£103.83

Routledge Estimation of M-equation Linear Models Book

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Description

Explore advanced econometric techniques with this Routledge Library Edition focused on the estimation of M-equation linear models. Originally published in 1984, this text provides a comprehensive set of results regarding Constraint Item (CI) estimation procedures. It is designed for researchers and students who require a deep understanding of estimation under the assumption of accurate specification. The book covers essential scenarios, including cases where all explanatory variables are non-stochastic and instances involving identified simultaneous equations. It addresses both known and unknown error terms, making it a versatile resource for complex mathematical analysis. Readers will find detailed guidance on deriving criteria for choosing the Constraint Item, ensuring more precise modeling outcomes. Structured into two clear sections, the content moves from the best CI estimators in Part 1 to an examination of equation by equation estimation in Part 2. This logical progression helps users understand forecasting accuracy and practical application in economic modeling. This edition remains a significant resource for those studying business and economic history through a mathematical lens.

Key Features

Comprehensive analysis of Constraint Item estimation procedures under accurate specification assumptions.

Covers diverse modeling scenarios including non-stochastic explanatory variables and identified simultaneous equations.

Detailed guidance on managing both known and unknown error terms within linear models.

Provides specific methods for deriving criteria to select the most effective Constraint Item.

Part 1 focuses on identifying and utilizing the best CI estimators for your research.

Part 2 examines equation by equation estimation with a focus on improving forecasting accuracy.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
07 March 2018
Listed Since
28 September 2017

Barcode

No barcode data available

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