Hostname: page-component-8448b6f56d-mp689 Total loading time: 0 Render date: 2024-04-24T06:27:46.870Z Has data issue: false hasContentIssue false
  • English
  • Français

A Bayesian Approach to Dynamic Panel Models with Endogenous Rarely Changing Variables*

Published online by Cambridge University Press:  20 January 2016

Tsung-Han Tsai
Get access Rights & Permissions [Opens in a new window]

Abstract

Whether democratic and non-democratic regimes perform differently in social provision policy is an important issue to social scientists and policy makers. As political regimes are rarely changing, their long-term or dynamic effects on the outcome are of concern to researchers when they evaluate how political regimes affect social policy. However, estimating the dynamic effects of rarely changing variables in the analysis of time-series cross-sectional data by conventional estimators may be problematic when the unit effects are included in the model specification. This article proposes a model to account for and estimate the correlation between the unit effects and explanatory variables. Applying the proposed model to 18 Latin American countries, this article finds evidence that democracy has a positive effect on social spending both in the short and long term.

Type
Original Articles
Information
Political Science Research and Methods , Volume 4 , Supplement 3 , September 2016 , pp. 595 - 620
Copyright
© The European Political Science Association 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

Tsung-han Tsai is an Assistant Professor in the Department of Political Science, National Chengchi University, Taipei 11605, Taiwan (thtsai@nccu.edu.tw). An earlier version of this manuscript was presented at the 2011 Annual Meeting of the American Political Science Association, Seattle, WA. The author thanks Jeff Gill, Guillermo Rosas, Jacob Montgomery, Chia-yi Lee, Jamie Monogan, Keith Schnakenberg, and Justin Grimmer for helpful comments and suggestions at different stages of this manuscript. The author also thanks two anonymous reviewers and the PSRM editor for helping improve the manuscript. Any remaining errors are the author’s responsibility. To view supplementary material for this article, please visit http://dx.doi.org/10.1017/psrm.2015.81

References

Anderson, T. W., and Hsiao, Cheng. 1981. ‘Estimation of Dynamic Models with Error Components’. Journal of the American Statistical Association 76(375):598–606.Google Scholar
Anderson, T. W., and Hsiao, Cheng. 1982. ‘Formulation and Estimation of Dynamic Models Using Panel Data’. Journal of Econometrics 18(1):47–82.Google Scholar
Avelino, George, Brown, David S., and Hunter, Wendy. 2005. ‘The Effects of Capital Mobility, Trade Openness, and Democracy on Social Spending in Latin America, 1980–1999’. American Journal of Political Science 49(3):625–641.Google Scholar
Baltagi, Badi H. 2005. Econometric Analysis of Panel Data, 3rd ed. New York: John Wiley & Sons, Inc. Google Scholar
Bates, Douglas, Maechler, Martin, Bolker, Ben, and Walker, Steve. 2015. ‘Fitting Linear Mixed – Effects Models Using lme4’. Journal of Statistical Software 67(1):1–48.Google Scholar
Beck, Nathaniel. 2001. ‘Time-Series-Cross-Section Data: What Have we Learned in the Past Few Years?’. Annual Review of Political Science 4(1):271–293.Google Scholar
Beck, Nathaniel, and Katz, Jonathan N.. 1996. ‘Nuisance vs. Substance: Specifying and Estimating Time-Series-Cross-Section Models’. Political Analysis 6(1):1–36.Google Scholar
Beck, Nathaniel, and Katz, Jonathan N.. 2007. ‘Random Coefficient Models for Time-Series-Cross-Section Data: Monte Carlo Experiments’. Political Analysis 15(2):182–195.Google Scholar
Beck, Nathaniel, and Katz, Jonathan N.. 2011. ‘Modeling Dynamics in Time-Series-Cross-Section Political Economy Data’. Annual Review of Political Science 14:331–352.Google Scholar
Breusch, Trevor, Ward, Michael B., Nguyen, Hoa Thi Minh, and Kompas, Tom. 2011. ‘On the Fixed-Effects Vector Decomposition’. Political Analysis 19(2):123–134.Google Scholar
Brown, David S., and Hunter, Wendy. 1999. ‘Democracy and Social Spending in Latin America, 1980–92’. American Political Science Review 93(4):779–790.CrossRefGoogle Scholar
Carlin, Bradley P., and Louis, Thomas A.. 2000. Bayes and Empirical Bayes Methods for Data Analysis . New York: Chapman & Hall/CRC.Google Scholar
Casella, George, and George, Edward I.. 1992. ‘Explaining the Gibbs Sampler’. American Statistician 46(3):167–174.Google Scholar
Chib, Siddhartha, and Greenberg, Edward. 1995. ‘Understanding the Metropolis-Hastings Algorithm’. American Statistician 49(4):327–335.Google Scholar
Cominetti, Rossella. 1996. ‘Social Expenditure in Latin America: An Update’. Technical report, CEPAL Technical Department, Santiago, Chile.Google Scholar
Croissant, Yves, and Millo, Giovanni. 2008. ‘Panel Data Econometrics in R: The PLM Package’. Journal of Statistical Software 27(2):1–43.Google Scholar
De Boef, Suzanna, and Keele, Luke. 2008. ‘Taking Time Seriously’. American Journal of Political Science 52(1):184–200.Google Scholar
Enders, Walter. 2004. Applied Econometric Time Series. New York: John Wiley & Sons, Inc.Google Scholar
Gelfand, Alan E., and Smith, Adrian F. M.. 1990. ‘Sampling-Based Approaches to Calculating Marginal Densities’. Journal of the American Statistical Association 85(410):398–409.CrossRefGoogle Scholar
Gelman, Andrew. 2006. ‘Multilevel (Hierarchical) Modeling: What it Can and Cannot Do’. Technometrics 48(3):432–435.Google Scholar
Gelman, Andrew, and Hill, Jennifer. 2007. Data Analysis Using Regression and Multilevel/Hierarchical Models. New York: Cambridge University Press.Google Scholar
Gelman, Andrew, Carlin, John B., Stern, Hal S., and Rubin, Donald B.. 2004. Bayesian Data Analysis, 2nd ed. New York: Chapman & Hall/CRC.Google Scholar
Gill, Je. 2008a. Bayesian Methods: A Social and Behavioral Sciences Approach, 2nd ed. Boca Raton, FL: Chapman & Hall/CRC press.Google Scholar
Gill, Je. 2008b. Is Partial-Dimension Convergence a Problem for Inferences from MCMC Algorithms?’. Political Analysis 16(2):153–178.Google Scholar
Hamilton, James D. 1994. Time Series Analysis. Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar
Hsiao, Cheng. 2003. Analysis of Panel Data. New York: Cambridge University Press.Google Scholar
Hsiao, Chen, Hashem Pesaran, M., and Kamil Tahmiscioglu, A.. 1999. ‘Bayes Estimation of Short-Run Coefficients in Dynamic Panel Data Models’. In Chen Hsiao, M. Hashem Pesaran, Kajal Lahiri and Lung Fei Lee (eds), Analysis of Panels and Limited Dependent Variables, chapter 11, 268–296. New York, NY: Cambridge University Press.CrossRefGoogle Scholar
Huber, Evelyne, Stephens, John D., Mustillo, Thomas, and Pribble, Jenny. 2008. Social Policy in Latin America and the Caribbean Dataset, 1960–2006. Chapel Hill, NC: University of North Carolina.Google Scholar
Huber, Evelyne, Stephens, John D., Mustillo, Thomas, and Pribble, Jenny. 2008. ‘Politics and Social Spending in Latin America’. The Journal of Politics 70(2):420–436.Google Scholar
Kaufman, Robert R., and Segura-Ubiergo, Alex. 2001. ‘Globalization, Domestic Politics, and Social Spending in Latin America: A Time-Series Cross-Section Analysis, 1973–97’. World Politics 53(4):553–587.CrossRefGoogle Scholar
Keefer, Philip, and Vlaicu, Razvan. 2008. ‘Democracy, Credibility, and Clientelism’. Journal of Law, Economics, and Organization 24(2):371–406.CrossRefGoogle Scholar
Keefer, Philip, and Khemani, Stuti. 2005. ‘Democracy, Public Expenditures, and the Poor: Understanding Political Incentives for Providing Public Services’. The World Bank Research Observer 20(1):1–27.Google Scholar
Keele, Luke, and Kelly, Nathan J.. 2006. ‘Dynamic Models for Dynamic Theories: The Ins and Outs of Lagged Dependent Variables’. Political Analysis 14(2):186–205.Google Scholar
Koehler, Elizabeth, Brown, Elizabeth, and Haneuse, Sebastien J.-P.. 2009. ‘On the Assessment of Monte Carlo Error in Simulation-Based Statistical Analyses’. The American Statistician 63(2):155–162.Google Scholar
Lindley, D. V. 1972. Bayesian Statistics: A Review. Philadelphia, PA: Society for Industrial and Applied Mathematics.CrossRefGoogle Scholar
Lunn, David J., Thomas, Andrew, Best, Nicky, and Spiegelhalter, David. 2000. ‘WinBUGS – A Bayesian Modeling Framework: Concepts, Structure, and Extensibility’. Statistics and Computing 10(4):325–337.Google Scholar
Marshall, Monty G., Jaggers, Keith, and Gurr, Ted Robert. 2010. Polity IV Project: Political Regime Characteristics and Transition, 1800–2010. College Park, MD: University of Maryland.Google Scholar
Meltzer, Allan H., and Richard, Scott F.. 1981. ‘A Rational Theory of the Size of Government’. The Journal of Political Economy 89(5):914–927.Google Scholar
Mundlak, Yair. 1978. ‘On the Pooling of Time Series and Cross Section Data’. Econometrica: Journal of the Econometric Society 46(1):69–85.Google Scholar
Nerlove, Marc. 1971. ‘Further Evidence on the Estimation of Dynamic Economic Relations from a Time Series of Cross Sections’. Econometrica: Journal of the Econometric Society 39(2):359–382.Google Scholar
Nickell, Stephen. 1981. ‘Biases in Dynamic Models with Fixed Effects’. Econometrica: Journal of the Econometric Society 49(6):1417–1426.Google Scholar
Persson, Torsten, and Tabellini, Guido. 2000. Political Economics: Explaining Economic Policy. Cambridge, MA: MIT Press.Google Scholar
Pesaran, M. H., and Smith, R.. 1995. ‘Estimating Long-Run Relationships from Dynamic Heterogeneous Panels’. Journal of Econometrics 68(1):79–113.CrossRefGoogle Scholar
Plummer, Martyn. 2003. ‘Jags: A Program for Analysis of Bayesian Graphical Models Using Gibbs Sampling’. Proceedings of the 3rd International Workshop on Distributed Statistical Computing, March 20, Vienna, Austria.Google Scholar
Plümper, Thomas, and Troeger, Vera E.. 2007. ‘Efficient Estimation of Time-Invariant and Rarely Changing Variables in Finite Sample Panel Analyses with Unit Fixed Effects’. Political Analysis 15(2):124–139.Google Scholar
Plümper, Thomas, Troeger, Vera E., and Manow, Philip. 2005. ‘Panel Data Analysis in Comparative Politics: Linking Method to Theory’. European Journal of Political Research 44(2):327–354.Google Scholar
Robert, Christian. 2001. The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation, 2nd ed. New York: Springer Verlag.Google Scholar
Ross, Michael L. 2006. ‘Is Democracy Good for the Poor?’. American Journal of Political Science 50(4):860–874.Google Scholar
Rubin, Donald B. 1987. Multiple Imputation for Nonresponse in Surveys. Hoboken, NJ: John Wiley & Sons Inc.Google Scholar
Scheve, Kenneth, and Stasavage, David. 2009. ‘Institutions, Partisanship, and Inequality in the Long Run’. World Politics 61(2):215–253.Google Scholar
Sevestre, Patrick, and Trognon, Alain. 1985. ‘A Note on Autoregressive Error Components Models’. Journal of Econometrics 28(2):231–245.Google Scholar
Su, Yu-Sung, and Yajima, Masanao. 2012. A Package for Running JAGS from R. R Package ver. 0.03-08. http://cran.r-project.org/web/packages/R2jags.Google Scholar
Tsai, Tsung-han, and Gill, Je. 2012. ‘Superdiag: A Comprehensive Test Suite for Markov Chain Nonconvergence’. The Political Methodologist 19(2):12–18.Google Scholar
Van Buuren, S., and Groothuis-Oudshoorn, K.. 2011. ‘Mice: Multivariate Imputation by Chained Equations in R’. Journal of Statistical Software 45(3):1–66.Google Scholar
Western, Bruce. 1998. ‘Causal Heterogeneity in Comparative Research: A Bayesian Hierarchical Modeling Approach’. American Journal of Political Science 42(4):1233–1259.Google Scholar
Wooldridge, Jerey M. 2002. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT press.Google Scholar
World Development Indicators. 2011. Washington DC: World Bank. http://data.worldbank.org/data-catalog/world-development-indicators.Google Scholar
Supplementary material: Link

Tsai Dataset

Dataset

http://dx.doi.org/10.7910/DVN/08RCPK
Link
Supplementary material: PDF

Tsai supplementary material

Online Appendix

Download Tsai supplementary material(PDF)
PDF 104.2 KB