibbs.; Some pages are stained, faded and have print show-through. Signature **5 is marked and p. 7 has faded print; p. 187 and 201-208 are cropped and p. 239 is misprint in filmed copy. Beginning-p. 15 and p. 180-245 photographed from Bodleian Library copy and inserted at the end.; Reproduction of original in Union Theological Seminary Library, New York.; Marginal notes.
• Sommario/riassunto: eebo-0160

Limited Information Bayesian Model Averaging for Dynamic Panels with Short Time Periods / / Alin Mirestean, Charalambos Tsangarides, Huigang Chen

• Record Nr.: UNINA9910788337703321
• Autore: Mirestean Alin
• 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 Papers
• Altri autori (Persone): TsangaridesCharalambos; ChenHuigang
• Soggetti: Panel analysis; Bayesian statistical decision theory; Econometrics; Data Processing; Bayesian Analysis: General; Estimation; Data Collection and Data Estimation Methodology; Computer Programs: General; Bayesian inference; Econometrics & economic statistics; Data capture & analysis; Bayesian models; Estimation techniques; Data processing; Econometric models; Electronic data processing
• 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 probabilities for all the relevant regressors, and parameter estimates very close to the true values. These findings suggest that our methodology is well suited for inference in dynamic panel data models with short time periods in the presence of endogenous regressors under model uncertainty.