Abstract
For situations with a large number of series,N, each withTobservations and each containing a certain amount of information for prediction of the variable of interest, we propose a new statistical modelling methodology that first estimates the common factors from a panel of data using principal component analysis and then employs the estimated factors in a standard quantile regression. A crucial step in the model-building process is the selection of a good model among many possible candidates. Taking into account the effect of estimated regressors, we develop an information-theoretic criterion. We also investigate the criterion when there is no estimated regressors. Results of Monte Carlo simulations demonstrate that the proposed criterion performs well in a wide range of situations.
| Original language | English |
|---|---|
| Pages (from-to) | 1-24 |
| Number of pages | 24 |
| Journal | Econometrics Journal |
| Volume | 14 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2011 Feb |
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Keywords
- Approximate factor models
- Generated regressors
- Information-theoretic approach
- Panel data
- Quantiles
ASJC Scopus subject areas
- Economics and Econometrics
Cite this
Quantile regression models with factor-augmented predictors and information criterion. / Ando, Tomohiro; Tsay, Ruey S.
In: Econometrics Journal, Vol. 14, No. 1, 02.2011, p. 1-24.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Quantile regression models with factor-augmented predictors and information criterion
AU - Ando, Tomohiro
AU - Tsay, Ruey S.
PY - 2011/2
Y1 - 2011/2
N2 - For situations with a large number of series,N, each withTobservations and each containing a certain amount of information for prediction of the variable of interest, we propose a new statistical modelling methodology that first estimates the common factors from a panel of data using principal component analysis and then employs the estimated factors in a standard quantile regression. A crucial step in the model-building process is the selection of a good model among many possible candidates. Taking into account the effect of estimated regressors, we develop an information-theoretic criterion. We also investigate the criterion when there is no estimated regressors. Results of Monte Carlo simulations demonstrate that the proposed criterion performs well in a wide range of situations.
AB - For situations with a large number of series,N, each withTobservations and each containing a certain amount of information for prediction of the variable of interest, we propose a new statistical modelling methodology that first estimates the common factors from a panel of data using principal component analysis and then employs the estimated factors in a standard quantile regression. A crucial step in the model-building process is the selection of a good model among many possible candidates. Taking into account the effect of estimated regressors, we develop an information-theoretic criterion. We also investigate the criterion when there is no estimated regressors. Results of Monte Carlo simulations demonstrate that the proposed criterion performs well in a wide range of situations.
KW - Approximate factor models
KW - Generated regressors
KW - Information-theoretic approach
KW - Panel data
KW - Quantiles
UR - http://www.scopus.com/inward/record.url?scp=79951740418&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79951740418&partnerID=8YFLogxK
U2 - 10.1111/j.1368-423X.2010.00320.x
DO - 10.1111/j.1368-423X.2010.00320.x
M3 - Article
AN - SCOPUS:79951740418
VL - 14
SP - 1
EP - 24
JO - Econometrics Journal
JF - Econometrics Journal
SN - 1368-4221
IS - 1
ER -