Get this at ₹ 8397Extra 5% OffAdd items worth ₹999 to the cart & get extra 5% off
Get this at ₹ 7955Extra 10% OffAdd items worth ₹1499 to the cart & get extra 10% off
Product Specifications
Book Summary
This volume is the result of an Advances in Econometrics conference held in
November of 2002 at Louisiana State University in recognition of Halbert White's
pioneering work published in Econometrica in 1980 and 1982 on robust
variance-covariance estimation and quasi-maximum likelihood estimation. It
contains 11 papers on a range of related topics including the estimation of
possibly misspecified error component and fixed effects panel models, estimation
and inference in possibly misspecified quantile regression models, quasi-maximum
likelihood estimation of linear regression models with bounded and symmetric
errors and quasi-maximum likelihood estimation of models with parameter
dependencies between the mean vector and error variance-covariance matrix. Other
topics include GMM, HAC, Heckit,
ISBN-13
9780762310753
Language
English
Binding
Hard Bound
Publisher
Emerald Group Publishing Limited
Publishing Date
2003-12-01
Total Pages
264
Maximum Likelihood Estimation Of Misspecified Models: Twenty Years Later, Volume 17 (Advances In Econometrics)
This volume is the result of an Advances in Econometrics conference held in
November of 2002 at Louisiana State University in recognition of Halbert White's
pioneering work published in Econometrica in 1980 and 1982 on robust
variance-covariance estimation and quasi-maximum likelihood estimation. It
contains 11 papers on a range of related topics including the estimation of
possibly misspecified error component and fixed effects panel models, estimation
and inference in possibly misspecified quantile regression models, quasi-maximum
likelihood estimation of linear regression models with bounded and symmetric
errors and quasi-maximum likelihood estimation of models with parameter
dependencies between the mean vector and error variance-covariance matrix. Other
topics include GMM, HAC, Heckit,