Solutions Manual for Econometrics
β Scribed by Professor Badi H. Baltagi (auth.)
- Publisher
- Springer Berlin Heidelberg
- Year
- 1998
- Tongue
- English
- Leaves
- 328
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Table of Contents
Front Matter....Pages I-VIII
A Review of Some Basic Statistical Concepts....Pages 1-21
Simple Linear Regression....Pages 22-39
Multiple Regression Analysis....Pages 40-68
Violations of the Classical Assumptions....Pages 69-107
Distributed Lags and Dynamic Models....Pages 108-136
The General Linear Model: The Basics....Pages 137-162
Regression Diagnostics and Specification Tests....Pages 163-188
Generalized Least Squares....Pages 189-202
Seemingly Unrelated Regressions....Pages 203-226
Simultaneous Equations Model....Pages 227-263
Pooling Time-Series of Cross-Section Data....Pages 264-282
Limited Dependent Variables....Pages 283-297
Time-Series Analysis....Pages 298-321
β¦ Subjects
Econometrics; Economic Theory
π SIMILAR VOLUMES
<P>This Second Edition updates the <STRONG>Solutions Manual for Econometrics</STRONG> to match the Fourth Edition of the Econometrics textbook. It corrects typos in the previous edition and adds problems and solutions using latest software versions of Stata and EViews. Special features include empir
<span>This Fourth Edition updates the "Solutions Manual forΒ </span><span>Econometrics"</span><span>Β to match the Sixth Edition of the Econometrics textbook. It adds problems and solutions using latest software versions of Stata and EViews. Special features include empirical examples replicated using
<p>This Third Edition updates the "Solutions Manual for <i>Econometrics"</i> to match the Fifth Edition of the Econometrics textbook. It adds problems and solutions using latest software versions of Stata and EViews. Special features include empirical examples using EViews and Stata. The book offers
For a one-year graduate course in Econometrics. This text has two objectives. The first is to introduce students to applied econometrics, including basic techniques in regression analysis and some of the rich variety of models that are used when the linear model proves inadequate or inappropriate.