04102nam 2200625Ia 450 991046407020332120170821160733.01-4623-7192-21-4527-1274-397866128429551-4518-7221-61-282-84295-1(CKB)3170000000055239(EBL)1608239(SSID)ssj0000941859(PQKBManifestationID)11614171(PQKBTitleCode)TC0000941859(PQKBWorkID)10971226(PQKB)10055765(OCoLC)608248516(MiAaPQ)EBC1608239(EXLCZ)99317000000005523920041202d2009 uf 0engur|n|---|||||txtccrLimited information Bayesian model averaging for dynamic panels with short time periods[electronic resource] /prepared by Huigang Chen, Alin Mirestean, and Charalambos G. Tsangarides[Washington D.C.] International Monetary Fund20091 online resource (45 p.)IMF working paper ;WP/09/74Description based upon print version of record.1-4519-1656-6 Includes bibliographical references.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 Models3. 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 10Bayesian 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 IMF working paper ;WP/09/74.Panel analysisBayesian statistical decision theoryElectronic books.Panel analysis.Bayesian statistical decision theory.Chen Huigang960816Mirestean Alin964959Tsangarides Charalambos G943498MiAaPQMiAaPQMiAaPQBOOK9910464070203321Limited information Bayesian model averaging for dynamic panels with short time periods2189323UNINA