Handbook of EconometricsEdited by
- M.D. Intriligator, University of California, Los Angeles, CA, USA
- Z. Griliches, Harvard University, Cambridge, MA, USA
The Handbook is a definitive reference source and teaching aid for econometricians. It examines models, estimation theory, data analysis and field applications in econometrics. Comprehensive surveys, written by experts, discuss recent developments at a level suitable for professional use by economists, econometricians, statisticians, and in advanced graduate econometrics courses.
For more information on the Handbooks in Economics series, please see our home page on http://www.elsevier.nl/locate/hes
Handbooks in Economics
Hardbound, 804 Pages
Published: November 1983
As one would expect, given the well known and distinguished contributors, this is an extremely impressive volume that assembles several really excellent surveys on the current state of knowledge in different fields of econometrics.
...will almost certainly prove to be an invaluable reference book for professional econometricians.
Richard T. Baillie , International Journal of Forecasting
There is a wealth of information in these surveys and their very useful bibliographies.
Esfandiar Maasoumi , Mathematical Reviews
This first volume of the Handbook of Econometrics, which is mainly concerned with technical, mathematical, statistical, and computational issues in econometrics, contains some valuable contributions which will make a trip to the library well worthwhile.
Grayham E. Mizon , The Economic Journal
This is one of the more useful contributions, in the sense of summarizing most of the things the study of econometrics ought to know about these matters and yet will have difficulty finding out except from a variety of disparate sources.
James Davidson , Journal of The Royal Stastical Society
- Mathematical and Statistical Methods in Econometrics. Linear algebra and matrix methods in econometrics (H. Theil). Statistical theory and econometrics (A. Zellner). Econometric Models. Economic and econometric models (M.D. Intriligator). Identification (C. Hsiao). Model choice and specification analysis (E.E. Leamer). Estimation and Computation. Non-linear regression models (T. Amemiya). Specification and estimation of simultaneous equation models (J.A. Hausman). Exact small sample theory in the simultaneous equations model (P.C.B. Philipps). Bayesian analysis of simultaneous equation systems (J.H. Drèze, J.-F. Richard). Biased estimation (G.G. Judge, M.E. Bock). Estimation for dirty data and flawed models (W.S. Krasker, E. Kuh and R.E. Welsch). Compuational problems and methods (R.E. Quandt).