Introduction to Econometrics

Maintaining G.S. Maddala’s brilliant expository style of cutting through the technical superstructure to reveal only essential details, while retaining the nerve centre of the subject matter, Professor Kajal Lahiri has brought forward this new edition of one of the most important textbooks in its field.
The new edition continues to provide a large number of worked examples, and some shorter data sets.  Further data sets and additional supplementary material to assist both the student and lecturer are available on the companion website www.wileyeurope.com/college/maddala
New features for the fourth edition:Chapters 5 and 6, on Heteroscedasticity and Autocorrelation, now reflect the latest professional practice in dealing with these common variations of the basic regression model.Chapter 10 includes extensive discussion on diagnostic checking in linear models, various nested and non–nested model selection procedures, specification testing, data transformations, and tests for non–normality.The first three chapters of Part III cover an introduction to time–series analysis, including the Box–Jenkins approach, forecasting and seasonality, models of expectations and distributed lag models, and vector auto–regressions, unit roots, and cointegration.Chapters 15 and 16 cover, respectively, the latest developments in panel data analysis and various re–sampling methods for use in small sample inference.


Spis treści:
Part I: Introduction and the Linear Regression Model
Chapter 1 What is Econometrics?
Chapter 2 Statistical Background and Matrix Algebra
Chapter 3 Simple Regression
Chapter 4 Multiple Regression
Part II: Violation of the Assumptions of the Basic Model
Chapter 5 Heteroskedasticity
Chapter 6 Autocorrelation
Chapter 7 Multicollinearity
Chapter 8 Dummy Variables and Truncate d Variables
Chapter 9 Simultaneous Equations Models
Chapter 10 Diagnostic Checking, Model Selection, and Specification Testing
Chapter 11 Errors in Variables
Part III: Special Topics
Chapter 12 Introduction to Time–Series Analysis
Chapter 13 Models of Expectations and Distributed Lags
Chapter 14 Vector Autoregressions, Unit Roots, and Cointegration
Chapter 15 Panel Data Analysis
Chapter 16 Small–Sample Inference: Resampling Methods
References
Index

Nota biograficzna:
G.S.Maddala was one of the leading figures in field of econometrics for more than 30 years until he passed away in 1999. At the time of his death, he held the University Eminent Scholar Professorship in the Department of Economics at Ohio State University. His previous affiliations include Stanford University, University of Rochester and University of Florida.
Kajal Lahiri is Distinguished Professor of Economics, and Health Policy, and Management and Behaviour at the State University of New York, Albany where he is also Director of the Econometric Research Institute. Professor Lahiri is an Honorary Fellow of the International Institute of Forecasters.


Okładka tylna:
Maintaining G.S. Maddala’s brilliant expository style of cutting through the technical superstructure to reveal only essential details, while retaining the nerve centre of the subject matter, Professor Kajal Lahiri has brought forward this new edition of one of the most important textbooks in its field.
The new edition continues to provide a large number of worked examples, and some shorter data sets.  Further data sets and additional supplementary material to assist both the student and lecturer are available on the companion website www.wileyeurope.com/college/maddala
New features for the fourth edition:Chapters 5 and 6, on Heteroscedasticity and Autoc orrelation, now reflect the latest professional practice in dealing with these common variations of the basic regression model.Chapter 10 includes extensive discussion on diagnostic checking in linear models, various nested and non–nested model selection procedures, specification testing, data transformations, and tests for non–normality.The first three chapters of Part III cover an introduction to time–series analysis, including the Box–Jenkins approach, forecasting and seasonality, models of expectations and distributed lag models, and vector auto–regressions, unit roots, and cointegration.Chapters 15 and 16 cover, respectively, the latest developments in panel data analysis and various re–sampling methods for use in small sample inference.