Testing for Structural Stability of Factor Augmented Forecasting Models
AbstractMild factor loading instability, particularly if sufficiently independent across the different constituent variables, does not affect the estimation of the number of factors, nor subsequent estimation of the factors themselves (see e.g. Stock and Watson (2009)). This result does not hold in the presence of large common breaks in the factor loadings, however. In this case, information criteria overestimate the number of breaks. Additionally, estimated factors are no longer consistent estimators of "true" factors. Hence, various recent research papers in the diffusion index literature focus on testing the constancy of factor loadings. One reason why this is a positive development is that in applied work, factor augmented forecasting models are used widely for prediction, and it is important to understand when such models are stable. Now, forecast failure of factor augmented models can be due to either factor loading instability, regression coefficient instability, or both. To address this issue, we develop a test for the joint hypothesis of structural stability of both factor loadings and factor augmented forecasting model regression coefficients. The proposed statistic is based on the difference between full sample and rolling sample estimators of the sample covariance of the factors and the variable to be forecasted. Failure to reject the null ensures the structural stability of the factor augmented forecasting model. If the null is instead rejected, one can proceed to disentangle the cause of the rejection as being due to either (or both) of the afore mentioned varieties of instability. Standard inference can be carried out, as the suggested statistic has a chi-squared limiting distribution. We also establish the first order validity of (block) bootstrap critical values. Finally, we provide an empirical illustration by testing for the structural stability of factor augmented forecasting models for 11 U.S. macroeconomic indicators.
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Bibliographic InfoPaper provided by Rutgers University, Department of Economics in its series Departmental Working Papers with number 201314.
Length: 20 pages
Date of creation: 16 Jul 2013
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diffusion index; factor loading stability; forecast failure; forecast stability; regression coefficient stability;
Find related papers by JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-07-20 (All new papers)
- NEP-ECM-2013-07-20 (Econometrics)
- NEP-FOR-2013-07-20 (Forecasting)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Raffaella Giacomini & Barbara Rossi, 2009.
"Detecting and Predicting Forecast Breakdowns,"
Review of Economic Studies,
Oxford University Press, vol. 76(2), pages 669-705.
- Giacomini, Raffaella & Rossi, Barbara, 2006. "Detecting and predicting forecast breakdowns," Working Paper Series 0638, European Central Bank.
- Raffella Giacomini & Barbara Rossi, 2005. "Detecting and Predicting Forecast Breakdowns," UCLA Economics Working Papers 845, UCLA Department of Economics.
- Rossi, Barbara & Giacomini, Raffaella, 2006. "Detecting and Predicting Forecast Breakdowns," Working Papers 06-01, Duke University, Department of Economics.
- Jushan Bai & Serena Ng, 2000.
"Determining the Number of Factors in Approximate Factor Models,"
Econometric Society World Congress 2000 Contributed Papers
1504, Econometric Society.
- Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
- Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Boston College Working Papers in Economics 440, Boston College Department of Economics.
- Michael P. Clements & David F. Hendry, 2002. "Modelling methodology and forecast failure," Econometrics Journal, Royal Economic Society, vol. 5(2), pages 319-344, 06.
- Breitung, Jörg & Eickmeier, Sandra, 2009.
"Testing for structural breaks in dynamic factor models,"
Discussion Paper Series 1: Economic Studies
2009,05, Deutsche Bundesbank, Research Centre.
- Breitung, Jörg & Eickmeier, Sandra, 2011. "Testing for structural breaks in dynamic factor models," Journal of Econometrics, Elsevier, vol. 163(1), pages 71-84, July.
- Jennifer Castle & David Hendry & Jurgen A. Doornik, 2008.
"Model Selection when there are Multiple Breaks,"
Economics Series Working Papers
407, University of Oxford, Department of Economics.
- Liang Chen & Juan José Dolado & Jesús Gonzalo, 2011.
"Detecting big structural breaks in large factor models,"
Economics Working Papers
we1141, Universidad Carlos III, Departamento de Economía.
- Chen, Liang & Dolado, Juan Jose & Gonzalo, Jesus, 2011. "Detecting big structural breaks in large factor models," MPRA Paper 31344, University Library of Munich, Germany.
- Jörg Breitung & Sandra Eickmeier, 2006.
"Dynamic factor models,"
AStA Advances in Statistical Analysis,
Springer, vol. 90(1), pages 27-42, March.
- repec:att:wimass:9710 is not listed on IDEAS
- Goncalves, Silvia & White, Halbert, 2000.
"Maximum Likelihood and the Bootstrap for Nonlinear Dynamic Models,"
University of California at San Diego, Economics Working Paper Series
qt1bj657ff, Department of Economics, UC San Diego.
- Goncalves, Silvia & White, Halbert, 2004. "Maximum likelihood and the bootstrap for nonlinear dynamic models," Journal of Econometrics, Elsevier, vol. 119(1), pages 199-219, March.
- Goncalves, Silvia & White, Halbert, 2002. "Maximum Likelihood and the Bootstrap for Nonlinear Dynamic Models," University of California at San Diego, Economics Working Paper Series qt8hx21540, Department of Economics, UC San Diego.
- Sílvia Gonçalves & Halbert White, 2002. "Maximum Likelihood and the Bootstrap for Nonlinear Dynamic Models," CIRANO Working Papers 2002s-41, CIRANO.
- Norman R. Swanson & Nii Ayi Armah, 2011.
"Seeing Inside the Black Box: Using Diffusion Index Methodology to Construct Factor Proxies in Largescale Macroeconomic Time Series Environments,"
Departmental Working Papers
201105, Rutgers University, Department of Economics.
- Nii Ayi Armah & Norman Swanson, 2010. "Seeing Inside the Black Box: Using Diffusion Index Methodology to Construct Factor Proxies in Large Scale Macroeconomic Time Series Environments," Econometric Reviews, Taylor and Francis Journals, vol. 29(5-6), pages 476-510.
- Nii Ayi Armah & Norman R. Swanson, 2008. "Seeing inside the black box: Using diffusion index methodology to construct factor proxies in large scale macroeconomic time series environments," Working Papers 08-25, Federal Reserve Bank of Philadelphia.
- Kenneth D. West & Michael W. McCracken, 1998.
"Regression-Based Tests of Predictive Ability,"
NBER Technical Working Papers
0226, National Bureau of Economic Research, Inc.
- Jennifer Castle & David Hendry, 2012. "Forecasting by factors, by variables, or both?," Economics Series Working Papers 600, University of Oxford, Department of Economics.
- Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
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