Arbeitspapier
Automatic positive semi-definite HAC covariance matrix and GMM estimation
This paper proposes a new class of HAC covariance matrix estimators. The standard HAC estimation method re-weights estimators of the autocovariances. Here we initially smooth the data observations themselves using kernel function based weights. The resultant HAC covariance matrix estimator is the normalised outer product of the smoothed random vectors and is therefore automatically positive semi-definite. A corresponding efficient GMM criterion may also be defined as a quadratic form in the smoothed moment indicators whose normalised minimand provides a test statistic for the over-identifying moment conditions.
- Sprache
-
Englisch
- Erschienen in
-
Series: cemmap working paper ; No. CWP17/04
- Klassifikation
-
Wirtschaft
Estimation: General
Multiple or Simultaneous Equation Models; Multiple Variables: General
- Thema
-
GMM , HAC Covariance Matrix Estimation , Overidentifying Moments
Regression
Schätztheorie
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Smith, Richard J.
- Ereignis
-
Veröffentlichung
- (wer)
-
Centre for Microdata Methods and Practice (cemmap)
- (wo)
-
London
- (wann)
-
2004
- DOI
-
doi:10.1920/wp.cem.2004.1704
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 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
- Smith, Richard J.
- Centre for Microdata Methods and Practice (cemmap)
Entstanden
- 2004
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