Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/63317


Title: Bayesian Inference for Dynamics of Slowly Changing Variables in Time-Series Cross-Sectional Data Analyses
Authors: 蔡宗漢
Contributors: 政治系
Keywords: Bayesian inference;multilevel modeling;dynamic panel models;time-invariant variables;social spending;Latin America working papers series
Date: 2011.09
Issue Date: 2014-01-07 14:13:19 (UTC+8)
Abstract: The time-invariant and/or rarely changing explanatory variables are of interest to political scientists, including both their short- and long-run effects. However, estimating these effects in the analysis of time-series cross-sectional (TSCS) data by the conventional estimators may be problematic when unit effects are included in the model. This paper discusses the advantages of using Bayesian multilevel modeling to estimate the dynamic effects of these slowly changing explanatory variables in the analysis of TSCS data and applies a Bayesian dynamic multilevel model to analyzing the effects of political regime on social spending in Latin America.
Relation: 2011 American Political Science Association Annual Meeting, American Political Science Association
Data Type: conference
Appears in Collections:[政治學系] 會議論文

Files in This Item:

File Description SizeFormat
34.pdf1333KbAdobe PDF951View/Open


All items in 學術集成 are protected by copyright, with all rights reserved.


社群 sharing