Arbeitspapier
Bootstrap Improved Inference for Factor-Augmented Regressions with CCE
The Common Correlated Effects (CCE) methodology is now well established for the analysis of factor-augmented panel models. Yet, it is often neglected that the pooled variant is biased unless the cross-section dimension (N) of the dataset dominates the time series length (T). This is problematic for inference with typical macroeconomic datasets where T often equal or larger than N. Given that an analytical correction is also generally infeasible, the issue remains without a solution. In response, we provide in this paper the theoretical foundation for the cross-section, or pairs bootstrap in large N and T panels with T/N finite. We show that the scheme replicates the distribution of the CCE estimators, under both constant and heterogeneous slopes, such that bias can be eliminated and asymptotically correct inference can ensue even when N does not dominate. Monte Carlo experiments illustrate that the asymptotic properties also translate well to finite samples.
- Sprache
-
Englisch
- Erschienen in
-
Series: Working Paper ; No. 2021:16
- Klassifikation
-
Wirtschaft
Hypothesis Testing: General
Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models
- Thema
-
Panel data
CCE
Bootstrap
Pairs
Factors
Bias Correction
- Ereignis
-
Geistige Schöpfung
- (wer)
-
De Vos, Ignace
Stauskas, Ovidijus
- Ereignis
-
Veröffentlichung
- (wer)
-
Lund University, School of Economics and Management, Department of Economics
- (wo)
-
Lund
- (wann)
-
2021
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- De Vos, Ignace
- Stauskas, Ovidijus
- Lund University, School of Economics and Management, Department of Economics
Entstanden
- 2021
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