Estimation of simultaneous Equation Models with Error Components Structure

Krishnakumar, Jayalakshmi.

ISBN 10: 3540500316 / ISBN 13: 9783540500315

Published by Berlin/Heidelberg/New York/London/Paris/Tokyo, Publishers: Springer Verlag, 1988., 1988

About this Item

Gr.-8°. X, 357 Pages. Publishers: Springer Verlag (1988). X, 357 Pages. Original Flexible Boards (titled). Ex-Library-Copy. Pages browned (age-related). Library-Button on the Spine. Library-Stamp [dropped out] on Title. No Markings in the Text! No Underlinings! No handwritten Owner-Notation! Cover only with small Signs of Usage. ('Lecture Notes in Economics and Mathematical Systems', Volume 312).

Seller Inventory # 21909BB

Bibliographic Details

• Title: Estimation of simultaneous Equation Models with Error Components Structure
• Publisher: Berlin/Heidelberg/New York/London/Paris/Tokyo, Publishers: Springer Verlag, 1988.
• Publication Date: 1988
• Language: English
• ISBN 10: 3540500316
• ISBN 13: 9783540500315
• Binding: Soft cover
• Seller catalogs:
  • Mathematik - Mathematics

About this title

Synopsis

Economists can rarely perform controlled experiments to generate data. Existing information in the form of real-life observations simply has to be utilized in the best possible way. Given this, it is advantageous to make use of the increasing availability and accessibility of combinations of time-series and cross-sectional data in the estimation of economic models. But such data call for a new methodology of estimation and hence for the development of new econometric models. This book proposes one such new model which introduces error components in a system of simultaneous equations to take into account the temporal and cross-sectional heterogeneity of panel data. After a substantial survey of panel data models, the newly proposed model is presented in detail and indirect estimations, full information and limited information estimations, and estimations with and without the assumption of normal distribution errors. These estimation methods are then applied using a computer to estimate a model of residential electricity demand using data on American households. The results are analysed both from an economic and from a statistical point of view.