Selecting the Correct Number of Factors in Approximate Factor Models: The Large Panel Case With Group Bridge Estimators

Journal of Business and Economic Statistics

Volumn 32, Issue 3, 2014, Pages 359-374

  Caner, Mehmet ^a   Han, Xu ^b  

^a NORTH CAROLINA STATE UNIVERSITY   (United States)

^b CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Bridge estimation; Common factors; Selection consistency

Indexed keywords

EID: 84925934601     PISSN: 07350015     EISSN: 15372707     Source Type: Journal    
DOI: 10.1080/07350015.2014.880349     Document Type: Article

References (44)

1. Alessi, L., Barigozzi, M., and Capasso, M. (2010), “Improved Penalization for Determining the Number of Factors in Approximate Factor Models,” Statistics and Probability Letters, 80, 1806–1813.
2. Amengual, D., and Watson, M. (2007), “Consistent Estimation of the Number of Dynamic Factors in a Large N And T Panel,” Journal of Business and Economic Statistics, 25, 91–96.
3. Bai, J. (2003), “Inferential Theory for Factor Models of Large Dimensions,” Econometrica, 71, 135–173.
4. Bai, J., and Ng, S. (2002), “Determining the Number of Factors in Approximate Factor Models,” Econometrica, 70, 191–221.
5. Bai, J., and Ng, S. (2007), “Determining the Number of Primitive Shocks in Factor Models,” Journal of Business and Economic Statistics, 25, 52–60.
6. Bai, J., and Ng, S. (2008), “Forecasting Economic Time Series Using Targeted Predictors,” Journal of Econometrics, 146, 304–317.
7. Bernanke, B.S., Boivin, J., and Eliasz, P. (2005), “Measuring the Effects of Monetary Policy: A Factor-augmented Vector Autoregressive (FAVAR) Approach,” Quarterly Journal of Economics, 120, 387–422.
8. Boivin, J., and Giannoni, M. (2006), “DSGE Models in a Data-Rich Environment,” NBER Working Paper, No. 12772.
9. Breiman, L. (1996), “Heuristics of Instability and Stabilization in Model Selection,” The Annals of Statistics, 24, 2350–2383.
10. Breitung, J., and Eickmeier, S. (2011), “Testing For Structural Breaks in Dynamic Factor Models,” Journal of Econometrics, 163, 71–84.
11. Caner, M., and Knight, K. (2013), “An Alternative to Unit Root Tests: Bridge Estimators Differentiate Between Nonstationary Versus Stationary Models and Select Optimal Lag,” Journal of Statistical Planning and Inference, 143, 691–715.
12. Caner, M., and Zhang, H. (2014), “Adaptive Elastic Net GMM With Diverging Number of Moments,” Journal of Business and Economics Statistics, forthcoming.
13. Chamberlain, G., and Rothschild, M. (1983), “Arbitrage, Factor Structure, and Mean Variance Analysis on Large Asset Markets,” Econometrica, 51, 1281–1304.
14. Davidson, J. (1994), Stochastic Limit Theory: An Introduction for Econometricians, New York: Oxford University Press.
15. De Mol, C., Giannone, D., and Reichlin, L. (2008), “Forecasting Using a Large Number of Predictors: Is Bayesian Shrinkage a Valid Alternative to Principal Components,” Journal of Econometrics, 146, 318–328.
16. Donoho, D., and Johnstone, I. (1994), “Ideal Spatial Adaptation by Wavelet Shrinkage,” Biometrika, 81, 425–455.
17. Fan, J., and Li, R. (2001), “Variable Selection via Nonconcave Penalized Likelihood and Its Oracle Properties,” Journal of the American Statistical Association, 96, 1348–1360.
18. Forni, M., and Gambetti, L. (2010), “The Dynamic Effects of Monetary Policy: A Structural Factor Model Approach,” Journal of Monetary Economics, 57, 203–216.
19. Forni, M., Hallin, M., Lippi, M., and Reichlin, L. (2000), “The Generalized Dynamic Factor Model: Identification and Estimation,” Review of Economics Statistics, 82, 540–554.
20. Forni, M., and Lippi, M. (1997), Aggregation and the Microfoundations of Dynamic Macroeconomics, Oxford, U.K.: Oxford University Press.
21. Giannone, D., Reichlin, L., and Sala, L. (2004), “Monetary Policy in Real Time,” NBER Macroeconomics Annual, available at http://www.nber.org/chapters/c6670.pdf.
22. Gregory, A., and Head, A. (1999), “Common and Country-Specific Fluctuations in Productivity, Investment, and the Current Account,” Journal of Monetary Economics, 44, 423–452.
23. Han, X. (2012), “Determining the Number of Factors with Potentially Strong Cross-sectional Correlation in Idiosyncratic Shocks,” unpublished manuscript, North Carolina State University.
24. Hallin, M., and Liska, R. (2007), “The Generalized Dynamic Factor Model: Determining the Number of Factors,” Journal of the American Statistical Association, 102, 603–617.
25. Hirose, K., and Konishi, S. (2012), “Variable Selection via the Weighted Group LASSO for Factor Analysis Models,” The Canadian Journal of Statistics, 40, 345–361.
26. Huang, J., Ma, S., Xie, H., and Zhang, C. (2009), “A Group Bridge Approach for Variable Selection,” Biometrika, 96, 339–355.
27. Huang, J., Horowitz, J., and Ma, S. (2008), “Asymptotic Properties of Bridge Estimators in Sparse High-Dimensional Regression Models,” The Annals of Statistics, 36, 587–613.
28. Knight, K., and Fu, W. (2000), “Asymptotics for Lasso Type Estimators,” The Annals of Statistics, 28, 1356–1378.
29. Kapetanios, G. (2010), “A Testing Procedure for Determining the Number of Factors in Approximate Factor Models with Large Datasets,” Journal of Business and Economic Statistics, 28, 397–409.
30. Lewbel, A. (1991), “The Rank of Demand Systems: Theory and Nonparametric Estimation,” Econometrica, 59, 711–730.
31. Luo, R., Wang, H., and Tsai, C.L. (2009), “Contour Projected Dimension Reduction,” The Annals of Statistics, 37, 3743–3778.
32. Onatski, A. (2009), “Testing Hypotheses About the Number of Factors in Large Factor Models,” Econometrica, 77, 1447–1479.
33. Onatski, A. (2010), “Determining the Number of Factors From Empirical Distribution of Eigenvalues,” The Review of Economics and Statistics, 92, 1004–1016.
34. Sargent, T.J., and Sims, C.A. (1977), “Business Cycle Modelling Without Pretending to Have Too Much a-priori Economic Theory,” in New Methods in Business Cycle Research, eds. Sims et al., Minneapolis, MN: Federal Reserve Bank of Minneapolis.
35. Seung, A., and Horenstein, A. (2013), “Eigenvalue Ratio Test for the Number of Factors,” Econometrica, 81, 1203–1227.
36. Stock, J., and Watson, M. (1989), “New Indexes of Coincident and Leading Economic Indications,” in NBER Macroeconomics Annual 1989, eds. O. J. Blanchard and S. Fischer, Cambridge: MIT press.
37. Stock, J., and Watson, M. (2002), “Macroeconomic Forecasting Using Diffusion Indexes,” Journal of Business and Economic Statistics, 20, 147–162.
38. Stock, J., and Watson, M. (2005), “Implications of Dynamic Factor Models for VAR Analysis,” NBER Working Paper, 11647.
39. Stock, J., and Watson, M. (2009), “Forecasting in Dynamic Factor Models Subject to Structural Instability,” in The Methodology and Practice of Econometrics, A Festschrift in Honour of Professor David F. Hendry, eds. Jennifer Castle and Neil Shephard, Oxford: Oxford University Press.
40. Uhlig, H. (2009), “Macroeconomic Dynamics in the Euro Area. Discussion by Harald Uhlig,” in NBER Macroeconomics Annual 23, ed. D. Acemoglu, K. Rogoff, and M. Woodford.
41. Wang, H. (2012), “Factor Profiled Sure Independence Screening,” Biometrika, 99, 15–28.
42. Wang, H., Li, B., and Leng, C. (2009), “Shrinkage Tuning Parameter Selection With a Diverging Number of Parameters,” Journal of the Royal Statistical Society, 71, 671–683.
43. Yuan, M., and Lin, Y. (2006), “Model Selection and Estimation in Regression With Grouped Variables,” Journal of the Royal Statistical Society, 68, 49–67.
44. Zou, H., and Zhang, H. (2009), “On the Adaptive Elastic Net With a Diverging Number of Parameters,” The Annals of Statistics, 37, 1733–1751.