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Diffusion Index Models and Index Proxies: Recent Results and New Directions

Journal Name:

  • European Journal of Pure and Applied Mathematics

Publication Year:

  • 2010

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Abstract (2. Language): 
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.
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  • English

REFERENCES

References: 

[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 using principal components from a large number of
predictors. Journal of American Statistical Association, 97:1167–1179, 2002.
[19] J Stock and M Watson. Macroeconomic forecasting using diffusion indexes. Journal of
Business and Economic Statistics, 20:147–161, 2002.
[20] J Stock and M Watson. An empirical comparison of methods for forecasting using many
predictors. Working Paper Princeton University, 2004.
[21] J Stock and M Watson. Forecasting with many predictors. Handbook of Forecasting: A
Forthcoming Book, 2004.
[22] J Stock and M Watson. Implications of Dynamic Factor Models for VAR Analysis. Working
Paper 11467 National Bureau of Economic Research, 2004.

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