Estimation of Common Factors under Cross-Sectional and Temporal Aggregation Constraints

International Statistical Review

Volumn 79, Issue 3, 2011, Pages 455-476

  Proietti, Tommaso ^a  

^a UNIVERSITY OF SYDNEY   (Australia)

Author keywords

Chain linking; Dynamic factor models; EM algorithm; Monthly GDP; Nonlinear smoothing; Nonlinear state space models; Temporal disaggregation

Indexed keywords

EID: 82055200212     PISSN: 03067734     EISSN: 17515823     Source Type: Journal    
DOI: 10.1111/j.1751-5823.2011.00152.x     Document Type: Article

References (53)

1. Altissimo, F., Cristadoro, R., Forni, M., Lippi, M. & Veronese, G. (2010). New eurocoin: tracking economic growth in real time. Rev. Econ. Stat., 92, 1024-1034.
2. Amenegual, D. & Watson, M.W. (2007). Consistent estimation of the number of dynamic factors in a large N and T panel. J. Bus. Econ. Stat., 25, 91-96.
3. Anderson, B.D.O. & Moore, J.B. (1979). Optimal Filtering. Englewood Cliffs, NJ: Prentice-Hall.
4. Angelini, E., Henry, J. & Marcellino, M. (2006). Interpolation and back-dating with a large information set. J. Econ. Dynamics and Control, 30, 2693-2724.
5. Aruoba, S.B., Diebold, F.X. & Scotti, C. (2009). Real-time measurement of business conditions. J. Bus. Econom. Statist., 27, 417-427.
6. Bai, J. (2003). Inferential theory for factor models of large dimension. Econometrica, 71, 135-171.
7. Bai, J. & Ng, S. (2002). Determining the number of factors in approximate factor models. Econometrica, 70, 191-221.
8. Bańbura, M. & Rünstler, G. (2007). A look into the factor model black box. Publication lags and the role of hard and soft data in forecasting GDP. ECB working paper No 751. European Central Bank, Frankfurt am Main.
9. Bartholomew, D.J. & Knott, M. (1999). Latent Variable Models and Factor Analysis. London: Arnold.
10. Baxter, M. & King, R.G. (1999). Measuring business cycles: approximate band-pass filters for economic time series. Rev. Econom. Statist., 81, 575-593.
11. Bloem, A., Dippelsman, R.J. & Maehle, N.O. (2001). Quarterly National Accounts Manual Concepts, Data Sources, and Compilation. International Monetary Fund.
12. Boivin, J. & Ng, S. (2004). Are more data always better for factor analysis? J. Econometrics, 132, 169-194.
13. Schumacher, C. & Breitung, J. (2008). Real-time forecasting of GDP based on a large factor model with monthly and quarterly data. Internat. J. Forecasting, 24, 386-398.
14. Bryson, A.E. & Ho, Y.C. (1969). Applied Optimal Control: Optimization, Estimation, and Control. Waltham, MA: Blaisdell Publishing.
15. Chamberlain, G. & Rothschild, M. (1983). Arbitrage, factor structure and mean-variance analysis in large asset markets. Econometrica, 51, 1281-1304.
16. Chow, G. & Lin, A.L. (1971). Best linear unbiased interpolation, distribution and extrapolation of time series by related series. Rev. Econom. Statist., 53(4), 372-375.
17. Cristadoro, R., Forni, M., Reichlin, L. & Veronese, G. (2005). A core inflation indicator for the euro area. J. Money Credit Bank., 37, 539-560.
18. Dempster, A., Laird, N. & Rubin, D. (1977). Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. Series B, 39(1), 1-38.
19. de Jong, P. (1989). Smoothing and interpolation with the state space model. J. Amer. Statist. Assoc., 84, 1085-1088.
20. de Jong, P. (1991). The diffuse Kalman filter. Ann. Stat., 19, 1073-1083.
21. Doornik, J.A. (2006). Ox 4.0 - An Object-Oriented Matrix Programming Language. London: Timberlake Consultants Ltd.
22. Durbin, J. & Koopman, S.J. (2001). Time Series Analysis by State Space Methods. New York: Oxford University Press.
23. Engle, R.F. & Watson, M.W. (1981). A One-Factor Multivariate Time Series Model of Metropolitan Wage Rates. J. Amer. Statist. Assoc., 76, 774-778.
24. Eurostat (1999). Handbook of quarterly national accounts, Luxembourg, European Commission.
25. Forni, M., Hallin, M., Lippi, M. & Reichlin, L. (2000). The generalized dynamic factor model: identification and estimation. Rev. Econ. Stat., 82, 540-554.
26. Forni, M., Hallin, M., Lippi, M. & Reichlin, L. (2001). Coincident and leading indicators for the euro area. Econ. J., 111, C62-C85.
27. Forni, M., Hallin, M., Lippi, M. & Reichlin, L. (2005). The generalized factor model: one-sided estimation and forecasting. J. Amer. Statist. Assoc., 100, 830-840.
28. Geweke, J.F. (1977). The dynamic factor analysis of economic time series. In Latent Variables in Socio-economic Models, Eds. D.J. Aigner & A.S. Goldberger, pp. 365-383. Amsterdam: North-Holland.
29. Geweke, J.F. & Singleton, K.J. (1981). Maximum likelihood "confirmatory" factor analysis of economic time series. Int. Econ. Rev., 22, 37--54.
30. Gill, P.E., Murray, W., Saunders, M.A. & Wright, M.H. (1989). Constrained nonlinear programming. In Handbooks in Operations Research and Management Science, Vol. 1, Optimization, Eds. G.L. Nemhauser, A.H.G. Rinnooy Kan & M.J. Todd, pp. 171-210. Amsterdam: Elsevier.
31. Gower, J.C. & Hand, D.J. (1996). Biplots. London: Chapman & Hall.
32. Hallin, M. & Liska, R. (2007). Determining the number of factors in the generalized factor model. J. Amer. Statist. Assoc., 102, 603-617.
33. Harvey, A.C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press.
34. Harvey, A.C. & Chung, C.H. (2000). Estimating the underlying change in unemployment in the UK. J. Roy. Stat. Soc. Ser. A, 163, 303-339.
35. Koopman, S.J. (1993). Disturbance smoother for state space models. Biometrika, 80, 117-126.
36. Koopman, S.J. & Durbin, J. (2000). Fast filtering and smoothing for multivariate state space models. J. Time Series Anal., 21, 281-296.
37. Mariano, R.S. & Murasawa, Y. (2003). A new coincident index of business cycles based on monthly and quarterly series. J. Appl. Econ., 18, 427-443.
38. Magnus, J.R. & Neudecker, H. (2007). Matrix Differential Calculus with Applications in Statistics and Econometrics, 2nd ed. New York: Wiley.
39. Mitchell, J., Smith, R.J., Weale, M.R., Wright, S. & Salazar, E.L. (2005). An indicator of monthly GDP and an early estimate of quarterly GDP growth. Econ. J., 115, 108-129.
40. Mönch, E. & Uhlig, H. (2004). Towards a monthly business cycle chronology for the euro area. CEPR Discussion Papers 4377.
41. Moauro, F. & Savio, G. (2005). Temporal disaggregation using multivariate structural time series models. Econ. J., 8, 214-234.
42. Peña, D. (1997). Combining information in statistical modeling. Am. Stat., 51, 326-332.
43. Proietti, T. (2006). Temporal disaggregation by state space methods: dynamic regression methods revisited. Econ. J., 9, 357-372.
44. Proietti, T. (2006). On the estimation of nonlinearly aggregated mixed models. J. Comput. Graph. Stat., 15, 1-21.
45. Proietti, T. & Moauro, F. (2006). Dynamic factor analysis with nonlinear temporal aggregation constraints. J. Roy. Stat. Soc. Ser. C, 55, 281-300.
46. Proietti, T. & Musso, A. (2011). Growth accounting for the euro area: a structural approach. Empirical Econ., DOI.
47. Sargent, T.J. & Sims, A.C. (1977). Business cycle modeling without pretending to have too much a priori economic theory. In New Methods in Business Research, Ed. A.C. Sims Minneapolis: Federal Reserve Bank of Minneapolis.
48. Shumway, R.H. & Stoffer, D.S. (1982). An approach to time series smoothing and forecasting using the EM algorithm. J. Time Ser. Anal., 3, 253-264.
49. Stock, J.H. & Watson, M.W. (1991). A probability model of the coincident economic indicators. In Leading Economic Indicators, Eds. K. Lahiri & G.H. Moore, New York: Cambridge University Press.
50. Stock, J.H. & Watson, M.W. (2002a). Macroeconomic forecasting using diffusion indexes. J. Bus. Econom. Statist., 20, 147-162.
51. Stock, J.H. & Watson, M.W. (2002b). Forecasting using principal components from a large number of predictors. J. Amer. Statist. Assoc., 97, 1167-1179.
52. Stock, J.H. & Watson, M.W. (2006). Forecasting with many predictors. In Handbook of Economic Forecasting, Eds. G. Elliott, C.W.J. Granger & A. Timmermann, Vol. 1. Amsterdam: Elsevier.
53. Watson, M.W. & Engle, R.F. (1983). Alternative algorithms for the estimation of dynamic, mimic and varying coefficient regression models. J. Econ., 23, 385-400.