Question(s): What is the idea and intuition behind quasi maximum likelihood estimation (QMLE)? What makes the estimator work when the actual error distribution does not match the assumed error distribution?

The Wikipedia site for QMLE is fine (brief, intuitive, to the point), but I could use some more intuition and detail, perhaps also an illustration. Other references are most welcome. (I remember going over quite a few econometrics textbooks before and surprisingly QMLE was only covered in one or two of them, e.g. Wooldridge "Econometric Analysis of Cross Section and Panel Data" (2010), Chapter 13 Section 11, pp. 502-517.)

Question(s): What is the idea and intuition behind quasi maximum likelihood estimation (QMLE; also known as pseudo maximum likelihood estimation, PMLE)? What makes the estimator work when the actual error distribution does not match the assumed error distribution?

The Wikipedia site for QMLE is fine (brief, intuitive, to the point), but I could use some more intuition and detail, perhaps also an illustration. Other references are most welcome. (I remember going over quite a few econometrics textbooks looking for material on QMLE, and to my surprise, QMLE was only covered in one or two of them, e.g. Wooldridge "Econometric Analysis of Cross Section and Panel Data" (2010), Chapter 13 Section 11, pp. 502-517.)

Question(s): What is the idea and intuition behind quasi maximum likelihood estimation? What makes the estimator work when the actual error distribution does not match the assumed error distribution?

The Wikipedia site for QMLE is fine (brief, intuitive, to the point), but I could use some more intuition and detail, perhaps also an illustration. Other references are most welcome. (I remember going over quite a few econometrics textbooks before and surprisingly QMLE was only covered in one or two of them, e.g. Wooldridge "Econometric Analysis of Cross Section and Panel Data".)

Question(s): What is the idea and intuition behind quasi maximum likelihood estimation (QMLE)? What makes the estimator work when the actual error distribution does not match the assumed error distribution?

The Wikipedia site for QMLE is fine (brief, intuitive, to the point), but I could use some more intuition and detail, perhaps also an illustration. Other references are most welcome. (I remember going over quite a few econometrics textbooks before and surprisingly QMLE was only covered in one or two of them, e.g. Wooldridge "Econometric Analysis of Cross Section and Panel Data" (2010), Chapter 13 Section 11, pp. 502-517.)