Tests of Equal Forecasting Accuracy for Nested Models with Estimated CCE Factors*
(2022) In Journal of Business and Economic Statistics 40(4). p.1745-1758- Abstract
In this article, we propose new tests of equal predictive ability between nested models when factor-augmented regressions are used to forecast. In contrast to the previous literature, the unknown factors are not estimated by principal components but by the common correlated effects (CCE) approach, which employs cross-sectional averages of blocks of variables. This makes for easy interpretation of the estimated factors, and the resulting tests are easy to implement and they account for the block structure of the data. Assuming that the number of averages is larger than the true number of factors, we establish the limiting distributions of the new tests as the number of time periods and the number of variables within each block jointly go... (More)
In this article, we propose new tests of equal predictive ability between nested models when factor-augmented regressions are used to forecast. In contrast to the previous literature, the unknown factors are not estimated by principal components but by the common correlated effects (CCE) approach, which employs cross-sectional averages of blocks of variables. This makes for easy interpretation of the estimated factors, and the resulting tests are easy to implement and they account for the block structure of the data. Assuming that the number of averages is larger than the true number of factors, we establish the limiting distributions of the new tests as the number of time periods and the number of variables within each block jointly go to infinity. The main finding is that the limiting distributions do not depend on the number of factors but only on the number of averages, which is known. The important practical implication of this finding is that one does not need to estimate the number of factors consistently in order to apply our tests.
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- author
- Stauskas, Ovidijus LU and Westerlund, Joakim LU
- organization
- publishing date
- 2022
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Common correlated effects, Common factor model, Factor-augmented regression model, Forecasting
- in
- Journal of Business and Economic Statistics
- volume
- 40
- issue
- 4
- pages
- 1745 - 1758
- publisher
- American Statistical Association
- external identifiers
-
- scopus:85116395023
- ISSN
- 0735-0015
- DOI
- 10.1080/07350015.2021.1970576
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2021 The Author(s). Published with license by Taylor & Francis Group, LLC.
- id
- f1bf574c-63e0-48fb-bb89-df57ad8a6f75
- date added to LUP
- 2021-10-28 15:06:10
- date last changed
- 2022-10-31 14:56:21
@article{f1bf574c-63e0-48fb-bb89-df57ad8a6f75,
abstract = {{<p>In this article, we propose new tests of equal predictive ability between nested models when factor-augmented regressions are used to forecast. In contrast to the previous literature, the unknown factors are not estimated by principal components but by the common correlated effects (CCE) approach, which employs cross-sectional averages of blocks of variables. This makes for easy interpretation of the estimated factors, and the resulting tests are easy to implement and they account for the block structure of the data. Assuming that the number of averages is larger than the true number of factors, we establish the limiting distributions of the new tests as the number of time periods and the number of variables within each block jointly go to infinity. The main finding is that the limiting distributions do not depend on the number of factors but only on the number of averages, which is known. The important practical implication of this finding is that one does not need to estimate the number of factors consistently in order to apply our tests.</p>}},
author = {{Stauskas, Ovidijus and Westerlund, Joakim}},
issn = {{0735-0015}},
keywords = {{Common correlated effects; Common factor model; Factor-augmented regression model; Forecasting}},
language = {{eng}},
number = {{4}},
pages = {{1745--1758}},
publisher = {{American Statistical Association}},
series = {{Journal of Business and Economic Statistics}},
title = {{Tests of Equal Forecasting Accuracy for Nested Models with Estimated CCE Factors*}},
url = {{http://dx.doi.org/10.1080/07350015.2021.1970576}},
doi = {{10.1080/07350015.2021.1970576}},
volume = {{40}},
year = {{2022}},
}