A simultaneous estimation and variable selection rule

Article Abstract:

A new data-based method of estimation and variable selection in linear statistical models is proposed. This method is based on a generalized maximum entropy formalism, and makes use of both sample and non-sample information in determining a basis for coefficient shrinkage and extraneous variable identification. In contrast to tradition, shrinkage and variable selection are achieved on a coordinate-by-coordinate basis, and the procedure works well for both ill- and well-posed statistical models. Analytical asymptotic results are presented and sampling experiments are used as a basis for determining finite sample behavior and comparing the sampling performance of the new estimation rule with traditional competitors. Solution algorithms for the non-linear inversion problem that results are simple to implement. [C] 2001 Elsevier Science S.A. All rights reserved. JEL classification: C13; C14; C5 Keywords: Shrinkage estimator; Maximum entropy; Extraneous variables; Squared error loss; Data weighted prior; Subset selection

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Financial econometrics; Past developments and future challenges

Article Abstract:

The field of financial econometrics has had a glamorous run during the life span of the Journal of Econometrics. This note provides a selective summary of the most important developments in the field over the past two decades, notably ARCH and GMM, along with a discussion of promising avenues for future research. [C] 2001 Elsevier Science S.A. All rights reserved. JEL classification: C1; G1 Keywords: ARCH; GMM; High-frequency data; Long memory; Continuous time modeling; Risk-neutral distributions

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