LEADER 05555oam 22009734 450 001 9910812320003321 005 20200520144314.0 010 $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(IMF)WPIEE2009074 035 $a(MiAaPQ)EBC1608239 035 $a(IMF)WPIEA2009074 035 $a(EXLCZ)993170000000055239 100 $a20020129d2009 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 /$fAlin Mirestean, Charalambos Tsangarides, Huigang Chen 205 $a1st ed. 210 1$aWashington, D.C. :$cInternational Monetary Fund,$d2009. 215 $a1 online resource (45 p.) 225 1 $aIMF Working Papers 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 3 $aBayesian Model Averaging (BMA) provides a coherent mechanism to address the problem of model uncertainty. In this paper we extend the BMA framework to panel data models where the lagged dependent variable as well as endogenous variables appear as regressors. We propose a Limited Information Bayesian Model Averaging (LIBMA) methodology and then test it using simulated data. Simulation results suggest that asymptotically our methodology performs well both in Bayesian model selection and averaging. In particular, LIBMA recovers the data generating process very well, with high posterior inclusion probabilities for all the relevant regressors, and parameter estimates very close to the true values. These findings suggest that our methodology is well suited for inference in dynamic panel data models with short time periods in the presence of endogenous regressors under model uncertainty. 410 0$aIMF Working Papers; Working Paper ;$vNo. 2009/074 606 $aPanel analysis 606 $aBayesian statistical decision theory 606 $aBayesian Analysis: General$2imf 606 $aBayesian inference$2imf 606 $aBayesian models$2imf 606 $aComputer Programs: General$2imf 606 $aData capture & analysis$2imf 606 $aData Collection and Data Estimation Methodology$2imf 606 $aData Processing$2imf 606 $aData processing$2imf 606 $aEconometric models$2imf 606 $aEconometrics & economic statistics$2imf 606 $aEconometrics$2imf 606 $aElectronic data processing$2imf 606 $aEstimation techniques$2imf 606 $aEstimation$2imf 615 0$aPanel analysis. 615 0$aBayesian statistical decision theory. 615 7$aBayesian Analysis: General 615 7$aBayesian inference 615 7$aBayesian models 615 7$aComputer Programs: General 615 7$aData capture & analysis 615 7$aData Collection and Data Estimation Methodology 615 7$aData Processing 615 7$aData processing 615 7$aEconometric models 615 7$aEconometrics & economic statistics 615 7$aEconometrics 615 7$aElectronic data processing 615 7$aEstimation techniques 615 7$aEstimation 676 $a332.152 700 $aMirestean$b Alin$01659946 701 $aChen$b Huigang$01599330 701 $aTsangarides$b Charalambos$01462110 801 0$bDcWaIMF 906 $aBOOK 912 $a9910812320003321 996 $aLimited Information Bayesian Model Averaging for Dynamic Panels with Short Time Periods$94014884 997 $aUNINA