Arbeitspapier
Testing factors in CCE
One of the most popular estimators of interactive effects panel data models is the common correlated effects (CCE) approach, which uses the cross-sectional averages of the observables as proxies of the unobserved factors. The present paper proposes a simple test that is suitable for testing hypotheses about the factors in CCE and that is valid provided only that the number of cross-sectional units is large. The new test can be used to test if a subset of the averages is enough to proxy the factors, or if there are observable variables that capture the factors. The test can also be used sequentially to determine the smallest set of averages needed to proxy the factors.
- Sprache
-
Englisch
- Erschienen in
-
Series: Queen’s Economics Department Working Paper ; No. 1491
- Klassifikation
-
Wirtschaft
- Thema
-
Factor model selection
Interactive effects models
CCE estimation
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Brown, Nicholas
Westerlund, Joakim
- Ereignis
-
Veröffentlichung
- (wer)
-
Queen's University, Department of Economics
- (wo)
-
Kingston (Ontario)
- (wann)
-
2022
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Brown, Nicholas
- Westerlund, Joakim
- Queen's University, Department of Economics
Entstanden
- 2022
Ähnliche Objekte (12)
Bootstrap Improved Inference for Factor-Augmented Regressions with CCE
Uniform Theory for CCE under Heterogeneous Slopes and General Unknown Factors
Testing multiple non-nested factor demand systems
Testing for structural breaks in dynamic factor models
Testing for structural stability of factor augmented forecasting models
A testing procedure for determining the number of factors in approximate factor models with large datasets
Testing for convergence in stock markets : a non-linear factor approach
Testing for convergence in stock markets: a non-linear factor approach
A factor-model approach for correlation scenarios and correlation stress-testing
Testing
Testing
Testing
Bootstrap Improved Inference for Factor-Augmented Regressions with CCE
Uniform Theory for CCE under Heterogeneous Slopes and General Unknown Factors
Testing multiple non-nested factor demand systems
Testing for structural breaks in dynamic factor models
Testing for structural stability of factor augmented forecasting models
A testing procedure for determining the number of factors in approximate factor models with large datasets
Testing for convergence in stock markets : a non-linear factor approach
Testing for convergence in stock markets: a non-linear factor approach
A factor-model approach for correlation scenarios and correlation stress-testing
Testing
Testing
Testing
Bootstrap Improved Inference for Factor-Augmented Regressions with CCE
Uniform Theory for CCE under Heterogeneous Slopes and General Unknown Factors
Testing multiple non-nested factor demand systems
Testing for structural breaks in dynamic factor models
Testing for structural stability of factor augmented forecasting models
A testing procedure for determining the number of factors in approximate factor models with large datasets
Testing for convergence in stock markets : a non-linear factor approach
Testing for convergence in stock markets: a non-linear factor approach
A factor-model approach for correlation scenarios and correlation stress-testing
Testing
Testing