LEADER 04102nam 2200625Ia 450 001 9910464070203321 005 20170821160733.0 010 $a1-4623-7192-2 010 $a1-4527-1274-3 010 $a9786612842955 010 $a1-4518-7221-6 010 $a1-282-84295-1 035 $a(CKB)3170000000055239 035 $a(EBL)1608239 035 $a(SSID)ssj0000941859 035 $a(PQKBManifestationID)11614171 035 $a(PQKBTitleCode)TC0000941859 035 $a(PQKBWorkID)10971226 035 $a(PQKB)10055765 035 $a(OCoLC)608248516 035 $a(MiAaPQ)EBC1608239 035 $a(EXLCZ)993170000000055239 100 $a20041202d2009 uf 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aLimited information Bayesian model averaging for dynamic panels with short time periods$b[electronic resource] /$fprepared by Huigang Chen, Alin Mirestean, and Charalambos G. Tsangarides 210 $a[Washington D.C.] $cInternational Monetary Fund$d2009 215 $a1 online resource (45 p.) 225 1 $aIMF working paper ;$vWP/09/74 300 $aDescription based upon print version of record. 311 $a1-4519-1656-6 320 $aIncludes bibliographical references. 327 $aContents; 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 327 $a3. 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) 327 $aAppendix 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 10 330 $aBayesian Model Averaging (BMA) provides a coherent mechanism to address the problem of model uncertainty. In this paper we extend the BMA framework to panel data models where the lagged dependent variable as well as endogenous variables appear as regressors. We propose a Limited Information Bayesian Model Averaging (LIBMA) methodology and then test it using simulated data. Simulation results suggest that asymptotically our methodology performs well both in Bayesian model selection and averaging. In particular, LIBMA recovers the data generating process very well, with high posterior inclusion 410 0$aIMF working paper ;$vWP/09/74. 606 $aPanel analysis 606 $aBayesian statistical decision theory 608 $aElectronic books. 615 0$aPanel analysis. 615 0$aBayesian statistical decision theory. 700 $aChen$b Huigang$0960816 701 $aMirestean$b Alin$0964959 701 $aTsangarides$b Charalambos G$0943498 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910464070203321 996 $aLimited information Bayesian model averaging for dynamic panels with short time periods$92189323 997 $aUNINA