Automatic positive semi-definite HAC covariance matrix and GMM estimation

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: cemmap working paper ; No. CWP17/04

Classification and Topics

• Classification: Wirtschaft ; Estimation: General ; Multiple or Simultaneous Equation Models; Multiple Variables: General
• Subject: GMM , HAC Covariance Matrix Estimation , Overidentifying Moments ; Regression ; Schätztheorie

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Smith, Richard J.
• Event: Veröffentlichung
• (who): Centre for Microdata Methods and Practice (cemmap)
• (where): London
• (when): 2004

Further information

• DOI: doi:10.1920/wp.cem.2004.1704
• Handle: http://hdl.handle.net/10419/79265
• Last update: 10.03.2025, 11:42 AM CET