Journals / European Journal of Pure and Applied Mathematics (elektronik) / 2010 / Cilt: 3 - Sayı: 3

Diffusion index models and index proxies: recent results and new directions

Pages
478–501
Publication Date
2022-11-05
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

We discuss, for example, the construction of factors used in prediction models implemented using diffusion index methodology and approaches that are useful for assessing whether there are observable variables that adequately “proxy” for estimated factors. 2000 Mathematics Subject Classifications: 62M10,62-07

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Diffusion index models have received considerable attention from both theoreticians and empirical econometricians in recent years.
📊
Key Findings & Data:In this paper we review some recent results in the study of diffusion index models, focusing primarily on advances due to [4, 5] and [1].
💡
Conclusion & Impact:We discuss, for example, the construction of factors used in prediction models implemented using diffusion index methodology and approaches that are useful for assessing whether there are observable variables that adequately “proxy” for estimated factors. 2000 Mathematics Subject Classifications: 62M10,62-07
🔬Design:Review & Systematic Survey
📍Scope:2000 Verileri
⚙️Variables:Domain Parameters

Abstract

Diffusion index models have received considerable attention from both theoreticians and empirical econometricians in recent years. One reason for this is that datasets with many variables are increasingly becoming available and being utilized for economic modelling, and another is that common factors are often assumed to underlie the co-movements of a set of macroeconomic variables. In this paper we review some recent results in the study of diffusion index models, focusing primarily on advances due to [4, 5] and [1]. We discuss, for example, the construction of factors used in prediction models implemented using diffusion index methodology and approaches that are useful for assessing whether there are observable variables that adequately “proxy” for estimated factors. 2000 Mathematics Subject Classifications: 62M10,62-07

🔖

Cite This Article

SWANSON, N. R., ARMAH, N. A. (2022). Diffusion index models and index proxies: recent results and new directions. European Journal of Pure and Applied Mathematics (elektronik), Cilt: 3 - Sayı: 3, 478–501. https://ajindex.com/articles/650430

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (18)

  1. N Armah and N Swanson. Seeing inside the black box: Using diffusion index methodology to construct factor proxies in largescale macroeconomic time series environments. Economic Reviews, Forthcoming, 2010
  2. J Bai. Inferential theory for factor models of large dimensions. Econometrica, 71(1):135– 172, 2003
  3. J Bai and S Ng. Determining the number of factors in approximate factor models. Econometrica, 70:192–221, 2002
  4. J Bai and S Ng. Confidence intervals for diffusion index forecasts and inference for factor-augmented regressions. Econometrica, 74(4):1133–1150, 2006
  5. J Bai and S Ng. Evaluating latent and observed factors in macroeconomics and finance. Journal of Econometrics, 113:507–537, 2006
  6. J Bai and S Ng. Forecasting economic time series using targeted predictors. Working Paper, University of Michigan, Ann-Arbor, 2006
  7. J Bai and S Ng. Determining the number of primitive shocks in factor models. Journal of Business and Economic Statistics, 25:52–60, 2007
  8. J Boivin and S Ng. Understanding and comparing factor based macroeconomic forecasts. International Journal of Central Banking, 1(3):117–152, 2005
  9. F Diebold and R Mariano. Comparing predictive accuracy. Journal of Business and Economic Statistics, 13:253–263, 1995
  10. A Ding and J Hwang. Prediction intervals, factor analysis models, and high-dimensional empirical linear prediction. Journal of the American Statistical Association, 95:446–455, 1999
  11. M Forni, M Hallin, M Lippi, and L Reichlin. The generalized dynamic factor model: Identification and estimation. The Review of Economics and Statistics, 82(4):540–552, 2000
  12. M Forni and L Reichlin. Dynamic common factors in large cross-sections. Empirical Economics, 21:27–42, 1996
  13. M Forni and L Reichlin. Lets get real: A dynamic factor analytical approach to disaggregated business cycle. Review of Economic Studies, 65:453–474, 1998
  14. G Korajczyk and R Korajczyk. Performance measurement with the arbitrage pricing theory. Journal of Financial Economics, 15:373–394, 1986
  15. G Korajczyk and R Korajczyk. Risk and return in an equilibrium apt: Application of a new test methodology. Journal of Financial Economics, 21:255–289, 1988
  16. G Korajczyk and R Korajczyk. A test for the number of factors in an approximate factor model. Journal of Finance, 48(4), 1993
  17. J Stock and M Watson. Diffusion indexes. Working Paper 6702, National Bureau of Economic Research, 1998
  18. J Stock and M Watson. Forecasting u