Artikel
TSLS and LIML estimators in panels with unobserved shocks
The properties of the two stage least squares (TSLS) and limited information maximum likelihood (LIML) estimators in panel data models where the observables are affected by common shocks, modelled through unobservable factors, are studied for the case where the time series dimension is fixed. We show that the key assumption in determining the consistency of the panel TSLS and LIML estimators, as the cross section dimension tends to infinity, is the lack of correlation between the factor loadings in the errors and in the exogenous variables-including the instruments-conditional on the common shocks. If this condition fails, both estimators have degenerate distributions. When the panel TSLS and LIML estimators are consistent, they have covariance-matrix mixed-normal distributions asymptotically. Tests on the coefficients can be constructed in the usual way and have standard distributions under the null hypothesis.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 6 ; Year: 2018 ; Issue: 2 ; Pages: 1-12 ; Basel: MDPI
- Klassifikation
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Wirtschaft
Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
Single Equation Models: Single Variables: Instrumental Variables (IV) Estimation
Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models
- Thema
-
two-stage least squares
limited information maximum likelihood
common shocks
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Forchini, Giovanni
Jiang, Bin
Peng, Bin
- Ereignis
-
Veröffentlichung
- (wer)
-
MDPI
- (wo)
-
Basel
- (wann)
-
2018
- DOI
-
doi:10.3390/econometrics6020019
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:41 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Forchini, Giovanni
- Jiang, Bin
- Peng, Bin
- MDPI
Entstanden
- 2018
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