Maximum likelihood estimation for dynamic factor models with missing data
This paper concerns estimating parameters in a high-dimensional dynamic factor model by the method of maximum likelihood. To accommodate missing data in the analysis, we propose a new model representation for the dynamic factor model. It allows the Kalman filter and related smoothing methods to evaluate the likelihood function and to produce optimal factor estimates in a computationally efficient way when missing data is present. The implementation details of our methods for signal extraction and maximum likelihood estimation are discussed. The computational gains of the new devices are presented based on simulated data sets with varying numbers of missing entries.
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- Ruud, Paul A., 1991.
"Extensions of estimation methods using the EM algorithm,"
Journal of Econometrics,
Elsevier, vol. 49(3), pages 305-341, September.
- Paul A. Ruud., 1988. "Extensions of Estimation Methods Using the EM Algorithm.," Economics Working Papers 8899, University of California at Berkeley.
- 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.
- 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.
- 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.
- Bańbura, Marta & Modugno, Michele, 2010.
"Maximum likelihood estimation of factor models on data sets with arbitrary pattern of missing data,"
Working Paper Series
1189, European Central Bank.
- Marta Bańbura & Michele Modugno, 2014. "Maximum Likelihood Estimation Of Factor Models On Datasets With Arbitrary Pattern Of Missing Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 133-160, 01.
- Watson, Mark W. & Engle, Robert F., 1983. "Alternative algorithms for the estimation of dynamic factor, mimic and varying coefficient regression models," Journal of Econometrics, Elsevier, vol. 23(3), pages 385-400, December.
- S. J. Koopman & J. Durbin, 2003. "Filtering and smoothing of state vector for diffuse state-space models," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(1), pages 85-98, 01.
- repec:dgr:uvatin:20080007 is not listed on IDEAS
- Otrok, Christopher & Whiteman, Charles H, 1998.
"Bayesian Leading Indicators: Measuring and Predicting Economic Conditions in Iowa,"
International Economic Review,
Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 997-1014, November.
- Otrok, C. & Whiteman, C.H., 1996. "Bayesian Leading Indicators: Measuring and Predicting Economic Conditions in Iowa," Working Papers 96-14, University of Iowa, Department of Economics.
- Otrok, Christopher & Pourpourides, Panayiotis M., 2008.
"On The Cyclicality of Real Wages and Wage Differentials,"
Cardiff Economics Working Papers
E2008/19, Cardiff University, Cardiff Business School, Economics Section, revised Mar 2009.
- Christopher Otrok & Panayiotis M. Pourpourides, 2011. "On the Cyclicality of Real Wages and Wage Differentials," Working Papers 2011-4, Central Bank of Cyprus.
- Kapetanios, George & Marcellino, Massimiliano, 2006.
"A Parametric Estimation Method for Dynamic Factor Models of Large Dimensions,"
CEPR Discussion Papers
5620, C.E.P.R. Discussion Papers.
- George Kapetanios & Massimiliano Marcellino, 2009. "A parametric estimation method for dynamic factor models of large dimensions," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(2), pages 208-238, 03.
- Thomas J. Sargent & Christopher A. Sims, 1977.
"Business cycle modeling without pretending to have too much a priori economic theory,"
55, Federal Reserve Bank of Minneapolis.
- Tom Doan, . "RATS program to estimate observable index model from Sargent-Sims(1977)," Statistical Software Components RTZ00126, Boston College Department of Economics.
- Durbin, James & Koopman, Siem Jan, 2001.
"Time Series Analysis by State Space Methods,"
Oxford University Press, number 9780198523543, March.
- Tom Doan, . "SEASONALDLM: RATS procedure to create the matrices for the seasonal component of a DLM," Statistical Software Components RTS00251, Boston College Department of Economics.
- Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
- 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.
- Siem Jan Koopman & N.G. Shephard, 1992. "Exact Score for Time Series Models in State Space Form (Now published in Biometrika (1992), 79, 4, pp.283-6.)," STICERD - Econometrics Paper Series /1992/241, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
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