Identifying relevant and irrelevant variables in sparse factor models

Journal of Applied Econometrics

Volumn 32, Issue 6, 2017, Pages 1123-1144

  Kaufmann, Sylvia ^a   Schumacher, Christian ^b  

^a Study Center Gerzensee   (Switzerland)

^b DEUTSCHE BUNDESBANK   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85018701290     PISSN: 08837252     EISSN: 10991255     Source Type: Journal    
DOI: 10.1002/jae.2566     Document Type: Article

References (42)

1. Aguilar, O., & West, M. (2000). Bayesian dynamic factor models and portfolio allocation. Journal of Business & Economic Statistics, 18, 338–357.
2. Altissimo, F., Cristadoro, R., Forni, M., Lippi, M., & Veronese, G. (2010). New Eurocoin: Tracking economic growth in real time. Review of Economics and Statistics, 92, 1024–1034.
3. Anderson, T., & Rubin, H. (1956). Statistical inference in factor analysis. In Neyman, J (Ed.), Proceedings of the third Berkeley symposium on mathematical statistics and probability: Contributions to econometrics, industrial research, and psychometry (Vol. 5, pp. 111–150). Berkeley, CA: University of California Press.
4. Bai, J., & Ng, S. (2013). Principal components estimation and identification of the factors. Journal of Econometrics, 176, 18–29.
5. Bai, J., & Wang, P. (2014). Identification theory for high dimensional static and dynamic factor models. Journal of Econometrics, 178, 794–804.
6. Bauwens, L., Lubrano, M., & Richard, J. F. (1999). Bayesian Inference in Dynamic Econometric Models. Oxford, UK: Oxford University Press.
7. Bernanke, B., Boivin, J., & Eliasz, P. (2005). Measuring the effects of monetary policy: A factor-augmented vector autoregressive (FAVAR) approach. Quarterly Journal of Economics, 120, 387–422.
8. Bhattacharya, A., & Dunson, D. (2011). Sparse Bayesian infinite factor models. Biometrika, 98, 291–306.
9. Boivin, J., & Ng, S. (2006). Are more data always better for factor analysis?. Journal of Econometrics, 132, 169–194.
10. Carter, C. K., & Kohn, R. (1994). On Gibbs sampling for state space models. Biometrika, 84, 541–553.
11. Carvalho, C. M. (2006). Structure and sparsity in high-dimensional multivariate analysis, (Doctoral dissertation, Institute of Statistics and Decision Sciences, Duke University, Durham, NC).
12. Carvalho, C. M., Chang, C., Lucas, J. E., Nevins, J. R., Wang, Q., & West, M. (2008). High-dimensional sparse factor modeling: Applications in gene expression genomics. Journal of the American Statistical Association, 103, 1438–1456.
13. Conti, G., Frühwirth-Schnatter, S., Heckman, J., & Piatek, R. (2014). Bayesian exploratory factor analysis. Journal of Econometrics, 183, 31–57.
14. Del Negro, M., & Otrok, C. (2008). Dynamic factor models with time-varying parameters: Measuring changes in international business cycles. (Staff Report No.326): New York, NY: Federal Reserve Bank of New York.
15. Forni, M., Giannone, D., Lippi, M., & Reichlin, L. (2009). Opening the black box: Structural factor models with large cross-sections. Econometric Theory, 25, 1319–1347.
16. Forni, M., Hallin, M., Lippi, M., & Zaffaroni, P. (2015). Dynamic factor models with infinite-dimensional factor space: One-sided representations. Journal of Econometrics, 185, 359–371.
17. Francis, N., Owyang, M. T., & Savascin, Ö. (2012). An endogenously clustered factor approach to international business cycles. (Working Paper 2012-014A): St.Louis, MO: Federal Reserve Bank of St.Louis.
18. Frühwirth-Schnatter, S. (1994). Data augmentation and dynamic linear models. Journal of Time Series Analysis, 15, 183–202.
19. Frühwirth-Schnatter, S. (2011). Dealing with label switching under model uncertainty. In Kerrie, CPR, Mengersen, L, & Titterington, DM (Eds.), Mixtures: Estimation and Applications (pp. 213–240). Hoboken, NJ: Wiley.
20. Frühwirth-Schnatter, S., & Lopes, H. F. (2010). Parsimonious Bayesian factor analysis when the number of factors is unknown. (Technical report): Chicago, IL: University of Chicago Booth School of Business.
21. George, E. I., & McCulloch, R. E. (1993). Variable selection via Gibbs sampling. Journal of the American Statistical Association, 88, 881–889.
22. George, E. I., & McCulloch, R. E. (1997). Approaches for Bayesian variable selection. Statistica Sinica, 7, 339–373.
23. Geweke, J. (1996). Variable selection and model comparison in regression. Bayesian Statistics, 5, 609–620.
24. Geweke, J., & Zhou, G. (1996). Measuring the pricing error of the arbitrage pricing theory. Review of Financial Studies, 9, 557–587.
25. Hanson, T., & McMillan, G. T. (2012). Scheffé-style simultaneous credible bands for regression surfaces with application to Ache honey gathering. Journal of Data Science, 10, 175–193.
26. Held, L. (2004). Simultaneous posterior probability statements from Monte Carlo output. Journal of Computational and Graphical Statistics, 13, 20–35.
27. Heston, A., Summers, R., & Aten, B. (2011). Penn World Table Version 7.0: Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania.
28. 1–norm and related methods (pp. 405–416). Amsterdam, Netherlands: North-Holland.
29. Kaufmann, S., & Schumacher, C. (2012). Finding relevant variables in sparse Bayesian factor models: Economic applications and simulation results. (Discussion Paper No.29/2012): Frankfurt am Main, Germany: Deutsche Bundesbank.
30. Kaufmann, S., & Schumacher, C. (2013). Bayesian estimation of sparse dynamic factor models with order-independent identification. (Working Paper 13.04): Gerzensee, Switzerland: Study Center Gerzensee.
31. Knowles, D., & Ghahramani, Z. (2011). Nonparametric Bayesian sparse factor models with application to gene expression modelling. Annals of Applied Statistics, 5(2B), 1534–1552.
32. Koopman, S. J., & Harvey, A. (2003). Computing observation weights for signal extraction and filtering. Journal of Economic Dynamics & Control, 27, 1317–1333.
33. Kose, M. A., Otrok, C., & Whiteman, C. H. (2003). International business cycles: World, region and country specific factors. American Economic Review, 93, 1216–1239.
34. Kose, M. A., Otrok, C., & Whiteman, C. H. (2010). Understanding the evolution of world business cycles. Journal of International Economics, 75, 110–130.
35. Lawley, J. N., & Maxwell, A. E. (1971). Factor Analysis as a Statistical Method. London, UK: Butterworths.
36. Litterman, R. B. (1986). Forecasting with Bayesian vector autoregressions: Five years of experience. Journal of Business & Economic Statistics, 4, 25–38.
37. Lopes, H. F., & West, M. (2004). Bayesian model assessment in factor analysis. Statistica Sinica, 14, 41–67.
38. Lucas, J., Carvalho, C., Wang, Q., Bild, A., Nevins, J., & West, M. (2006). Sparse statistical modelling in gene expression genomics. In Do, KA, Müller, P, & Vannucci, M (Eds.), Bayesian inference for gene expression and proteomics (pp. 155–176). Cambridge, UK: Cambridge University Press.
39. Mackowiak, B., Mönch, E., & Wiederholt, M. (2009). Sectoral price data and models of price setting. Journal of Monetary Economics, 56, S78–S99.
40. Nakamura, E., & Steinsson, J. (2008). Five facts about prices: A reevaluation of menu cost models. Quarterly Journal of Economics, 123, 1415–1464.
41. Stock, J., & Watson, M. (2002). Macroeconomic forecasting using diffusion indexes. Journal of Business & Economic Statistics, 20, 147–162.
42. West, M. (2003). Bayesian factor regression models in the “large p, small n” paradigm. Bayesian Statistics, 7, 723–732.