A GMM estimator asymptotically more efficient than OLS and WLS in the presence of heteroskedasticity of unknown form

ABSTRACT

We propose a generalized method of moments (GMM) estimator, where our specific moment conditions, where our specific moment conditions ensure that the GMM estimator is asymptotically at least as efficient as ordinary least squares (OLS) and whatever competing weighted least squares (WLS) we wish to consider. With a popular exponential model of heteroskedasticity, our new GMM estimator performs significantly better than OLS or WLS. In an empirical application to a financial wealth equation, we show that the efficiency gains can be nontrivial with real data.

KEYWORDS:

• Heteroskedasticity
• GMM
• WLS
• financial wealth equation

JEL CLASSIFICATION:

• C13
• G00

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This paper is supported by the National Natural Science Foundation of China, under Grant No.71601094.