IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Log in (now much improved!) to save this paper

Measuring the uncertainty of Principal Components in Dynamic Factor Models

Listed author(s):
  • Ruiz Ortega, Esther
  • Vicente Maldonado, Javier de

In the context of Dynamic Factor Models, factors are unobserved latent variables of interest. One of the most popular procedures for the factor extraction is Principal Components (PC). Measuring the uncertainty associated to factor estimates should be part of interpreting these estimates. Several procedures have been proposed in the context of PC factor extraction to estimate this uncertainty. In this paper, we show that these methods are not adequate when implemented to measure the uncertainty associated to the factor estimation. We propose an alternative procedure and analyze its finite sample properties. The results are illustrated in the context of extracting the common factors of a large system of macroeconomic variables.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: https://e-archivo.uc3m.es/bitstream/handle/10016/23974/ws201614.pdf?sequence=1
Download Restriction: no

Paper provided by Universidad Carlos III de Madrid. Departamento de Estadística in its series DES - Working Papers. Statistics and Econometrics. WS with number 23974.

as
in new window

Length:
Date of creation: Dec 2016
Handle: RePEc:cte:wsrepe:23974
Contact details of provider: Web page: http://portal.uc3m.es/portal/page/portal/dpto_estadistica

References listed on IDEAS
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.:

as
in new window


  1. Kapetanios, George & Marcellino, Massimiliano, 2010. "Factor-GMM estimation with large sets of possibly weak instruments," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2655-2675, November.
  2. Bräuning, Falk & Koopman, Siem Jan, 2014. "Forecasting macroeconomic variables using collapsed dynamic factor analysis," International Journal of Forecasting, Elsevier, vol. 30(3), pages 572-584.
  3. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2014. "Forecasting with factor-augmented error correction models," International Journal of Forecasting, Elsevier, vol. 30(3), pages 589-612.
  4. Bai, Jushan & Ng, Serena, 2010. "Instrumental Variable Estimation In A Data Rich Environment," Econometric Theory, Cambridge University Press, vol. 26(06), pages 1577-1606, December.
  5. Yohei Yamamoto, 2012. "Bootstrap Inference for Impulse Response Functions in Factor-Augmented Vector Autoregressions," Global COE Hi-Stat Discussion Paper Series gd12-249, Institute of Economic Research, Hitotsubashi University.
  6. Forni, Mario & Lippi, Marco, 2001. "The Generalized Dynamic Factor Model: Representation Theory," Econometric Theory, Cambridge University Press, vol. 17(06), pages 1113-1141, December.
  7. Bai, Jushan & Ng, Serena, 2013. "Principal components estimation and identification of static factors," Journal of Econometrics, Elsevier, vol. 176(1), pages 18-29.
  8. Gonçalves, Sílvia & Perron, Benoit, 2014. "Bootstrapping factor-augmented regression models," Journal of Econometrics, Elsevier, vol. 182(1), pages 156-173.
  9. Sydney C. Ludvigson & Serena Ng, 2009. "Macro Factors in Bond Risk Premia," Review of Financial Studies, Society for Financial Studies, vol. 22(12), pages 5027-5067, December.
  10. Jörg Breitung & Sandra Eickmeier, 2006. "Dynamic factor models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 27-42, March.
  11. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
  12. Lorenzo Pascual & Juan Romo & Esther Ruiz, 2004. "Bootstrap predictive inference for ARIMA processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(4), pages 449-465, July.
  13. Bernanke, Ben S. & Boivin, Jean, 2003. "Monetary policy in a data-rich environment," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 525-546, April.
  14. Jörg Breitung & In Choi, 2013. "Factor models," Chapters,in: Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 11, pages 249-265 Edward Elgar Publishing.
    • In Choi & Jorg Breitung, 2011. "Factor models," Working Papers 1121, Research Institute for Market Economy, Sogang University, revised Dec 2011.
  15. Angela Abbate & Sandra Eickmeier & Wolfgang Lemke & Massimiliano Marcellino, 2016. "The Changing International Transmission of Financial Shocks: Evidence from a Classical Time‐Varying FAVAR," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(4), pages 573-601, 06.
  16. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
  17. Alvarez, Rocio & Camacho, Maximo & Perez-Quiros, Gabriel, 2016. "Aggregate versus disaggregate information in dynamic factor models," International Journal of Forecasting, Elsevier, vol. 32(3), pages 680-694.
  18. Thomas J. Sargent & Christopher A. Sims, 1977. "Business cycle modeling without pretending to have too much a priori economic theory," Working Papers 55, Federal Reserve Bank of Minneapolis.
  19. 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.
  20. Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2011. "A two-step estimator for large approximate dynamic factor models based on Kalman filtering," Journal of Econometrics, Elsevier, vol. 164(1), pages 188-205, September.
  21. Alexei Onatski, 2010. "Determining the Number of Factors from Empirical Distribution of Eigenvalues," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 1004-1016, November.
  22. Van Aelst, Stefan & Willems, Gert, 2013. "Fast and Robust Bootstrap for Multivariate Inference: The R Package FRB," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 53(i03).
  23. Diebold, Francis X & Rudebusch, Glenn D, 1996. "Measuring Business Cycles: A Modern Perspective," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 67-77, February.
  24. repec:hal:journl:peer-00844811 is not listed on IDEAS
  25. Seung C. Ahn & Alex R. Horenstein, 2013. "Eigenvalue Ratio Test for the Number of Factors," Econometrica, Econometric Society, vol. 81(3), pages 1203-1227, 05.
  26. Maximo Camacho & Gabriel Perez‐Quiros & Pilar Poncela, 2015. "Extracting Nonlinear Signals from Several Economic Indicators," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(7), pages 1073-1089, November.
  27. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
  28. Ayhan Kose, M. & Otrok, Christopher & Whiteman, Charles H., 2008. "Understanding the evolution of world business cycles," Journal of International Economics, Elsevier, vol. 75(1), pages 110-130, May.
  29. Stock, James H. & Watson, Mark W., 2006. "Forecasting with Many Predictors," Handbook of Economic Forecasting, Elsevier.
  30. Massimiliano Marcellino & Carlo A. Favero & Francesca Neglia, 2005. "Principal components at work: the empirical analysis of monetary policy with large data sets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(5), pages 603-620.
  31. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2003. "Macroeconomic forecasting in the Euro area: Country specific versus area-wide information," European Economic Review, Elsevier, vol. 47(1), pages 1-18, February.
  32. Mario Forni & Lucrezia Reichlin, 1998. "Let's Get Real: A Factor Analytical Approach to Disaggregated Business Cycle Dynamics," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 453-473.
  33. Corradi, Valentina & Swanson, Norman R., 2014. "Testing for structural stability of factor augmented forecasting models," Journal of Econometrics, Elsevier, vol. 182(1), pages 100-118.
  34. Mototsugu Shintani & Zi-yi Guo, 2015. "Improving the Finite Sample Performance of Autoregression Estimators in Dynamic Factor Models: A Bootstrap Approach," Vanderbilt University Department of Economics Working Papers 15-00013, Vanderbilt University Department of Economics.
  35. Bai, Jushan & Ng, Serena, 2008. "Large Dimensional Factor Analysis," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(2), pages 89-163, June.
  36. Jean Boivin & Serena Ng, 2005. "Understanding and Comparing Factor-Based Forecasts," International Journal of Central Banking, International Journal of Central Banking, vol. 1(3), December.
  37. Stock, James H. & Watson, Mark, 2011. "Dynamic Factor Models," Scholarly Articles 28469541, Harvard University Department of Economics.
  38. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
  39. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, Oxford University Press, vol. 120(1), pages 387-422.
  40. repec:hal:journl:hal-00638009 is not listed on IDEAS
  41. Borus Jungbacker & Siem Jan Koopman, 2015. "Likelihood‐based dynamic factor analysis for measurement and forecasting," Econometrics Journal, Royal Economic Society, vol. 18(2), pages 1-21, June.
  42. Nikolay Gospodinov & Serena Ng, 2013. "Commodity Prices, Convenience Yields, and Inflation," The Review of Economics and Statistics, MIT Press, vol. 95(1), pages 206-219, March.
  43. Antoine Djogbenou & Sílvia Gonçalves & Benoit Perron, 2015. "Bootstrap inference in regressions with estimated factors and serial correlation," CIRANO Working Papers 2015s-20, CIRANO.
  44. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
  45. Connor, Gregory & Korajczyk, Robert A., 1986. "Performance measurement with the arbitrage pricing theory : A new framework for analysis," Journal of Financial Economics, Elsevier, vol. 15(3), pages 373-394, March.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:cte:wsrepe:23974. 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: (Ana Poveda)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.