A Generalized Dynamic Factor Model for Panel Data: Estimation with a Two-Cycle Conditional Expectation-Maximization Algorithm
We develop a generalized dynamic factor model for panel data with the goal of estimating an unobserved index. While similar models have been developed in the literature of dynamic factor analysis, our contribution is threefold. First, contrary to simple dynamic factor analysis where multiple attributes of the same subject are measured at each time period, our model also accounts for multiple subjects. It is therefore suitable to a panel data framework. Second, our model estimates a unique unobserved index for every subject for every time period, as opposed to previous work where a temporal index common to all subjects was used. Third, we develop a novel iterative estimation process which we call the Two-Cycle Conditional Expectation-Maximization (2CCEM) algorithm and is flexible enough to handle a variety of different types of datasets. The model is applied on a panel measuring attributes related to the operation of water and sanitation utilities.
|Date of creation:||Jan 2013|
|Date of revision:|
|Contact details of provider:|| Web page: http://www.umass.edu/resec/|
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
- Caroline van den Berg & Alexander Danilenko, 2011. "The IBNET Water Supply and Sanitation Performance Blue Book," World Bank Publications, The World Bank, number 2545.
- Jungbacker, B. & Koopman, S.J. & van der Wel, M., 2011. "Maximum likelihood estimation for dynamic factor models with missing data," Journal of Economic Dynamics and Control, Elsevier, vol. 35(8), pages 1358-1368, August.
- Ben S. Bernanke & Jean Boivin, 2001.
"Monetary Policy in a Data-Rich Environment,"
NBER Working Papers
8379, National Bureau of Economic Research, Inc.
- Lucrezia Reichlin & Mario Forni & Marc Hallin & Marco Lippi, 2001.
"Coincident and leading indicators for the Euro area,"
ULB Institutional Repository
2013/10137, ULB -- Universite Libre de Bruxelles.
- Forni, Mario, et al, 2001. "Coincident and Leading Indicators for the Euro Area," Economic Journal, Royal Economic Society, vol. 111(471), pages C62-85, May.
- James H. Stock & Mark W. Watson, 1989.
"New Indexes of Coincident and Leading Economic Indicators,"
in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409
National Bureau of Economic Research, Inc.
- Stock, J.H. & Watson, M.W., 1989. "New Indexes Of Coincident And Leading Economic Indicators," Papers 178d, Harvard - J.F. Kennedy School of Government.
- Mario Forni & Marc Hallin & Lucrezia Reichlin & Marco Lippi, 2000.
"The generalised dynamic factor model: identification and estimation,"
ULB Institutional Repository
2013/10143, ULB -- Universite Libre de Bruxelles.
- Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
- Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 1999. "The Generalized Dynamic Factor Model: Identification and Estimation," CEPR Discussion Papers 2338, C.E.P.R. Discussion Papers.
- Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012.
"A Quasi Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models,"
Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers)
- Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1014-1024, November.
- Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2006. "A quasi maximum likelihood approach for large approximate dynamic factor models," Working Paper Series 0674, European Central Bank.
- Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2006. "A Quasi Maximum Likelihood Approach for Large Approximate Dynamic Factor Models," CEPR Discussion Papers 5724, C.E.P.R. Discussion Papers.
- Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2008. "A Quasi Maximum Likelihood Approach for Large Approximate Dynamic Factor Models," Working Papers ECARES 2008_034, ULB -- Universite Libre de Bruxelles.
- Donald Rubin & Dorothy Thayer, 1982. "EM algorithms for ML factor analysis," Psychometrika, Springer, vol. 47(1), pages 69-76, March.
- Grosskopf, S. & Valdmanis, V., 1987. "Measuring hospital performance : A non-parametric approach," Journal of Health Economics, Elsevier, vol. 6(2), pages 89-107, June.
- Jean Boivin & Serena Ng, 2003.
"Are More Data Always Better for Factor Analysis?,"
NBER Working Papers
9829, National Bureau of Economic Research, Inc.
- Sudhir Anand and Amartya Sen, 1994. "Human development Index: Methodology and Measurement," Human Development Occasional Papers (1992-2007) HDOCPA-1994-02, Human Development Report Office (HDRO), United Nations Development Programme (UNDP).
- Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
- repec:cup:cbooks:9780521405737 is not listed on IDEAS
When requesting a correction, please mention this item's handle: RePEc:dre:wpaper:2013-1. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Eileen Keegan)
If references are entirely missing, you can add them using this form.