05720oam 22010454 450 991096180710332120250426110533.0978661284295597814623719211462371922978145271274114527127439781451872217145187221697812828429531282842951(CKB)3170000000055239(EBL)1608239(SSID)ssj0000941859(PQKBManifestationID)11614171(PQKBTitleCode)TC0000941859(PQKBWorkID)10971226(PQKB)10055765(OCoLC)608248516(IMF)WPIEE2009074(MiAaPQ)EBC1608239(IMF)WPIEA2009074WPIEA2009074(EXLCZ)99317000000005523920020129d2009 uf 0engur|n|---|||||txtccrLimited Information Bayesian Model Averaging for Dynamic Panels with Short Time Periods /Alin Mirestean, Charalambos Tsangarides, Huigang Chen1st ed.Washington, D.C. :International Monetary Fund,2009.1 online resource (45 p.)IMF Working PapersDescription based upon print version of record.9781451916560 1451916566 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 theoryBayesian Analysis: GeneralimfBayesian inferenceimfBayesian modelsimfComputer Programs: GeneralimfData capture & analysisimfData Collection and Data Estimation MethodologyimfData ProcessingimfData processingimfEconometric modelsimfEconometrics & economic statisticsimfEconometricsimfElectronic data processingimfEstimation techniquesimfEstimationimfPanel analysis.Bayesian statistical decision theory.Bayesian Analysis: GeneralBayesian inferenceBayesian modelsComputer Programs: GeneralData capture & analysisData Collection and Data Estimation MethodologyData ProcessingData processingEconometric modelsEconometrics & economic statisticsEconometricsElectronic data processingEstimation techniquesEstimation332.152Mirestean Alin1815699Chen Huigang1815876Tsangarides Charalambos1462110DcWaIMFBOOK9910961807103321Limited Information Bayesian Model Averaging for Dynamic Panels with Short Time Periods4371484UNINA04192nam 22006495 450 991074606910332120251009075146.09789819942336981994233010.1007/978-981-99-4233-6(MiAaPQ)EBC30745835(Au-PeEL)EBL30745835(DE-He213)978-981-99-4233-6(CKB)28234559700041(EXLCZ)992823455970004120230915d2023 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierThe Discovery of Chinese Literature (Wenxue) /by Laiming Yu1st ed. 2023.Singapore :Springer Nature Singapore :Imprint: Palgrave Macmillan,2023.1 online resource (139 pages)Key Concepts in Chinese Thought and Culture,2524-8472Print version: Yu, Laiming The Discovery of Chinese Literature (Wenxue) Singapore : Palgrave Macmillan,c2023 9789819942329 9819942322 Includes bibliographical references.1. Introduction:Understanding Chinese “wenxue” in History -- 2. Literature in Chinese Traditional Culture -- 3. The Encounter of Chinese “wenxue” and Literature -- 4. Struggling for Life: The Intersection of Chinese and Foreign Concepts of Literature -- 5. Becoming A Discipline -- 6. Redefining “Literature”: The Cultural and Historical Significance of the “Literary Revolution” -- 7. How to Narrate the Chinese Literary History -- Conclusion: Concept, History and Method.This book traces the origin and evolvement of two Chinese characters “wenxue”(literature) by using the methods of conceptual history and historical and cultural semantics, and by taking the evolution and changes of the concept of the these two characters and their interpretations in the west as a window, and re-examining the contemporary morphology of concept evolution in the historical context of concept generation and development to discover the historical and cultural connotations hidden behind the characters, so as to embark on a vivid journey to explore the history of literary thought, discipline and culture. The entire book is woven with the concept of “literature” at its core. Following the author's analysis and interpretation, an interlocking and orderly network of description of ancient and modern, Chinese and foreign unfolds. In this context, the chapters are progressive and mutually responsive, forming an organic whole which is connected at the beginning and the end. For those readers who are trying to understand how Chinese “wenxue” evolved from one of the “four disciplines of Confucius” into a modern discipline and concept, this book will provide the most detailed, in-depth, and vivid historical picture. Laiming Yu, Doctor of Chinese Literature, post-doctor in History, is now a Luojia Distinguished Professor and doctoral supervisor at Wuhan University, the deputy director of the Center of Traditional Chinese Cultural Studies, Wuhan University, and also a member of the Academic Committee of Wuhan University and the deputy director of the Professor Committee of the Academy of Humanities and Social Sciences. .Key Concepts in Chinese Thought and Culture,2524-8472LiteratureSocial evolutionHistorical linguisticsPhilosophy, ChineseLiteratureCultural EvolutionHistorical LinguisticsChinese PhilosophyLiterature.Social evolution.Historical linguistics.Philosophy, Chinese.Literature.Cultural Evolution.Historical Linguistics.Chinese Philosophy.895.109Yu Laiming1429206MiAaPQMiAaPQMiAaPQBOOK9910746069103321The Discovery of Chinese Literature (Wenxue)3569145UNINA