05477oam 22009374 450 991078833770332120230721045623.01-4623-7192-21-4527-1274-397866128429551-4518-7221-61-282-84295-1(CKB)3170000000055239(EBL)1608239(SSID)ssj0000941859(PQKBManifestationID)11614171(PQKBTitleCode)TC0000941859(PQKBWorkID)10971226(PQKB)10055765(OCoLC)608248516(MiAaPQ)EBC1608239(IMF)WPIEE2009074(EXLCZ)99317000000005523920020129d2009 uf 0engur|n|---|||||txtccrLimited Information Bayesian Model Averaging for Dynamic Panels with Short Time Periods /Alin Mirestean, Charalambos Tsangarides, Huigang ChenWashington, D.C. :International Monetary Fund,2009.1 online resource (45 p.)IMF Working PapersDescription 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 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.IMF Working Papers; Working Paper ;No. 2009/074Panel analysisBayesian statistical decision theoryEconometricsimfData ProcessingimfBayesian Analysis: GeneralimfEstimationimfData Collection and Data Estimation MethodologyimfComputer Programs: GeneralimfBayesian inferenceimfEconometrics & economic statisticsimfData capture & analysisimfBayesian modelsimfEstimation techniquesimfData processingimfEconometric modelsimfElectronic data processingimfPanel analysis.Bayesian statistical decision theory.EconometricsData ProcessingBayesian Analysis: GeneralEstimationData Collection and Data Estimation MethodologyComputer Programs: GeneralBayesian inferenceEconometrics & economic statisticsData capture & analysisBayesian modelsEstimation techniquesData processingEconometric modelsElectronic data processingMirestean Alin1472716Tsangarides Charalambos1462110Chen Huigang1472717DcWaIMFBOOK9910788337703321Limited Information Bayesian Model Averaging for Dynamic Panels with Short Time Periods3685582UNINA